Simulation constraints cannot help in explaining afterlife beliefs in general because belief in an afterlife is a precondition for running a simulation. Instead, an explanation may be found by examining more deeply our common-sense dualistic conception of the mind or soul.

Cognitive Science, Volume 46, Issue 4, April 2022.

This article examines the proposition that a major cause of the major financial accounting scandals that received much publicity around the world was unethical leadership in the companies and compares the role of unethical leaders in a variety of scenarios. Through the use of computer simulation models, it shows how a combination of CEO's narcissism, financial incentive, shareholders' expectations and subordinate silence as well as CEO's dishonesty can do much to explain some of the findings highlighted in recent high (...) profile financial accounting scandals. Furthermore, it shows that the nature and impact of ethical leadership depends greatly on the institutional setting and can be expected to vary greatly by country and culture. In certain circumstances ethical leadership can have either a negligible or even opposite effect to that expected. (shrink)

Motor imagery typically involves an experience as of moving a body part. Recent studies reveal close parallels between the constraints on motor imagery and those on actual motor performance. How are these parallels to be explained? We advance a simulative theory of motor imagery, modeled on the idea that we predict and explain the decisions of others by simulating their decision-making processes. By proposing that motor imagery is essentially off-line motor action, we explain the tendency of motor imagery to mimic (...) motor performance. We close by arguing that a simulative theory of motor imagery gives (modest) support to and illumination of the simulative theory of decision-prediction. (shrink)

I present arguments against both explicit and implicit versions of the simulation theory for intersubjective understanding. Logical, developmental, and phenomenological evidence counts against the concept of explicit simulation if this is to be understood as the pervasive or default way that we understand others. The concept of implicit (subpersonal) simulation, identified with neural resonance systems (mirror systems or shared representations), fails to be the kind of simulation required by simulation theory, because it fails to explain (...) how neuronal processes meet constraints that involve instrumentality and pretense. Implicit simulation theory also fails to explain how I can attribute a mental or emotion state that is different from my own to another person. I also provide a brief indication of an alternative interpretation of neural resonance systems. (shrink)

Nick Bostrom’s ‘Simulation Argument’ purports to show that, unless we are confident that advanced ‘posthuman’ civilizations are either extremely rare or extremely rarely interested in running simulations of their own ancestors, we should assign significant credence to the hypothesis that we are simulated. I argue that Bostrom does not succeed in grounding this constraint on credence. I first show that the Simulation Argument requires a curious form of selective scepticism, for it presupposes that we possess good evidence (...) for claims about the physical limits of computation and yet lack good evidence for claims about our own physical constitution. I then show that two ways of modifying the argument so as to remove the need for this presupposition fail to preserve the original conclusion. Finally, I argue that, while there are unusual circumstances in which Bostrom’s selective scepticism might be reasonable, we do not currently find ourselves in such circumstances. There is no good reason to uphold the selective scepticism the Simulation Argument presupposes. There is thus no good reason to believe its conclusion. (shrink)

I argue that general constraints on how humans think about humans produce universal features of the concept of mind. Some of these constraints determine how we imagine other people's thinking and action through our own. I formulate this in opposition to what I call the "theory theory". I believe this was the first use of this terminology, and this work was an early version of what has come to be called the simulation theory.

In this paper, we first review recent arguments about the direct perception of the intentions and emotions of others, emphasizing the role of embodied interaction. We then consider a possible objection to the direct perception hypothesis from social psychology, related to phenomena like ‘dehumanization’ and ‘implicit racial bias’, which manifest themselves on a basic bodily level. On the background of such data, one might object that social perception cannot be direct since it depends on and can in fact be interrupted (...) by a set of cultural beliefs. We argue, however, that far from threatening the idea of direct perception, these findings clearly contradict the idea of hardwired theory of mind modules. More generally, we suggest that in order to further the understanding of social cognition we must take seriously insights about in-group and out-group distinctions and related phenomena, all of which are currently neglected in the mainstream social cognition literature. (shrink)

It is argued that the traditional distinction between artificial intelligence and cognitive simulation amounts to little more than a difference in style of research - a different ordering in goal priorities and different methodological allegiances. Both enterprises are constrained by empirical considerations and both are directed at understanding classes of tasks that are defined by essentially psychological criteria. Because of the different ordering of priorities, however, they occasionally take somewhat different stands on such issues as the power/generality trade-off and (...) on the relevance of the sort of data collected in experimental psychology laboratories. (shrink)

Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats (...) both relational correspondences and object similarity as soft constraints that operate within a limited-capacity working memory; but not with a model (SME) that treats relational structure as pre-eminent. (shrink)

The majority of the extant languages have one of three dominant basic word orders: SVO, SOV or VSO. Various hypotheses have been proposed to explain this word order bias, including the existence of a universal grammar, the learnability imposed by cognitive constraints, the descent of modern languages from an ancestral protolanguage, and the constraints from functional principles. We run simulations using a multi-agent computational model to study this bias. Following a local order approach, the model simulates individual language processing mechanisms (...) in production and comprehension. The simulation results demonstrate that the semantic structures that a language encodes can constrain the global syntax, and that local syntax can help trigger bias towards the global order SOV/SVO. (shrink)

The majority of the extant languages have one of three dominant basic word orders: SVO, SOV or VSO. Various hypotheses have been proposed to explain this word order bias, including the existence of a universal grammar, the learnability imposed by cognitive constraints, the descent of modern languages from an ancestral protolanguage, and the constraints from functional principles. We run simulations using a multi-agent computational model to study this bias. Following a local order approach, the model simulates individual language processing mechanisms (...) in production and comprehension. The simulation results demonstrate that the semantic structures that a language encodes can constrain the global syntax, and that local syntax can help trigger bias towards the global order SOV/SVO. (shrink)

The national new area Xiong’an has been established to take over the noncapital functions of Beijing in China. In light of local resources and environmental constraints, it is important to clarify the mode of industrial transfer for Xiong’an new area to achieve the developmental goals. This study simulates and analyzes the speed of industrial transfer and the capacities of XNA in light of resource and environmental constraints. The results show that, to just realize modernization in 2035, the transfer rate of (...) the secondary industry and tertiary industry is annually 3.7 billion yuan and 6.4 billion yuan, respectively. But at this speed, the atmospheric environment will be overloaded by 2028. The minimum and maximum transfer rate to realize modernization in 2035 without overloading resources and environment are also specified as well as the total size of economy and population. The results indicate that land for the construction of XNA is rich, but local water is not sufficient to support industrial growth. The atmospheric environment of XNA is also an important limiting factor. It is thus necessary to reduce air pollution and increase the population and industrial scale of the area by increasing the transfer rate of the tertiary industry. (shrink)

This paper argues that, for a prospective philosophical analysis of models and simulations to be successful, it must accommodate an account of mathematically rigorous results. Such rigorous results are best thought of as genuinely model-specific contributions, which can neither be deduced from fundamental theory nor inferred from empirical data. Rigorous results often provide new indirect ways of assessing the success of computer simulations of individual models. This is most obvious in cases where rigorous results map different models on to one (...) another. Not only does this allow for the transfer of warrant across different models, it also puts constraints on the extent to which performance in specific empirical contexts may be regarded as the main touchstone of success in scientific modelling. Rigorous results and relations can thus come to be seen as giving cohesion and stability to actual practices of scientific modelling. (shrink)

Bayesian epistemologyEpistemology offers a powerful framework for characterizing scientific inference. Its basic idea is that rational belief comes in degrees that can be measured in terms of probabilities. The axioms of the probability calculus and a rule for updatingUpdating emerge as constraints on the formation of rational belief. Bayesian epistemologyEpistemology has led to useful explications of notions such asConfirmation confirmation. It thus is natural to ask whether Bayesian epistemologyEpistemology offers a useful framework for thinking about the inferences implicit in the (...) validation of computer simulations. The aim of this chapter is to answer this question. Bayesian epistemologyEpistemology is briefly summarized and then applied to validation. UpdatingUpdating is shown to form a viable method for data-driven validation. Bayesians can also express how a simulation obtains prior credibilityCredibility because the underlying conceptual modelConceptual model is credible. But the impact of this prior credibilityCredibility is indirect since simulations at best provide partial and approximate solutions to theConceptual model conceptual model. Fortunately, this gap between the simulations and the conceptual model can be addressed using what we call Bayesian verification. The final part of the chapter systematically evaluates the use of Bayesian epistemologyEpistemology in validation, e.g., by comparing it to a falsificationist approach. It is argued that Bayesian epistemologyEpistemology goes beyond mere calibrationCalibration and that it can provide the foundations for a sound evaluationEvaluation of computer simulations. (shrink)

Guidelines orient best practices in medicine, yet, in health care, many real world constraints limit their optimal realization. Since guideline implementation problems are not systematically anticipated, they will be discovered only post facto, in a learning curve period, while the already implemented guideline is tweaked, debugged and adapted. This learning process comes with costs to human health and quality of life. Despite such predictable hazard, the study and modeling of medical guideline implementation is still seldom pursued. In this article we (...) argue that to systematically identify, predict and prevent medical guideline implementation errors is both an epistemic responsibility and an ethical imperative in health care, in order to properly provide beneficence, minimize or avoid harm, show respect for persons, and administer justice. Furthermore, we suggest that implementation knowledge is best achieved technically by providing simulation modeling studies to anticipate the realization of medical guidelines, in multiple contexts, with system and scenario analysis, in its alignment with the emerging field of implementation science and in recognition of learning health systems. It follows from both claims that it is an ethical imperative and an epistemic responsibility to simulate medical guidelines in context to minimize harm in health care, before guideline implementation. (shrink)

This paper describes a theory that explains both the creativity and the efficiency of people's conceptual combination. In the constraint theory, conceptual combination is controlled by three constraints of diagnosticity, plausibility, and informativeness. The constraints derive from the pragmatics of communication as applied to compound phrases. The creativity of combination arises because the constraints can be satisfied in many different ways. The constraint theory yields an algorithmic model of the efficiency of combination. The C3 model admits the full (...) creativity of combination and yet efficiently settles on the best interpretation for a given phrase. The constraint theory explains many empirical regularities in conceptual combination, and makes various empirically verified predictions. In computer simulations of compound phrase interpretation, the C3 model has produced results in general agreement with people's responses to the same phrases. (shrink)

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other (...) tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations. (shrink)

This paper defends the naïve thesis that the method of experiment has per se an epistemic superiority over the method of computer simulation, a view that has been rejected by some philosophers writing about simulation, and whose grounds have been hard to pin down by its defenders. I further argue that this superiority does not come from the experiment’s object being materially similar to the target in the world that the investigator is trying to learn about, as both (...) sides of dispute over the epistemic superiority thesis have assumed. The superiority depends on features of the question and on a property of natural kinds that has been mistaken for material similarity. Seeing this requires holding other things equal in the comparison of the two methods, thereby exposing that, under the conditions that will be specified, the simulation is necessarily epistemically one step behind the corresponding experiment. Practical constraints like feasibility and morality mean that scientists do not often face an other-things- equal comparison when they choose between experiment and simulation. Nevertheless, I argue, awareness of this superiority and of the general distinction between experiment and simulation is important for maintaining motivation to seek answers to new questions. (shrink)

This paper defends the naïve thesis that the method of experiment has per se an epistemic superiority over the method of computer simulation, a view that has been rejected by some philosophers writing about simulation, and whose grounds have been hard to pin down by its defenders. I further argue that this superiority does not come from the experiment’s object being materially similar to the target in the world that the investigator is trying to learn about, as both (...) sides of dispute over the epistemic superiority thesis have assumed. The superiority depends on features of the question and on a property of natural kinds that has been mistaken for material similarity. Seeing this requires holding other things equal in the comparison of the two methods, thereby exposing that, under the conditions that will be specified, the simulation is necessarily epistemically one step behind the corresponding experiment. Practical constraints like feasibility and morality mean that scientists do not often face an other-things- equal comparison when they choose between experiment and simulation. Nevertheless, I argue, awareness of this superiority and of the general distinction between experiment and simulation is important for maintaining motivation to seek answers to new questions. (shrink)

Continuous culture techniques were developed in the early twentieth century to replace cumbersome studies of cell growth in batch cultures. In contrast to batch cultures, they constituted an open concept, as cells are forced to proliferate by adding new medium while cell suspension is constantly removed. During the 1940s and 1950s new devices have been designed—called “automatic syringe mechanism,” “turbidostat,” “chemostat,” “bactogen,” and “microbial auxanometer”—which allowed increasingly accurate quantitative measurements of bacterial growth. With these devices cell growth came under the (...) external control of the experimenters and thus accessible for developing a mathematical theory of growth kinetics—developed mainly by Jacques Monod, Aron Novick and Leo Szilard in the early 1950s and still in use today. The paper explores the development of continuous culture devices and claims that these devices are simulators for standard cells following specific requirements, in particular involving mathematical constraints in the design and setting of the devices as well as experiments. These requirements have led to contemporary designs of continuous culture techniques realizing a specific event-based flow algorithm able to simulate directed evolution and produce artificial cells and microorganisms. This current development is seen as an alternative approach to today’s synthetic biology. (shrink)

A number of research projects have recently taken up the challenge of formulating large-scale models of brain mechanisms at unprecedented levels of detail. These research enterprises have raised lively debates in the press and in the scientific and philosophical literature, some of them revolving around the question whether the incorporation of so many details in a theoretical model and in a computer simulations of it is really needed for the model to be explanatory. Is there a “right” level of detail? (...) In this article I analyse the claim, made by two leading neuroscientists, according to which the content of the why-question addressed and the amount of computational resources available constrains the choice of the most appropriate level of detail in brain modelling. Based on the recent philosophical literature on scientific explanation, I distinguish between two kinds of details, called here mechanistic decomposition and property details, and argue that the nature of the why-question provides only partial constraints to the choice of the most appropriate level of detail under the two interpretations of the term considered here. (shrink)

The paper presents a new proposal how to explain pictorial representation. Starting point is the phenomenological idea that pictures, in the first place, make something visible (instead of being a sign of something). Making something visible is taken as an achievement of the faculty of imagination, and the faculty of imagination in turn is taken to be a variety of our faculty to simulate (referring here to the concept of simulation as it is used in contemporary cognitive psychology, according (...) to which the ability to simulate e.g. other minds is the ability to reenact somebody else’s cognitive processes). Taken together, these pieces come down to the claim that pictorial representation of some x is simulating the perception of x with pictorial means; pictures prompt and guide such a simulation. Crucial is the addition »with pictorial means«. Picture-based simulation has constraints, which differ from those of perception and imagination. Therefore, in picture-based simulation perceptual processes are not simply copied but re-modeled and open to modification. The primary means of such modification are provided by the properties of pictures as material objects, such as their having a frame which encloses a two-dimensional surface. Therefore, being a material object promotes and shapes pictorial representation, instead of hampering it. (shrink)

Thought experiments have a mysterious way of informing us about the world, apparently without examining it, yet with a great degree of certainty. It is tempting to try to explain this capacity by making use of the idea that in thought experiments, the mind somehow simulates the processes about which it reaches conclusions. Here, I test this idea. I argue that when they predict the outcomes of hypothetical physical situations, thought experiments cannot simulate physical processes. They use mental models, which (...) should not be confused with process-driven simulations. A convincing case can be made that thought experiments about hypothetical mental processes are mental simulations. Concerning moral thought experiments, I argue that construing them as simulations of mental processes favours certain moral theories over others. The scope of mental simulation in thought experiments is primarily limited by the constraint of relevant similarity on source and target processes: on one hand, this constraint disqualifies thought from simulating external natural processes; on the other hand, it is a source of epistemic bias in moral thought experiments. In view of these results, I conclude that thought experiments and mental simulations cannot be assimilated as means of acquiring knowledge. (shrink)

This essay traces the history of early molecular dynamics simulations, specifically exploring the development of SHAKE, a constraint-based technique devised in 1976 by Jean-Paul Ryckaert, Giovanni Ciccotti and the late Herman Berendsen at CECAM (Centre Européen de Calcul Atomique et Moléculaire). The work of the three scientists proved to be instrumental in giving impetus to the MD simulation of complex polymer systems and it currently underpins the work of thousands of researchers worldwide who are engaged in computational physics, (...) chemistry and biology. Despite its impact and its role in bringing different scientific fields together, accurate historical studies on the birth of SHAKE are virtually absent. By collecting and elaborating on the accounts of Ryckaert and Ciccotti, this essay aims to fill this gap, while also commenting on the conceptual and computational difficulties faced by its developers. (shrink)

The epistemology of computer simulations has become a mainstream topic in the philosophy of technology. Within this large area, significant differences hold between the various types of models and simulation technologies. Agent-based and multi-agent systems simulations introduce a specific constraint on the types of agents and systems modelled. We argue that such difference is crucial and that simulation for the artificial sciences requires the formulation of its own specific epistemological principles. We present a minimally committed epistemology which (...) relies on the methodological principles of the Philosophy of Information and requires weak assumptions on the usability of the simulation and the controllability of the model. We use these principles to provide a new definition of simulation for the context of interest. (shrink)

Optimal strategy, one of the main transit assignment models, can better demonstrate the flexibility for passengers using routes in a transit network. According to the basic optimal strategy model, passengers can board trains based on their frequency without any capacity limitation. In the metropolitan cities such as Beijing, Shanghai, and Hong Kong, morning commuters face huge transit problems. Especially for the metro system, there is heavy rush in metro stations. Owing to the limited train capacity, some passengers cannot board the (...) first coming train and need to wait for the next one. To better demonstrate the behavior of passengers pertaining to the limited train capacity, we consider capacity constraints for the basic optimal strategy model to represent the real situation. We have proposed a simulation-based algorithm to solve the model and apply it to the Beijing Subway to demonstrate the feasibility of the model. The application of the proposed approach has been demonstrated using the computational results for transit networks originating from practice. (shrink)

What does it mean to trust the results of a computer simulation? This paper argues that trust in simulations should be grounded in empirical evidence, good engineering practice, and established theoretical principles. Without these constraints, computer simulation risks becoming little more than speculation. We argue against two prominent positions in the epistemology of computer simulation and defend a conservative view that emphasizes the difference between the norms governing scientific investigation and those governing ordinary epistemic practices.

Emergency scheduling of public resources on the cloud computing platform network can effectively improve the network emergency rescue capability of the cloud computing platform. To schedule the network common resources, it is necessary to generate the initial population through the Hamming distance constraint and improve the objective function as the fitness function to complete the emergency scheduling of the network common resources. The traditional method, from the perspective of public resource fairness and priority mapping, uses incremental optimization algorithm to (...) realize emergency scheduling of public resources, neglecting the improvement process of the objective function, which leads to unsatisfactory scheduling effect. An emergency scheduling method of cloud computing platform network public resources based on genetic algorithm is proposed. With emergency public resource scheduling time cost and transportation cost minimizing target, initial population by Hamming distance constraints, emergency scheduling model, and the corresponding objective function improvement as the fitness function, the genetic algorithm to individual selection and crossover and mutation probability were optimized and complete the public emergency resources scheduling. Experimental results show that the proposed method can effectively improve the efficiency of emergency resource scheduling, and the reliability of emergency scheduling is better. (shrink)

Although we are beginning to understand the neurocognitive processes that underlie the emergence of dreaming, what accounts for the bizarre phenomenology of dreams remains debated. I address this question by comparing dreaming with waking mind wandering and challenging previous accounts that utilize bizarreness to mark a sharp divide between conscious experiences in waking and sleeping. Instead, I propose that bizarreness is a common, non-deficient feature of spontaneous offline simulations occurring across the sleep-wake cycle and can be tied to the specific (...) characteristics of spontaneous thought as being dynamic, unconstrained, (hyper)associative and highly variable in content. Rather than misrepresenting waking reality, bizarreness can be employed to investigate the very building blocks of spontaneous cognition. The absence of bizarreness in thought processes is imposed by automatic and deliberate cognitive constraints. By contrast, thought and memory processes operating on their own without such constraints are inherently marked by different degrees and types of bizarreness. (shrink)

A general model of problem‐solving processes based on misconception elimination is presented to simulate both impasses and solving processes. The model operates on goal‐related rules and a set of constraint rules in the form of “if (state or goal), do not (Action)” for the explicit constraints in the instructions and the implicit constraints that come from misconceptions of legal moves. When impasses occur, a constraint elimination mechanism is applied. Because successive eliminations of implicit constraints enlarge the problem space (...) and have an effect on planning, the model integrates “plan‐based” and “constraint‐based” approaches to problem‐solving behavior.Simulating individual protocols of Tower of Hanoi situations shows that the model, which has a proper set of constraints, predicts a single move with no alternative on about 61% of the movements and that protocols are quite successfully simulated movement by movement. Finally, it is shown that many features of previous models are embedded in the constraint elimination model. (shrink)

An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when (...) the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness. (shrink)

The problem of adaptive finite-time fault-tolerant control and output constraints for a class of uncertain nonlinear half-vehicle active suspension systems are investigated in this work. Markovian variables are used to denote in terms of different random actuators failures. In adaptive backstepping design procedure, barrier Lyapunov functions are adopted to constrain vertical motion and pitch motion to suppress the vibrations. Unknown functions and coefficients are approximated by the neural network. Assisted by the stochastic practical finite-time theory and FTC theory, the proposed (...) controller can ensure systems achieve stability in a finite time. Meanwhile, displacement and pitch angle in systems would not violate their maximum values, which imply both ride comfort and safety have been enhanced. In addition, all the signals in the closed-loop systems can be guaranteed to be semiglobal finite-time stable in probability. The simulation results illustrate the validity of the established scheme. (shrink)

A central tenet of cognitive linguistics is that adults’ knowledge of language consists of a structured inventory of constructions, including various two-argument constructions such as the active, the passive and “fronting” constructions. But how do speakers choose which construction to use for a particular utterance, given constraints such as discourse/information structure and the semantic fit between verb and construction? The goal of the present study was to build a computational model of this phenomenon for two-argument constructions in Mandarin. First, we (...) conducted a grammaticality judgment study with 60 native speakers which demonstrated that, across 57 verbs, semantic affectedness – as determined by further 16 native speakers – predicted each verb’s relative acceptability in the bei-passive and ba-active constructions, but not the Notional Passive and SVO Active constructions. Second, in order to simulate acquisition of these competing constraints, we built a computational model that learns to map from corpus-derived input to an output representation corresponding to these four constructions. The model was able to predict judgments of the relative acceptability of the test verbs in the ba-active and bei-passive constructions obtained in Study 1, with model-human correlations in the region of r = 0.5 and r = 0.3, respectively. Surprisingly, these correlations increased when lexical verb identity was removed; perhaps because this information leads to over-fitting of the training set. These findings suggest the intriguing possibility that acquiring constructions involves forgetting as a mechanism for abstracting across certain fine-grained lexical details and idiosyncrasies. (shrink)

This paper investigates the vehicle platoon control problems with both velocity constraints and input saturation. Firstly, radial basis function neural networks are employed to approximate the unknown driving resistance of a vehicle’s dynamic model. Then, a bidirectional topology, where vehicles can only communicate with their direct preceding and following neighbors, is used to depict the relationship among the vehicles in the platoon. On this basis, a neural adaptive sliding-mode control algorithm with an anti-windup compensation technique is proposed to maintain the (...) vehicle platoon with desired distance. Moreover, the string stability and the strong string stability of the whole vehicle platoon are proven through the stability theorem. Finally, numerical simulations verify the feasibility and effectiveness of the proposed control method. (shrink)

With the advent of powerful parallel computers, efforts have commenced to simulate complete mammalian brains. However, so far none of these efforts has produced outcomes close to explaining even the behavioral complexities of animals. In this article, we suggest four challenges that ground this shortcoming. First, we discuss the connection between hypothesis testing and simulations. Typically, efforts to simulate complete mammalian brains lack a clear hypothesis. Second, we treat complications related to a lack of parameter constraints for large-scale simulations. To (...) demonstrate the severity of this issue, we review work on two small-scale neural systems, the crustacean stomatogastric ganglion and the Caenorhabditis elegans nervous system. Both of these small nervous systems are very thoroughly, but not completely understood, mainly due to issues with variable and plastic parameters. Third, we discuss the hierarchical structure of neural systems as a principled obstacle to whole-brain simulations. Different organizational levels imply qualitative differences not only in structure, but in choice and appropriateness of investigative technique and perspective. The challenge of reconciling different levels also undergirds the challenge of simulating and hypothesis testing, as modeling a system is not the same thing as simulating it. Fourth, we point out that animal brains are information processing systems tailored very specifically for the ecological niches the respective animals live in. (shrink)

This paper presents two formation tracking control strategies for a combined set of single and double integrator agents with an arbitrary undirected communication topology. The first approach is based on the design of distance-based potential functions with interagent collision avoidance using local information about the distance and orientation between agents and the desired trajectory. The second approach adds signed area constraints to the desired formation specification and a control strategy that uses distance as well as area terms is designed to (...) achieve tracking convergence. Numerical simulations show the performance from both control laws. (shrink)

In this study, an accurate convergence time of the supertwisting algorithm is proposed to build up a framework for nonaffine nonlinear systems’ finite-time control. The convergence time of the STA is provided by calculating the solution of a differential equation instead of constructing Lyapunov function. Therefore, precise convergence time is presented instead of estimation of the upper bound of the algorithm’s reaching time. Regardless of affine or nonaffine nonlinear systems, supertwisting control provides a general solution based on virtual control law (...) skill ensuring the output of the systems converges to the origin point at exact time. Benchmark tests are simulated to demonstrate the effectiveness and efficiency of the algorithm. (shrink)

In this paper, we mainly investigate the coordinated tracking control issues of multiple Euler–Lagrange systems considering constant communication delays and output constraints. Firstly, we devise a distributed observer to ensure that every agent can get the information of the virtual leader. In order to handle uncertain problems, the neural network technique is adopted to estimate the unknown dynamics. Then, we utilize an asymmetric barrier Lyapunov function in the control design to guarantee the output errors satisfy the time-varying output constraints. Two (...) distributed adaptive coordinated control schemes are proposed to guarantee that the followers can track the leader accurately. The first scheme makes the tracking errors between followers and leader be uniformly ultimately bounded, and the second scheme further improves the tracking accuracy. Finally, we utilize a group of manipulator networks simulation experiments to verify the validity of the proposed distributed control laws. (shrink)

The consensus problem is presented for the switched multiagent system, where the MAS is switched between continuous- and discrete-time systems with relative state constraints. With some standard assumptions, we obtain the fact that the switched MAS with relative state constraints can achieve consensus under both fixed undirected graphs and switching undirected graphs. Furthermore, based on the absolute average value of initial states, we propose sufficient conditions for consensus of the switched MAS. The challenge of this study is that relative state (...) constraints are considered, which will make the consensus problem much more complex. One of the main contributions is that, for the switched MAS with relative state constraints, we explore the solvability of the consensus problem. Finally, we present two simulation examples to show the effectiveness of the results. (shrink)

The stabilization problem of a macroeconomic dynamical system is considered in this paper. The main features of this system are that the system uncertainties may be unknown functions of state and time but with known bounds. Furthermore, the control inputs are subject to constraints, which is a salient feature in an economic control problem. To ensure that the controls are within the specified boundaries, in our control design procedure, a creative diffeomorphism, which converts bounded controls into unbounded corresponding signals by (...) choosing an appropriate transformation function, is proposed. For the uncertain system, a deterministic robust control is designed to render the practical stability: uniform boundedness and uniform ultimate boundedness. The range of the input bounds is related to the uncertainties and can be designed according to the actual situation. Numerical simulations are performed to verify the effectiveness of the stabilization policy. (shrink)

The problem of finite-time tracking control is discussed for a class of uncertain nonstrict-feedback time-varying state delay nonlinear systems with full-state constraints and unmodeled dynamics. Different from traditional finite-control methods, a C 1 smooth finite-time adaptive control framework is introduced by employing a smooth switch between the fractional and cubic form state feedback, so that the desired fast finite-time control performance can be guaranteed. By constructing appropriate Lyapunov-Krasovskii functionals, the uncertain terms produced by time-varying state delays are compensated for and (...) unmodeled dynamics is coped with by introducing a dynamical signal. In order to avoid the inherent problem of “complexity of explosion” in the backstepping-design process, the DSC technology with a novel nonlinear filter is introduced to simplify the structure of the controller. Furthermore, the results show that all the internal error signals are driven to converge into small regions in a finite time, and the full-state constraints are not violated. Simulation results verify the effectiveness of the proposed method. (shrink)

This article investigates an adaptive neural network control algorithm for marine surface vessels with time-varying output constraints and unknown external disturbances. The nonlinear state-dependent transformation is introduced to eliminate the feasibility conditions of virtual controller. Moreover, the barrier Lyapunov function is used to achieve time-varying output constraints. As an important approximation tool, the NN is employed to approximate uncertain and continuous functions. Subsequently, the disturbance observer is structured to observe time-varying constraints and unknown external disturbances. The novel strategy can guarantee (...) that all signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the simulation results verify the benefit of the proposed method. (shrink)

This paper proposes an adaptive neural network control approach for a direct-current system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with (...) the existing works on DC systems. As far as we know, this system is the first studied situations with time-varying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results. (shrink)

The purpose of this study is to explore the impact of constraining class-specific residual variances to be equal by examining and comparing the parameter estimation of a free model and a constrained model under various conditions. A Monte Carlo simulation study was conducted under several conditions, including the number of predictors, class-specific intercepts, sample size, class-specific regression weights, and class proportion to evaluate the results for parameter estimation of the free model and the restricted model. The free model yielded (...) a more accurate estimation than the restricted model for most of the conditions, but the accuracy of the free model estimation was impacted by the number of predictors, sample size, the disparity in the magnitude of class-specific slopes and intercepts, and class proportion. When equality constraints were imposed in residual variance discrepant conditions, the parameter estimates showed substantial inaccuracy for slopes, intercepts, and residual variances, especially for those in Class 2. When the residual variances were equal between the classes, the restricted model showed better performance under some conditions. (shrink)