This paper investigates multi-coalition games (MCGs) involving players with high-order dynamics, where their decisions satisfy both hard local set constraints and nonlinear coupled inequality constraints. The hard set constraints require that players’ decisions always satisfy the prescribed local set constraints. The coexistence of constraints and high-order dynamics leads to significant challenges for algorithm design and analysis, as the system inertia caused by high-order dynamics prevents direct control of the decisions of players through their inputs. To control the constrained high-order players to perform MCG tasks autonomously and distributedly, based on state feedback, leader-following consensus, dynamic average consensus, projection, and adaptive control methods, a fully distributed algorithm is proposed, where the decisions of all players are ensured to satisfy the set constraints all the time. It allows players to adjust their own parameters by using their procurable feedback errors without requiring direct access to others’ decisions or gradients, eliminating the need for global information and parameters tuning. Furthermore, the convergence of this algorithm is rigorously proven by nonsmooth analysis and Lyapunov stability theory. Finally, the algorithm is applied to the electricity market games (EMGs) of smart grids to demonstrate its effectiveness.

This paper explores nudge schemes for a central regulator aimed at incentivizing players in constrained noncooperative games to reach desired equilibria. Unlike traditional intervention mechanisms where players update their actions by blindly following signals from the regulator, the nudge mechanism comprehensively integrates players’ rational judgment by incorporating trust variables into players’ models. This implies that players update their actions by evaluating the signals from the regulator and their own expectations to the incentive mechanisms. If the regulator’s signals significantly deviate from the players’ expectations, players decrease trust in the regulator and rely more on the their expectations when updating their actions. Conversely, if the signals align closely with their expectations, players tend to increase trust in the regulator and place greater emphasis on the regulator’s signals. It should be noted that each player does not have access to other players’ actions, which means that it updates its action in a distributed manner by only observing the actions of its neighbors through a directed balanced graph. Furthermore, static and dynamic nudges are designed based on different information available to the regulator, which are also extended to an online case with the desired equilibrum being time-varying. Finally, an application to the robot formation control is shown to validate the obtained results.

In this article, the cooperative fuzzy tracking control problem for a type of high-order nonlinear MASs under the Round-Robin (RR) communication protocol is solved. To effectively reduce the communication among agents, an RR communication protocol is first proposed, where each agent can communicate with only one neighbor at any given time. Based on the developed communication protocol, a hierarchical control approach is introduced, consisting of a distributed observer layer that utilizes information from both local and neighboring agents, and a decentralized control layer that operates solely based on local agent information. Specifically, a distributed observer is designed in the distributed observer layer to achieve the distributed observation objective by introducing a time-varying gain in the observer input. Furthermore, an improved distributed observer with the upper triangular form is constructed to produce a signal with high-order derivatives. In the decentralized control layer, fuzzy adaptive controllers are designed for all agents by using the backstepping technique. By using the Lyapunov stability theory, rigorous stability analysis shows that the cooperative fuzzy tracking control problem is solved under the developed hierarchical control approach. In the end, a simulation example is presented to demonstrate the validity of our proposed method.

The control properties of discrete-time switched linear systems (SLSs) with switching signals generated by logical dynamical systems are studied using the semitensor product (STP) approach. With the algebraic state-space representation (ASSR), the linear modes and the logical generators are aggregated as a system with hybrid states, leading to the criteria of reachability, controllability, observability, and reconstructibility of the SLSs. Algorithms for checking these properties are given. Then, two kinds of realization problems concerning whether the logical dynamical systems can generate the desired switching signals are investigated, and necessary and sufficient conditions for the realizability of the desired switching signals are given with respect to the cases of fixed operating time (FOT) switching and finite reference signal switching.

This paper addresses the abstract dynamic programming (DP) in the online scenario, where the abstract DP mapping is time-varying, instead of static. In this case, optimal costs and policies at different time instants are not the same in general, and the problem amounts to tracking time-varying optimal costs and policies, which is of interest to many practical problems. It is thus necessary to analyze the performance of classical value iteration (VI) and policy iteration (PI) algorithms in the online case. In doing so, this paper develops and provides the theoretical analysis for several online algorithms, including approximate online VI, online PI, approximate online PI, online optimistic PI, and approximate online optimistic PI algorithms. It is proved that the tracking error bounds for all algorithms critically depend upon the largest difference between any two consecutive abstract mappings. Meanwhile, examples are presented to illustrate the theoretical results.

Since autonomous driving (AD) is one of the most critical problems in the automobile industry, it has garnered the interest of many academics in recent years. An AD framework or a simulator is used to simulate autonomous vehicles (AVs), it can test modules that an AV needs. There are a large number of researchers studying specific AD simulation tasks, but few studies of systematic AD frameworks and simulators exist. This study distinguishes the functions of AD frameworks and simulators, and it makes a deep study of them, helping promote AV development for researchers, enterprises , and developers. This paper reviews open-source and commercial AD frameworks and simulators, introducing and comparing their features, functionalities, and so on. Additionally, we analyze current research on open-source AD frameworks and simulators according to different applied algorithms and hardware studies and discuss efforts to improve their simulation performance. In the last part, this paper proposes promising research fronts for AD frameworks and simulators for the near future from the viewpoints of hardware deficiencies, AD algorithms, scenario generation, vehicle-to-everything, safety and performance, and co-simulation.

In this article, the finite-time disturbance decoupling problem (FTDDP) of Boolean networks (BNs) and Boolean control networks (BCNs) is studied. A novel necessary and sufficient condition is first provided to determine whether a given BN is finite-time disturbance decoupled. Then, an algorithm is devised to find the shortest time that guarantees that a given BN is finite-time disturbance decoupled. In addition, using a constructed truth matrix, a necessary and sufficient condition for solving the disturbance decoupling problem of BCNs is proposed, and all possible time optimal state feedback controllers are designed. Finally, two examples are introduced to illustrate the feasibility of the obtained results.

It has been shown that self-triggered control has the ability to deal with cases with constrained resources by properly setting up the rules for updating the system control when necessary. In this article, self-triggered stabilization of the Boolean control networks (BCNs), including the deterministic BCNs, probabilistic BCNs, and Markovian switching BCNs, is first investigated via the semitensor product of matrices and the Lyapunov theory of the Boolean networks. The self-triggered mechanism with the aim to determine when the controller should be updated is provided by the decrease of the corresponding Lyapunov functions between two consecutive samplings. Rigorous theoretical analysis is presented to prove that the designed self-triggered control strategy for BCNs is well defined and can make the controlled BCNs be stabilized at the equilibrium point.

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving/driver assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, novel applications and standards in real world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support, computation algorithms and current development state. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about current regulations and market state of ADAS in China are reviewed in this paper, and other open issues related to ADAS are also introduced in particular.

We investigate the problem of finding optimal one-bit perturbation that maximizes the size of the basin of attractions (BOAs) of desired attractors and minimizes the size of the BOAs of undesired attractors for large-scale Boolean networks by cascading aggregation. First, via the aggregation, a necessary and sufficient condition is given to ensure the invariance of desired attractors after one-bit perturbation. Second, an algorithm is proposed to identify whether the one-bit perturbation will cause the emergence of new attractors or not. Next, the change of the size of BOAs after one-bit perturbation is provided in an algorithm. Finally, the efficiency of the proposed method is verified by a T-cell receptor network.

This paper investigates the adaptive output regulation problem for heterogeneous linear multiagent systems under randomly switching communication topologies. The switching mechanism is governed by a time-homogeneous Markov process, whose states correspond to all possible communication topologies among agents. A novel distributed adaptive cooperative controller is presented, where the dynamic compensators are utilized to estimate the exogenous signal for all the agents in mean square sense. The distributed control law is based upon the local information of agents, without using the global information of the communication topologies. Finally, illustrative examples are put forward to demonstrate the effectiveness of the given control scheme.

This paper investigates stability and guaranteed cost of time-triggered Boolean networks (BNs) based on the semitensor product of matrices. The time triggering is generated by mode-dependent average dwell-time switching signals in the BNs. With the help of the copositive Lyapunov function, a sufficient condition is derived to ensure that the considered network is globally stable under a designed average dwell-time switching signal. Subsequently, an infinite time cost function is further discussed and its bound is presented according to the obtained stability result. Numerical examples are finally given to show the feasibility of the theoretical results.