Article
Computer Science, Artificial Intelligence
Haris E. Psillakis, Konstantinos A. Oikonomidis
Summary: This paper proposes a new framework for solving the optimal consensus problem of agents with continuous dynamics. By introducing novel auxiliary continuous variables and using suitable smoothing functions, the continuity of the variables is guaranteed. It also designs adaptive fuzzy distributed controllers to approximate the unknown system nonlinearities and ensure optimal consensus.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Heng Zhao, Huanqing Wang, Ning Xu, Xudong Zhao, Sanaa Sharaf
Summary: This paper investigates the fuzzy approximation-based optimal consensus control problem for nonlinear multi-agent systems with unknown perturbations. The problem is reformulated as finding Nash-equilibrium solutions to zero-sum games by constructing local error dynamics. Control signals are sequentially designed using sliding mode control technology and the concept of hierarchical design to regulate the consensus error and minimize the local value function. An identifier critic architecture is developed to relax the requirement for complete system dynamics information, and the hierarchical sliding mode surface based critic network is applied to approximate optimal control inputs. A simulation example is presented to illustrate the validity of the proposed approach.
Article
Automation & Control Systems
Qi Zhang, Yang Yang, Xue Song, Xiaoran Xie, Naibo Zhu, Zhi Liu
Summary: The purpose of this article is to use adaptive dynamic programming to solve the optimal consensus problem for double-integrator multiagent systems with completely unknown dynamics. Flocking algorithms that neglect agents' inertial effect in double-integrator multiagent systems can cause unstable group behavior. Despite the existence of an inertia-independent protocol, its control law is determined by dynamics and inertia. However, accurately measuring inertia in reality is difficult, so adaptive dynamic programming is developed to enable the consensus of agents in the presence of entirely unknown dynamics.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2023)
Article
Computer Science, Artificial Intelligence
Wang Xianjia, Xue Linzhao, Zhipeng Yang, Yang Liu
Summary: This paper introduces an adaptive learning framework for agents with bounded rationality to learn from dynamic games and make decisions accordingly. The framework allows agents to form beliefs based on their recognitive abilities and past experiences, while considering the tradeoff between current payoff and belief updated. The Boundedly Rational Multiagent Learning (BRML) algorithm is proposed and its convergence is proved. Experimental results show that coordination and Pareto optimality can be achieved through the behavior of agents with high recognitive ability.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Kewen Li, Yongming Li
Summary: This article investigates the problem of adaptive neural network (NN) optimal consensus tracking control for nonlinear multiagent systems (MASs) with stochastic disturbances and actuator bias faults. By utilizing adaptive dynamic programming (ADP), an adaptive NN optimal consensus fault-tolerant control algorithm is presented, and its effectiveness is proven.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Alireza Mousavi, Amir H. D. Markazi, Esmaeel Khanmirza
Summary: This study presents a new framework that combines Adaptive Fuzzy Sliding-Mode Control (AF-SMC) with off-policy Reinforcement Learning (RL) algorithm to control nonlinear under-actuated agents. The framework uses graphical games to achieve near-optimal leader-follower consensus. The coefficients of the sliding variables are adaptively tuned policies to achieve an optimal compromise between tracking performance and control efforts.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Kewen Li, Yongming Li
Summary: This article investigates the problem of adaptive fuzzy optimal distributed consensus control for stochastic multiagent systems (MASs) with full-state constraints and nonaffine nonlinear faults. Fuzzy logic systems are employed to identify the unknown nonlinearities. To solve the problem of optimal state constraint control, a barrier Lyapunov function based optimal cost function is designed. By introducing Butterworth low-pass filter into control design, the deleterious effects raised by nonlinear fault can be compensated. By utilizing adaptive dynamic programming algorithm in critic-actor construction, a fuzzy adaptive distributed optimal consensus fault-tolerant control method is proposed, which can ensure that all signals of the controlled system are semiglobally uniformly ultimately bounded in probability, and outputs of the follower agents keep consensus with the output of leader. In addition, system states are all not exceeded their constrained bound. Finally, simulation results are provided to illustrate the feasibility of the developed control method and theorem.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Chengjie Huang, Shengli Xie, Zhi Liu, C. L. Philip Chen, Yun Zhang
Summary: This paper addresses a neuroadaptive inverse optimal consensus problem for uncertain nonlinear multiagent systems subject to actuator and sensor faults. The proposed control mechanism minimizes a loss function without solving the Hamilton-Jacobi-Bellman equation, simplifying the computational workload. Additionally, a compensation strategy for actuator and sensor faults is considered, and a novel fault-tolerant adaptive inverse optimal protocol incorporating the Lyapunov design is constructed. The effectiveness of the control design is demonstrated through a simulation example.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Xiaoyuan Luo, Yuliang Fu, Xiaolei Li, Shaobao Li
Summary: This paper focuses on the dynamic event-based resilient consensus control of multiple networked Euler-Lagrangian systems under Denial of Service (DoS) attacks. Nonlinear networked Euler-Lagrangian systems are more complex and closer to actual mechanical systems compared to linear cyber-physical systems. A controller based on a dynamic event-trigger mechanism is designed to achieve consensus control for the networked E-L system when there are no DoS attacks. Sufficient conditions for stability of the closed-loop system are presented, and the resilient consensus problem under energy-constrained DoS attacks is analyzed.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Huaguang Zhang, He Ren, Yunfei Mu, Ji Han
Summary: This article presents a novel data-based adaptive dynamic programming (ADP) method for solving the optimal consensus tracking control problem for discrete-time multiagent systems (MASs) with multiple time delays. The method utilizes causal transformations to provide necessary and sufficient conditions of the equivalent time-delay system, and introduces an error estimator to construct the tracking error using only input and output data. By transforming the tracking error dynamics, the optimal tracking problem is solved by settling the Nash-equilibrium in a graphical game through solving coupled Hamilton-Jacobi equations. The designed data-based ADP algorithm minimizes cost functions and ensures the consensus of MASs without requiring knowledge of system dynamics.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Yafeng Li, Ju H. Park, Libing Wu, Qingkai Kong
Summary: This article investigates the distributed output-feedback adaptive fuzzy leader-following consensus for a type of high-order stochastic nonlinear multiagent systems. A novel dynamic gain filter is constructed to compensate unmeasured states, adaptive laws are constructed using the tuning function method, and the uncertain interaction functions are decomposed and approximated by fuzzy logic systems. By designing a dynamic gain filter-based distributed adaptive fuzzy leader-following protocol using the backstepping method, the problem of computing mutually dependent inputs in existing results is completely avoided.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yongwei Zhang, Bo Zhao, Derong Liu, Shunchao Zhang
Summary: This article presents a distributed fault-tolerant consensus control (DFTCC) approach for multiagent systems using adaptive dynamic programming. By establishing a local fault observer, the potential actuator faults of each agent are estimated. The DFTCC problem is then transformed into an optimal consensus control problem by designing a novel local value function for each agent. A critic-only structure is used to solve the Hamilton-Jacobi-Bellman equation and obtain the approximate local optimal consensus control law for each agent. The effectiveness of the proposed DFTCC scheme is validated through simulation examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Mechanical
Zitao Chen, Kairui Chen, Yun Zhang
Summary: This paper investigates an observer-based optimal consensus tracking problem with a dynamic event-triggered mechanism for nonlinear multi-agent systems. The control process is completed hierarchically with two layers. In the distributed observer layer, an extended distributed observer is designed for each follower to reconstruct states of the leader by exchanging local estimated information with its neighbours. In the decentralized protocol layer, a dynamic event-triggered adaptive dynamic programming algorithm is designed for tracking the observer state, which serves as a reference. The effectiveness of the proposed design is validated through an illustrative example.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Razieh Abdollahipour, Khosro Khandani, Aliakbar Jalali
Summary: This article investigates the consensus problem of linear multiagent systems (MASs) with uncertain dynamics and uncertain switching topology. Uncertainties are represented as granular fuzzy numbers using horizontal membership functions and relative distance measurement arithmetic. The existence of a crisp solution to a fuzzy linear matrix inequality problem is first proved, and then an algorithm is proposed for achieving consensus in the MASs with uncertainty. The efficiency of the proposed algorithm is demonstrated in a simulation example.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Yuanbo Su, Liang Cao, Xiaoshuai Zhou, Yingnan Pan
Summary: In this paper, the adaptive fuzzy singularity-free finite-time optimal consensus problem is investigated for nonlinear pure-feedback multiagent systems. To achieve optimized control, the fuzzy approximation-based reinforcement learning is employed. A distributed adaptive finite-time optimal consensus method is developed by using Butterworth low-pass filter to solve the algebraic loop problem. A new dynamic filtering optimized backstepping method is designed to avoid differentiations of virtual optimal controllers. The effectiveness of the proposed approach is verified through three simulation examples.
FUZZY SETS AND SYSTEMS
(2023)
