多智能体系统的自适应群集分布式优化

本文对具有非线性函数群集行为的连续时间多智能体系统的分布式优化问题进行了研究。本文的目的是 使局部代价函数之和最小。每个智能体只知道与其对应的代价函数。为了解决这一问题,本文设计了一个分布式 控制律,在这个研究中该控制律仅仅依赖于自己和邻居的速度。通过李雅普诺夫稳定性证明了多智能体系统的收 敛性,而且在最小化局部代价函数之和的同时所有智能体可以避免碰撞。最后,通过一个仿真案例来说明所获得 的分析结果。     

基于多智能体交通绿波效应分布式协同控制算法

基于"绿波"效应的交通控制通过实现干道上的车流不间断地经过多个交通灯路口而不停止,是目前公认的最有效率的交通控制策略之一.然而随着城市交通规模的不断扩大,传统的集中式交通控制方法可能遇到计算和通信上的瓶颈.而当路口交通灯只能获取城市交通网络全局有限的信息时,传统的分布式控制方法可能十分低效.提出了一种基于多智能体的交通灯分布式绿波自适应控制方法.在该设计中,每一个交通灯路口通过一个非集中式的协同智能体来控制.其核心是,智能体通过预测自身下一时刻的状态进行自主决策.由于只有来自邻居路口的车辆能够直接影响当前路口下一步的状态,这一决策过程仅需要智能体通过与邻居智能体间的局部交互来完成.描述了基于多智能体交通灯分布式"绿波"效应的控制算法,并通过仿真实验验证了该方法在大规模城市交通系统中的可行性.     

拒绝服务攻击下领导-跟随多智能体系统的均方一致性研究

针对带有过程噪声和测量噪声的领导-跟随多智能体系统,研究拒绝服务攻击下多智能体系统的一致性问题.首先,设计基于卡尔曼滤波的状态观测器,对智能体状态进行有效准确的估计;然后,基于预测控制理论提出一种基于状态估计信息的分布式预测控制算法,从而实现领导-跟随多智能体系统的均方一致性控制,并给出拒绝服务攻击环境下实现领导-跟随多智能体系统均方一致性的充分必要条件;最后,通过数值仿真验证所提出方法的正确性和有效性.     

单向通信拓扑领航者动态未知线性多智能体系统的协同迭代学习控制

研究单向通信拓扑领航者动态未知线性多智能体系统的协同跟踪问题.基于邻居的相对状态信息,设计了分布式迭代学习控制律实现对领航者的协同跟踪控制,采用Lyapunov-Krasovskii函数分析闭环系统的稳定性与收敛性.进而,将状态反馈结论拓展到输出反馈,通过构造局部观测器估计不可量测的状态信息,采用估计的相对状态信息设计了分布式迭代学习控制器.对于以上两种情形,多智能体系统在通讯拓扑含有生成树的条件下能够实现与领航者的状态同步,同时,所设计的分布式迭代学习律能够对领航者未知输入进行精确估计.仿真实例验证了所提方法的有效性.     

基于分布式模型预测控制的无人机编队控制

针对多四旋翼无人机编队在巡航飞行过程中队形形成和保持问题,采用分布式模型预测控制方法将该问题转化为在线滚动优化问题.建立线性时不变的编队运动模型,进而在考虑状态和输入约束,不考虑时延、外界干扰、噪声的情况下,利用领航跟随策略设计一种分布式模型预测控制器,通过引入自身和邻居的假设状态轨迹设计代价函数.其中邻居信息的交互是在有向、时不变通信拓扑结构下进行的.基于该控制器,无人机能够在跟踪目标轨迹的同时,快速形成预先设定的队形并保持队形飞行.通过引入终端等式约束保证系统稳定,进而将目标函数作为Lyapunov函数,给出编队系统渐近稳定的充分条件.最后,利用6架无人机仿真验证控制算法的有效性和优越性.     

二阶非线性多智能体系统有限时间分布式优化

本文研究一类具有未知常参数的二阶非线性多智能体系统的有限时间自适应分布式优化.首先,通过给定各个智能体的二次目标函数,并结合多智能体系统达到一致性的条件,构造含有惩罚因子的惩罚函数,提出加速智能体状态收敛至目标函数最优解的控制策略.其次,在给定惩罚因子下,基于幂积分方法和有限时间稳定理论,设计有限时间分布式自适应控制协...     

含约束和通信时滞的多智能体系统包含控制

随着人工智能的发展,包含控制成为近年来研究的热点问题.为了解决有向切换通信拓扑下含非凸输入约束和通信时滞的采样多智能体系统包含控制问题,设计了一种基于投影的分布式非线性包含控制算法.该算法只需要利用智能体自身和相邻智能体的交互信息就能实现输入受限跟随者的包含.首先将跟随者与领导者构成的凸区域的最大距离选定为李雅普诺夫函...     

带有不匹配干扰的多智能体系统有限时间积分滑模控制

针对动态多智能体系统协同控制问题,本文研究了带有不匹配干扰的二阶多智能体系统的有限时间包容控制,提出了基于非线性积分滑模控制（Integral sliding-mode control,ISMC）的复合分布式包容控制算法.首先利用Lyapunov稳定性和齐次性定理,分析了未受扰系统的有限时间包容控制问题;然后针对存在不匹配干扰的多智能体动态系统,设计非线性有限时间干扰观测器估算智能体的状态和干扰,提出基于干扰观测器的复合分布式积分滑模控制协议,结合现代控制理论和滑模控制理论,研究了带有不匹配干扰的多智能体系统有限时间包容控制问题.最后数值仿真证明了控制算法的有效性.     

基于事件触发的时变拓扑多智能体系统编队控制

针对资源受限的时变拓扑多智能体系统的编队控制问题,提出一种基于复合误差信息事件触发机制以减少不必要的信息传输,降低带宽占用,并设计了多智能体系统的编队控制协议和分布式事件触发机制.通过将多智能体系统的编队控制问题转化为闭环延时系统的稳定性问题,构造Lyapunov-Krasovskii函数并利用线性矩阵不等式技术,给出多智能体系统实现编队的充分条件.最后,通过仿真验证了所提出方法的有效性.     

一阶多智能体受扰系统的自抗扰分布式优化算法

研究一类具有未知外部干扰的一阶多智能体系统的分布式优化问题.在分布式优化任务中,每个智能体只被容许利用自己的局部目标函数和邻居的状态信息,设计一个分布式优化算法,使全局目标函数取得最小值,其中全局目标函数是所有局部目标函数之和.针对该问题,首先提出由扩张状态观测器和优化算法组成的自抗扰分布式优化算法.其次,在Lyapu...     

Formation and obstacle avoidance control for multiagent systems

This paper considers the problems of formation and obstacle avoidance for multiagent systems.The objective is to design a term of agents that can reach a desired formation while avoiding collision with obstacles.To reduce the amount of information interaction between agents and target,we adopt the leader-follower formation strategy.By using the receding horizon control (RHC),an optimal problem is formulated in terms of cost minimization under constraints.Information on obstacles is incorporated online as sensed in a limited sensing range.The communication requirements between agents are that the followers should obtain the previous optimal control trajectory of the leader to each update time.The stability is guaranteed by adding a terminal-state penalty to the cost function and a terminal-state region to optimal problem.Finally,simulation studies are provided to verify the effectiveness of the proposed approach.     

Broadcast stochastic receding horizon control of multi-agent systems

Optimal regulation of stochastically behaving agents is essential to achieve a robust aggregate behavior in a swarm of agents. How optimally these behaviors are controlled leads to the problem of designing optimal control architectures. In this paper, we propose a novel broadcast stochastic receding horizon control architecture as an optimal strategy for stabilizing a swarm of stochastically behaving agents. The goal is to design, at each time step, an optimal control law in the receding horizon control framework using collective system behavior as the only available feedback information and broadcast it to all agents to achieve the desired system behavior. Using probabilistic tools, a conditional expectation based predictive model is derived to represent the ensemble behavior of a swarm of independently behaving agents with multi-state transitions. A stochastic finite receding horizon control problem is formulated to stabilize the aggregate behavior of agents. Analytical and simulation results are presented for a two-state multi-agent system. Stability of the closed-loop system is guaranteed using the supermartingale theory. Almost sure (with probability 1) convergence of the closed-loop system to the desired target is ensured. Finally, conclusions are presented.     

A stabilizing receding horizon regulator for nonholonomic mobile robots

This paper presents a receding horizon (RH) controller used for regulating a nonholonomic mobile robot. The RH control stability is guaranteed by adding a terminal-state penalty to the cost function and a terminal-state region to optimization constraints. A suboptimal solution to the optimization problem is sufficient to achieve stability. A new terminal-state penalty and its corresponding terminal-state constraints are found. Implementation and simulation results are provided to verify the proposed control strategy.     

Distributed receding horizon control for multi-vehicle formation stabilization

We consider the control of interacting subsystems whose dynamics and constraints are decoupled, but whose state vectors are coupled non-separably in a single cost function of a finite horizon optimal control problem. For a given cost structure, we generate distributed optimal control problems for each subsystem and establish that a distributed receding horizon control implementation is stabilizing to a neighborhood of the objective state. The implementation requires synchronous updates and the exchange of the most recent optimal control trajectory between coupled subsystems prior to each update. The key requirements for stability are that each subsystem not deviate too far from the previous open-loop state trajectory, and that the receding horizon updates happen sufficiently fast. The venue of multi-vehicle formation stabilization is used to demonstrate the distributed implementation.     

基于自适应精确罚函数的分布式资源分配算法

在多智能体系统中, 分布式资源分配问题是近年来研究热点之一. 分布式资源分配问题旨在通过智能体间信息交互实现资源最优配置. 其中智能体局部约束给算法设计带来巨大挑战. 首先, 针对一阶多智能体系统, 提出基于自适应精确罚函数的分布式资源分配算法, 其中各智能体利用距离函数实现局部约束求解. 此外, 自适应设计思想旨在避免算法对全局先验知识获取. 其次, 利用跟踪技术实现二阶多智能体系统算法设计. 并利用凸函数和非光滑分析法给出严谨的收敛性分析. 最后, 仿真结果验证了本文所设计优化算法对强凸分布式资源分配问题的有效性.     

Two-person zero-sum Markov games: receding horizon approach

We consider a receding horizon approach as an approximate solution to two-person zero-sum Markov games with infinite horizon discounted cost and average cost criteria. We first present error bounds from the optimal equilibrium value of the game when both players take "correlated" receding horizon policies that are based on exact or approximate solutions of receding finite horizon subgames. Motivated by the worst-case optimal control of queueing systems by Altman, we then analyze error bounds when the minimizer plays the (approximate) receding horizon control and the maximizer plays the worst case policy. We finally discuss some state-space size independent methods to compute the value of the subgame approximately for the approximate receding horizon control, along with heuristic receding horizon policies for the minimizer.     

Distributed optimisation for resource allocation with event-triggered communication over general directed topology

This paper focuses on the resource allocation problem(RAP) with constraints under a fixed general directed topology by using the distributed sub-gradient algorithm with event-triggered scheme in multi-agent systems, where each agent owns a cost function and its state value is bounded. The distributed sub-gradient algorithm aims to minimise the total cost by a distributed manner while achieving an optimal solution. Unlike centralised methods, the triggering condition and algorithm for each agent are fully decentralised. At each instant of time, each agent updates its state by employing the states which are collected from itself and its neighbouring agents at their last triggering time. In order to illustrate the effectiveness of the proposed sub-gradient algorithm with event-triggered control law, one simulation example is presented before the conclusion.     

Receding Horizon Optimization Approach to PID Controller Parameters Auto-tuning

A novel supervised receding horizon optimal scheme is presented for discrete time systems in the process control.In the employing level,PID controller is used,while the receding horizon approach is applied to the optimized level.The considered problem is to optimize the employing level PID controller parameters through minimizing a generalized predictive control criterion.Compared with a fixed parameters PID controller,the proposed algorithm provides well performance over a range of operating condition.     

PID控制器参数调整的滚动优化算法

A novel supervised receding horizon optimal scheme is presented for discrete time systems in the process control. In the employing level, PID controller is used, while the receding horizon approach is applied to the optimized level. The considered problem is to optimize the employing level PID controller parameters through minimizing a generalized predictive control criterion. Compared with a fixed parameters PID controller, the proposed algorithm provides well performance over a range of operating condition.     

Input-to-state stability of robust receding horizon control with an expected value cost

This paper is concerned with the stability of a class of receding horizon control (RHC) laws for constrained linear discrete-time systems subject to bounded state disturbances and convex state and input constraints. The paper considers the class of finite horizon feedback control policies parameterized as affine functions of the system state, calculation of which can be shown to be tractable via a convex reparameterization. When minimizing the expected value of a finite horizon quadratic cost, we show that the value function is convex. When solving this optimal control problem at each time step and implementing the result in a receding horizon fashion, we provide sufficient conditions under which the closed-loop system is input-to-state stable (ISS).