Distributed LQR Design for Identical Dynamically Decoupled Systems

We consider a set of identical decoupled dynamical systems and a control problem where the performance index couples the behavior of the systems. The coupling is described through a communication graph where each system is a node and the control action at each node is only function of its state and the states of its neighbors. A distributed control design method is presented which requires the solution of a single linear quadratic regulator (LQR) problem. The size of the LQR problem is equal to the maximum vertex degree of the communication graph plus one. The design procedure proposed in this paper illustrates how stability of the large-scale system is related to the robustness of local controllers and the spectrum of a matrix representing the desired sparsity pattern of the distributed controller design problem.     

Distributed model predictive control for constrained linear systems

We address the distributed model predictive control (MPC) for a set of linear local systems with decoupled dynamics and a coupled global cost function. By the decomposition of the global cost function, the distributed control problem is converted to the MPC for each local system associated with a cost involving neighboring system states and inputs. For each local controller, the infinite horizon control moves are parameterized as N free control moves followed by a single state feedback law. An interacting compatibility condition is derived, disassembled and incorporated into the design of each local control so as to achieve the stability of the global closed‐loop system. Each local system exchanges with its neighbors the current states and the previous optimal control strategies. The global closed‐loop system is shown to be exponentially stable provided that all the local optimizers are feasible at the initial time. Copyright © 2009 John Wiley & Sons, Ltd.     

Distributed Receding Horizon Control of Dynamically Coupled Nonlinear Systems

This paper considers the problem of distributed control of dynamically coupled nonlinear systems that are subject to decoupled constraints. Examples of such systems include certain large scale process control systems, chains of coupled oscillators and supply chain management systems. Receding horizon control (RHC) is a method of choice in these venues as constraints can be explicitly accommodated. In addition, a distributed control approach is sought to enable the autonomy of the individual subsystems and reduce the computational burden of centralized implementations. In this paper, a distributed RHC algorithm is presented for dynamically coupled nonlinear systems that are subject to decoupled input constraints. By this algorithm, each subsystem computes its own control locally. Provided an initially feasible solution can be found, subsequent feasibility of the algorithm is guaranteed at every update, and asymptotic stabilization is established. The theoretical conditions for feasibility and stability are shown to be satisfied for a set of coupled Van der Pol oscillators that model a walking robot experiment. In simulations, distributed and centralized receding horizon controllers are employed for stabilization of the oscillators. The numerical experiments show that the controllers perform comparably, while the computational savings of the distributed implementation over the centralized implementation is clearly demonstrated.     

Stabilization of nonlinear systems with state and control constraints using Lyapunov-based predictive control

This work considers the problem of stabilization of nonlinear systems subject to state and control constraints, for cases where the state constraints need to be enforced at all times (hard constraints) and where they can be relaxed for some time (soft constraints). We propose a Lyapunov-based predictive control design that guarantees stabilization and state and input constraint satisfaction for all times from an explicitly characterized set of initial conditions. An auxiliary Lyapunov-based analytical bounded control design is used to characterize the stability region of the predictive controller and also provide a feasible initial guess to the optimization problem in the predictive controller formulation. For the case when the state constraints are soft, we propose a switched predictive control strategy that reduces the time during which state constraints are violated, driving the states into the state and input constraints feasibility region of the Lyapunov-based predictive controller. We demonstrate the application of the Lyapunov-based predictive controller designs through a chemical process example.     

具有多图拓扑结构的同类智能体LQR控制

本文考虑具有一般线性时不变动态特性的多智能体系统优化控制问题. 将智能体之间的通讯拓扑结构建模成具有自环的无向多图, 每个子系统就是一个节点, 每个节点的控制行为只与本身及邻居节点有关. 由于反馈矩阵具有块对角结构约束, 本文研究的LQR控制问题本质上是一类结构优化问题. 最小化系统LQR性能指标等价于最小化单个智能体性能指标和. 基于线性矩阵不等式得到系统的次优性能指标, 指出LQR性能域是凸集. 由此多智能体系统的LQR控制转化为若干个子系统的LQR控制, 可以通过求解系数是Laplacian矩阵最小最大特征值的两个矩阵不等式得到反馈增益. 数值例子验证了方法的有效性.     

Optimization over state feedback policies for robust control with constraints

This paper is concerned with the optimal control of linear discrete-time systems subject to unknown but bounded state disturbances and mixed polytopic constraints on the state and input. It is shown that the class of admissible affine state feedback control policies with knowledge of prior states is equivalent to the class of admissible feedback policies that are affine functions of the past disturbance sequence. This implies that a broad class of constrained finite horizon robust and optimal control problems, where the optimization is over affine state feedback policies, can be solved in a computationally efficient fashion using convex optimization methods. This equivalence result is used to design a robust receding horizon control (RHC) state feedback policy such that the closed-loop system is input-to-state stable (ISS) and the constraints are satisfied for all time and all allowable disturbance sequences. The cost to be minimized in the associated finite horizon optimal control problem is quadratic in the disturbance-free state and input sequences. The value of the receding horizon control law can be calculated at each sample instant using a single, tractable and convex quadratic program (QP) if the disturbance set is polytopic, or a tractable second-order cone program (SOCP) if the disturbance set is given by a 2-norm bound.     

基于终端不变集的 Markov 跳变系统约束预测控制

针对离散 Markov 跳变系统, 研究带输入输出约束的有限时域预测控制问题. 对于给定预测时域内的每条模态轨迹, 设计控制输入序列, 驱动系统状态到达相应的终端不变集内, 在预测时域外, 则寻求一个虚拟的状态反馈控制器以保证系统的随机稳定性, 在此基础上, 分别给出了以线性矩阵不等式 (LMI) 描述的带输入、输出约束预测控制器的设计方法.     

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).     

Distributed model predictive control of dynamically decoupled systems with coupled cost

This paper considers the distributed model predictive control (DMPC) of systems with interacting subsystems having decoupled dynamics and constraints but coupled costs. An easily-verifiable constraint is introduced to ensure asymptotic stability of the overall system in the absence of disturbance. The constraint introduced has a parameter which allows for the performance of the DMPC system to approach that controlled by a centralized model predictive controller. When the subsystems are linear and additive disturbance is present, the added constraint ensures the state of each subsystem converges to its respective minimal disturbance invariant set. The approach is demonstrated via several numerical examples.     

Event‐triggered MPC design for distributed systems toward global performance

This paper studies the coordination control problem of stabilizing large‐scale dynamically coupled systems via a novel event‐triggered distributed model predictive control (DMPC) approach. In order to achieve global performance, certain constraints relevant to the triggering instant are imposed on the DMPC optimization problem, and triggering mechanisms are designed by taking into account coupling influences. Specifically, the triggering conditions derived from the feasibility and stability analysis are based on the local subsystem state and the information received from its neighbors. Based on these triggering mechanisms, the event‐triggered DMPC algorithm is built, and a dual‐mode strategy is adopted. As a result, the controllers solve the optimization problem and coordinate with each other asynchronously, which reduces the amount of communication and lowers the frequency of controller updates while achieving global performance. The recursive feasibility of the proposed event‐triggered DMPC algorithm is proved, and sufficient parameter conditions about the prediction horizon and the triggering threshold are established. It shows that the system state can be gradually driven into the terminal set under the proposed strategy. Finally, an academic example and a realistic simulation problem to the water level of a 4‐tank system are provided to verify the effectiveness of the proposed algorithm.     

Constrained RHC for LPV systems with bounded rates of parameter variations

This paper studies a stabilization problem of polytopically uncertain linear parameter varying systems with input constraints and bounded rates of parameter variations. In the framework of finite receding horizon control (RHC), a system containing “parameter” uncertainties is modified into a system with “parameter-incremental” uncertainties within each horizon. For the system modified in this manner, a robust RHC is designed by solving an optimization problem at each time instant. Based on the feasibility of the problem and the optimality of its solution, the closed-loop system stability is guaranteed. A numerical example is included to illustrate the validity of the results.     

Adaptive sliding mode control for interval type-2 stochastic fuzzy systems subject to actuator failures

This paper investigates the problem of adaptive sliding mode control for a class of interval type-2 Itô stochastic fuzzy systems, where the actuator failures may happen. The sliding function is firstly constructed, whose key feature is its dependence on the upper membership functions. And then, an adaptive scheme is proposed to estimate the effectiveness lose values of faulty actuators, and a sliding mode controller based on estimating scheme is designed such that the reachability of the specified sliding surface can be guaranteed even in the presence of actuator failures, in which the lower and upper membership functions are involved. Moreover, the stability conditions of sliding mode dynamics are derived, which involve some coupling terms of Lyapunov matrix and the sliding matrix. By introducing additional matrix variables and employing the cone complementary linearisation algorithm, the above nonlinear stability criterions are decoupled and lastly converted to a minimisation problem with linear constraints. Finally, a numerical example demonstrates the validity of the proposed method.     

Distributed model predictive control for multiagent systems with improved consistency

This paper addresses an improved distributed model predictive control (DMPC) scheme for multiagent systems with an attempt to improving its consistency. The deviation between what an agent is actually doing and what its neighbors believe that agent is doing is penalized in the cost function of each agent. At each sampling instant the compatibility constraint of each agent is set tighter than the previous sampling instant. Like the traditional approach, the performance cost is utilized as the Lyapunov function to prove closed-looped stability. The closed-loop stability is guaranteed if the weight matrix for deviation in the cost function are sufficiently large. The proposed distributed control scheme is formulated as quadratic programming with quadratic constraints. A numerical example is given to illustrate the effectiveness of the proposed scheme.     

Nonfragile containment control of nonlinear multi-agent systems via a disturbance observer-based approach

In this article, we consider the nonfragile containment control problem of nonlinear multi-agent systems (MASs) with exogenous disturbance where the communication links among agents under consideration is directed. Firstly, based on relative output measurements between the agent and its neighbors, a disturbance observer-based control protocol is proposed to solve the containment control problem of MASs with inherent nonlinear dynamics and exogenous disturbances. Secondly, because of the additional tuning of parameters in the real control systems, uncertainties in the designing of observer and controller gains always occur, and as a result, an output feedback controller with disturbance rejection is conceived and the containment control problem of nonlinear MASs with nonfragility is thoroughly investigated. Then, depending on matrix transformation and inequality technique, sufficient conditions of the designed controller gains exist, which is derived from the asymptotic stability analysis problem of some containment error dynamics of MASs. Finally, two simulation examples are exploited to illustrate the effectiveness of the proposed techniques.     

具有不确定性的非线性切换系统的约束预测控制

针对一类具有不确定性和变量约束的非线性切换系统, 提出了一种基于Lyapunov函数的预测控制方法, 其中状态约束分为两种情况: 1)要求状态变量在所有时刻都满足约束(称为硬约束); 2)允许状态在某些时刻超出约束(称为软约束). 主要思想是: 对切换系统的每一个子系统, 在输入和状态均受约束的情况下, 设计基于Lyapunov函数的有界控制器和预测控制器, 在两者之间适当切换, 得到初始稳定区域的描述并使得子闭环系统保持稳定. 对整个切换系统, 设计适当的切换律以保证: 1)在切换时刻, 闭环系统的状态处在切入系统的稳定区域内; 2)切入模块的Lyapunov函数是非增的, 从而可保证稳定性. 在状态变量的约束是软约束时, 对每一子模块首先设计一个控制策略, 尽快将状态控制到初始稳定区域, 然后再利用稳定区域内的控制律使系统稳定.     

Practical stabilization of robotic systems by decentralized control

The practical stability of large-scale robotic systems with variable parameters is considered. The control should ensure the system state to belong to a finite region around the nominal trajectory for various values of parameters. The robotic system is considered as a set of decoupled subsystems each of which corresponds to one degree of freedom. For each decoupled subsystem a local controller is synthesized ensuring the practical stability of free subsystem. Then the practical stability of the coupled global system is analysed for various values of mechanical parameters. This permits the synthesis of decentralized control which provides practical stabilization of robotic systems in given finite regions and for the given set of allowable parameter values. Global control is also introduced. Decentralized control for a manipulation robot with variable payload is synthesized.     

General receding horizon control for linear time-delay systems

A general receding horizon control (RHC), or model predictive control (MPC), for time-delay systems is proposed. The proposed RHC is obtained by minimizing a new cost function that includes two terminal weighting terms, which are closely related to the closed-loop stability. The general solution of the proposed RHC is derived using the generalized Riccati method. Furthermore, an explicit solution is obtained for the case where the horizon length is less than or equal to the delay size. A linear matrix inequality (LMI) condition on the terminal weighting matrices is proposed, under which the optimal cost is guaranteed to be monotonically non-increasing. It is shown that the monotonic condition of the optimal cost guarantees closed-loop stability of the RHC. Simulations demonstrate that the proposed RHC effectively stabilizes time-delay systems.     

存在部分解耦扰动的网络化系统容错控制

针对一类存在执行器故障和部分解耦扰动的离散时间网络化控制系统,研究测量数据随机丢失情况下的主动容错控制问题。首先,通过模型转换将原系统化为一个与之等价的状态增广系统;然后在考虑测量数据发生随机丢失情况下,构造未知输入观测器(unknown input observer, UIO)实现对系统状态与故障的联合估计,再基于状态和故障的在线估计值,设计基于信号补偿的容错控制律实现对原系统的主动容错控制。在该容错控制算法中,观测器与控制器增益的存在性条件均可利用李雅普诺夫稳定性理论对误差系统进行随机分析得到,相应的估计器和控制器参数可通过在线求解具有凸约束的矩阵不等式获得。最后,通过一个喷气式发动机模型的仿真算例验证所提出的故障估计与主动容错控制方法的有效性。     

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.     

Robust time-optimal control of constrained linear Systems

A version of dynamic programming, which computes level sets of the value function rather than the value function set itself, is used to design robust non-linear controllers for linear, discrete-time, dynamical systems subject to hard constraints on controls and states. The controller stabilizes the system and steers all trajectories emanating in a prescribed set to a control invariant set in minimum time. For the robust regulator problem, the control invariant terminal set is a neighborhood, preferably small, of the origin; for the robust tracking problem, the control invariant terminal set is a neighborhood of the invariant set in which the tracking error is zero. Two non-linear controllers which utilize the level sets of the value function, are described. The first requires the controller to solve, on-line, a modest linear program whose dimension is approximately the same as that of the control variable. The second decomposes each level set into a set of simplices; a piecewise linear control law, affine in each simplex, is then constructed.