Computations of probabilistic output admissible set for uncertain constrained systems

This paper considers uncertain constrained systems, and develops two algorithms for computing a probabilistic output admissible (POA) set which is a set of initial states probabilistically assured to satisfy the constraint. The first algorithm is inspired by an existing randomized sequential technique. The second algorithm alleviates the computational effort based on heuristics. The present algorithms terminate in a finite number of iterations and provide a POA set. Additionally, we can obtain information on the size of the resulting set a posteriori. A numerical simulation demonstrates the applicability of the POA set to a control system design scheme.     

Constraint‐admissible sets for systems with soft constraints and their application in model predictive control

Constraint‐admissible sets have been widely used in the study of control systems with hard constraints. This paper proposes a generalization of the maximal constraint‐admissible set for constrained linear discrete‐time systems to the case where soft or probabilistic constraints are present. Defined in the most obvious way, the maximal probabilistic constraint‐admissible set is not invariant. An inner approximation of it is proposed which is invariant and has other nice properties. The application of this approximate set in a model predictive control framework with probabilistic constraints is discussed, including the feasibility and stability of the resulting closed‐loop system. The effectiveness of the proposed approach is illustrated via numerical examples. Copyright © 2011 John Wiley & Sons, Ltd.     

Robustly asymptotically stable finite-horizon MPC

This paper presents a robustly stable finite-horizon model predictive control (MPC) scheme for linear uncertain systems, in which the uncertainty is not restricted to some specific uncertainty class (polytopic, affine, LFT, etc.). The only requirement is that the state-space matrices remain bounded over the uncertainty set. Suitable constraints are added to the MPC cost function to impose robust asymptotic stability and to deal with input/output constraints. The resulting optimization problem is solved at each time instant in a probabilistic framework using an iterative randomized ellipsoid algorithm (REA). The method is compared in simulation to the existing approach of Kothare, Balakrishnan and Morari [(1996). Robust constrained model predictive control using linear matrix inequalities. Automatica, 32(10), 1361–1379].     

状态时滞时变不确定系统的鲁棒H∞ 输出反馈控制器设计

主要研究了存在状态滞后的线性时变不确定时滞系统的鲁棒H     

Admissible output consensus control for singular swarm systems

For swarm systems consisting of agents described by high-order linear singular systems, admissible output consensus problems are investigated. By using observability decomposition and eigenvalue decomposition techniques, a sufficient condition for admissible output consensus and a necessary and sufficient condition for admissible limited control energy output consensus are obtained. Furthermore, it is shown that stabilisability of the agents is a sufficient condition for admissible output consensualisability, and an approach to solve the protocol design problem is presented. Finally, a numerical example is provided to demonstrate the effectiveness of protocol design approach.     

Time-variant feedback controller based on capture point and maximal output admissible set of a humanoid

This paper formulates the maximal output admissible (MOA) set of a capture point (CP) feedback controller for adaptive humanoid balancing. In previous studies where the MOA set was employed, an adaptive strategy-switching control was proposed. In general, the MOA set requires iterative computation; however, this study has proven that the MOA set of the CP feedback controller can be obtained analytically. This result was applied to a continuous switching feedback gain, which was regarded as a time-variant feedback controller realizing an adaptive response to external disturbances. The validity of the proposed time-variant controller was verified by a whole-body dynamics simulation.     

Decentralized adaptive neural fixed-time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

This article devises a new adaptive fixed-time tracking control strategy for interconnected nonlinear systems containing partially unmeasurable states and time-varying output constraints. Radial basis function neural networks, as function approximators, are utilized to model the unknown functions, and the partially unmeasurable states of the systems are estimated by a reduced-order observer. By constructing a transferred function, system outputs are directly constrained in a time-varying constraint bound. Meanwhile, the first-order sliding mode differentiators are utilized to reduce the computational burden caused by the repeated differentiations of virtual controllers. Under the Lyapunov function and the fixed-time theory, the decentralized adaptive fixed-time controllers are constructed. It is proved that the closed-loop systems are fixed-time stable and the output signals are restricted in the bounded compact set. Finally, two simulation examples demonstrate the validity of the proposed control scheme.     

Output feedback optimal guaranteed cost control of uncertain piecewise linear systems

This paper proposes the output feedback optimal guaranteed cost controller design method for uncertain piecewise linear systems based on the piecewise quadratic Lyapunov functions technique. By constructing piecewise quadratic Lyapunov functions for the closed‐loop augmented systems, the existence of the guaranteed cost controller for closed‐loop uncertain piecewise linear systems is cast as the feasibility of a set of bilinear matrix inequalities (BMIs). Some of the variables in BMIs are set to be searched by genetic algorithm (GA), then for a given chromosome corresponding to the variables in BMIs, the BMIs turn to be linear matrix inequalities (LMIs), and the corresponding non‐convex optimization problem, which minimizes the upper bound on cost function, reduces to a semidefinite programming (SDP) which is convex and can be solved numerically efficiently with the available software. Thus, the output feedback optimal guaranteed cost controller can be obtained by solving the non‐convex optimization problem using the mixed algorithm that combines GA and SDP. Numerical examples show the effectiveness of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.     

Improved ellipsoidal bound of reachable sets for time-delayed linear systems with disturbances

We consider the problem of finding an ellipsoidal reachable set that bounds the states of time-delayed linear systems with bounded peak disturbances. The considered time-delay is time-varying but it has an upper bound in magnitude and rate. Based on the modified Lyapunov-Krasovskii(L-K) type functional, we derive a delay-dependent result expressed in the form of matrix inequalities containing only one non-convex scalar. The result is extended to uncertain time-delayed linear systems. Finally, we show the usefulness of our result by a comparative numerical example.     

基于容许集的多胞不确定系统三模RMPC

针对输入和状态受约束的多胞不确定线性系统,提出了基于容许集的扩大吸引域三模鲁棒模型预测控制方法.在多面体不变集离线模型预测控制算法的基础上引入容许集,以多面体不变集序列的并集作为模态1,基于N步容许集的控制容许集作为模态2,并利用离线设计和在线优化的控制策略,设计了三模变终端约束鲁棒模型预测控制算法,以实现系统渐近稳定.该算法不仅降低了在线运算量,而且扩大了吸引域.最后的仿真结果验证了所提出算法的有效性.     

Robust Kalman filtering for discrete time-varying uncertain systemswith multiplicative noises

In this paper, a robust finite-horizon Kalman filter is designed for discrete time-varying uncertain systems with both additive and multiplicative noises. The system under consideration is subject to both deterministic and stochastic uncertainties. Sufficient conditions for the filter to guarantee an optimized upper bound on the state estimation error variance for admissible uncertainties are established in terms of two discrete Riccati difference equations. A numerical example is given to show the applicability of the presented method     

Stochastic model predictive control for probabilistically constrained Markovian jump linear systems with additive disturbance

Model predictive control (MPC) for Markovian jump linear systems with probabilistic constraints has received much attention in recent years. However, in existing results, the disturbance is usually assumed with infinite support, which is not considered reasonable in real applications. Thus, by considering random additive disturbance with finite support, this paper is devoted to a systematic approach to stochastic MPC for Markovian jump linear systems with probabilistic constraints. The adopted MPC law is parameterized by a mode‐dependent feedback control law superimposed with a perturbation generated by a dynamic controller. Probabilistic constraints can be guaranteed by confining the augmented system state to a maximal admissible set. Then, the MPC algorithm is given in the form of linearly constrained quadratic programming problems by optimizing the infinite sum of derivation of the stage cost from its steady‐state value. The proposed algorithm is proved to be recursively feasible and to guarantee constraints satisfaction, and the closed‐loop long‐run average cost is not more than that of the unconstrained closed‐loop system with static feedback. Finally, when adopting the optimal feedback gains in the predictive control law, the resulting MPC algorithm has been proved to converge in the mean square sense to the optimal control. A numerical example is given to verify the efficiency of the proposed results.     

Adaptive robust output tracking of uncertain strict-feedback nonlinear time-delay systems via control schemes with simple structure

The output tracking control problem is considered for a class of uncertain strict-feedback nonlinear systems with time-varying delays. In the paper, the time-varying delays are assumed to be any non-negative continuous and bounded functions, and it is not necessary for their derivatives to be less than one. It is also assumed that the upper bounds of nonlinear delayed state perturbations and external disturbances are unknown. On the basis of backstepping algorithm, a novel design method is proposed by which some simple adaptive robust output tracking control schemes are synthesised. The proposed design method can avoid the repeated differentiation problem which appears in using the conventional backstepping algorithm, and need not know all the nonlinear upper bound functions of uncertainties, which are repeatedly employed at each step of the backstepping algorithm. In particular, it is not necessary to know any information on the time-varying delays to construct our simple output tracking control schemes. It is also shown that the tracking error can converge uniformly exponentially towards a neighbourhood of the origin. Finally, a numerical example and its simulations are provided to demonstrate the design procedure of the simple method proposed in the paper.     

Design of robust reliable H∞ output feedback control for a class of uncertain linear systems with sensor failure

The problem of robust reliable H_∞ output feedback controller design is investigated for uncertain linear systems with sensor failures within a prespecified subset of sensors. The uncertainty considered here is time-varying norm-bounded parameter uncertainty in the state matrix. The output of a faulty sensor is assumed to be any arbitrary energy-bounded signal. An observer-based output feedback control design is presented which stabilizes the plant and guarantees an H_∞ norm bound on attenuation of augmented disturbances, for all admissible uncertainties as well as sensor failures. The construction of the observer-based output feedback control law requires the positive-definite solutions of two algebraic Riccati equations. The result can be regarded as an extension of existing results on robust H_∞ control and reliable H_∞ control of uncertain linear systems.     

Discovering admissibleWeb services with uncertain QoS

Open and dynamic environments lead to inherent uncertainty of Web service QoS (Quality of Service), and the QoS-aware service selection problem can be looked upon as a decision problem under uncertainty. We use an empirical distribution function to describe the uncertainty of scores obtained from historical transactions. We then propose an approach to discovering the admissible set of services including alternative services that are not dominated by any other alternatives according to the expected utility criterion. Stochastic dominance (SD) rules are used to compare two services with uncertain scores regardless of the distribution form of their uncertain scores. By using the properties of SD rules, an algorithm is developed to reduce the number of SD tests, by which the admissible services can be reported progressively. We prove that the proposed algorithm can be run on partitioned or incremental alternative services. Moreover, we achieve some useful theoretical conclusions for correct pruning of unnecessary calculations and comparisons in each SD test, by which the efficiency of the SD tests can be improved. We make a comprehensive experimental study using real datasets to evaluate the effectiveness, efficiency, and scalability of the proposed algorithm.     

基于LMI 的不确定随机系统输出反馈保性能控制

针对一类不确定连续随机系统,研究了具有输出反馈控制器的保性能控制．首先,对于范数有界 的不确定随机系统,得到了具有输出反馈控制器的闭环系统的二次型性能指标的上界一般形式．其次,应用 线性矩阵不等式技术,研究了其闭环系统的二次稳定和二次保性能稳定;并且依据线性矩阵不等式优化方法, 得到了使闭环系统渐近稳定的最优输出反馈保性能控制器．最后,给出仿真算例,验证本文方法的可行性和 有效性．     

状态时滞时变不确定系统的鲁棒H∞ 输出反馈控制器设计

主要研究了存在状态滞后的线性时变不确定时滞系统的鲁棒∞控制分析和综合问题,给出了对所有容许不确定性,被控对象可二次镇定和满足从干扰输入到控制输出的Ｈ∞范数界约束的动态输入出反馈鲁棒Ｈ∞控制分析结果,将不确定时滞系统的鲁棒Ｈ∞输出反馈控制器设问题等价对两个线性时不变系统的状态反馈标准Ｈ∞控制问题,并由此得到反馈阵和观测阵,了终得到鲁棒Ｈ∞控制器综合设计方法。     

Robust Admissibility and Stabilization of Uncertain Singular Fractional-Order Linear Time-Invariant Systems

This paper addresses the robust admissibility problem in singular fractional-order continuous time systems. It is based on new admissibility conditions of singular fractional-order systems expressed in a set of strict linear matrix inequalities (LMIs). Then, a static output feedback controller is designed for the uncertain closed-loop system to be admissible. Numerical examples are given to illustrate the proposed methods.      

Guaranteed cost repetitive control for uncertain discrete-time systems

This paper considers the problem of guaranteed cost repetitive control for uncertain discrete-time systems. The uncertainty in the system is assumed to be norm-bounded and time-varying. The objective is to develop a novel design method for the repetitive control systems by introducing a robust output feedback controller so that the closed-loop system is quadratically stable and a certain bound of performance index is guaranteed for all admissible uncertainties. Necessary and sufficient conditions for the existence of such controllers are derived in terms of linear matrix inequality (LMI). A suboptimal guaranteed cost controller is obtained by solving an optimization problem with LMI constraints. Finally, a simulation example is provided to illustrate the validity of the proposed method.     

Interval estimation for uncertain systems with time-varying delays

The estimation problem for uncertain time-delay systems is addressed. A design method of reduced-order interval observers is proposed. The observer estimates the set of admissible values (the interval) for the state at each instant of time. The cases of known fixed delays and uncertain time-varying delays are analysed. The proposed approach can be applied to linear delay systems and nonlinear time-delay systems in the output canonical form. It involves the properties of quasi-monotone/Metzler/cooperative systems. In this framework, it is shown that if under a suitable coordinate transformation the delay-free subsystem is cooperative, then the delayed estimation error dynamics inherits this property. The conditions to find the observer gains are formulated in the form of LMI. The framework efficiency is demonstrated on examples of nonlinear systems.