Robust sampled‐data model predictive control for networked systems with time‐varying delay

In this paper, the problem of sampled‐data model predictive control (MPC) is investigated for linear networked control systems with both input delay and input saturation. The delay‐induced nonlinearity is overapproximatively modeled as a polytopic inclusion. The nonlinear behavior of input saturation is expressed as a convex polytope. The resulting closed‐loop systems are represented as linear systems with polytopic and additive norm‐bounded uncertainties. The aim is to determine a robust MPC controller that asymptotically stabilizes the uncertain system at the origin with a certain level of quadratic performance. The effectiveness of the proposed algorithm is demonstrated by a numerical example.     

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.     

A new OFRMPC formulation with on‐line synthesis of the dynamic output feedback controller

This paper proposes a new approach for the design of output feedback robust model predictive control (OFRMPC) with a dynamic output feedback controller (DOFC) for linear uncertain systems subject to input and output constraints. The main contribution of this work is the full on‐line synthesis of the DOFC as part of a convex optimization problem, with constraint satisfaction and asymptotic stability guarantees. A numerical example is employed to illustrate the advantage of the proposed control law, as compared with another OFRMPC strategy with partial DOFC synthesis. The present paper also points out an inconsistency in the mathematical development of a previous related OFRMPC formulation (‘improved dynamic output feedback RMPC for linear uncertain systems with input constraints’). Copyright © 2017 John Wiley & Sons, Ltd.     

ROBUST MODEL PREDICTIVE CONTROL FOR INPUT SATURATED AND SOFTENED STATE CONSTRAINTS

This paper starts with a brief review of robust model predictive control (RMPC) algorithsms for uncertain systems using linear matrix inequalities (LMIs) subject to input and/or output saturated constraints. However when RMPC has both input and state constraints, a difficulty will arise due to the inability of the optimizer to satisfy the state constraints due to the constraints on inputs. Therefore, a novel RMPC scheme is presented that softens the state constraints as penalty terms are added to its objective function. These terms maintain state violation at low values until a constrained solution is returned. The state violation can be regulated by changing the value of the weighting factor. A novel robust predictive controller for input saturated and softened state constraints for linear time varying (LTV) systems with polytopic model uncertainties is presented.     

Robust model predictive control under redundant channel transmission with applications in networked DC motor systems

In networked systems, intermittent failures in data transmission are usually inevitable due to the limited bandwidth of the communication channel, and an effective countermeasure is to add redundance so as to improve the reliability of the communication service. This paper is concerned with the model predictive control (MPC) problem by using static output feedback for a class of polytopic uncertain systems with redundant channels under both input and output constraints. By utilizing the min–max control approach combined with stochastic analysis, sufficient conditions are established to guarantee the feasibility of the designed MPC scheme that ensures the robust stability of the closed‐loop system. In terms of the solution to an auxiliary optimization problem, an easy‐to‐implement MPC algorithm is proposed to obtain the desired sub‐optimal control sequence as well as the upper bound of the quadratic cost function. Finally, to illustrate its effectiveness, the proposed design method is applied to control a networked direct current motor system. Copyright © 2016 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].     

Optimality of robust disturbance‐feedback strategies

In this paper, robust disturbance‐feedback strategies for finite time‐horizon problems are studied. Linear discrete‐time systems subject to linear control, state constraints, and quadratic objective functions are considered. In addition, persistent disturbances, which enter the system additively and are contained in a polytopic set, act on the system. The synthesis of robust strategies leads in the case of the traditional robust state‐feedback and open‐loop min–max strategies to, respectively, nonconvex problems or conservatism. However, robust disturbance‐feedback problems can easily be reformulated as convex problems and solved by tractable linear matrix inequalities. Hence this approach bypasses the nonconvexity issue while maintaining the advantages of feedback strategies. As a key result, it is shown that both sources of conservatism attributed to this approach, namely, the relaxation method and the affine parametrization, can be removed at the expense of an increase in computational effort. Copyright © 2015 John Wiley & Sons, Ltd.     

凸多面体不确定系统的符号稳定性分析

针对一类凸多面体描述的连续线性不确定系统研究其符号稳定性条件,从而从符号稳定的角度探索一种新的不确定系统鲁棒控制器设计方法.在矩阵符号稳定定义和必要条件的基础上,给出了矩阵集合符号稳定的定义和完整同源稳定符号型集合的概念,得到了凸多面体不确定系统符号稳定的充分必要条件.进一步分析了符号稳定凸多面体不确定系统的特征根分布与主对角线元素的关系,提出了基于符号稳定的不确定系统状态反馈镇定控制综合方法.相比于基于Lyapunov稳定性理论的不确定系统鲁棒控制方法,本文所提方法避免了求解线性矩阵不等式,并且更符合工程设计的习惯.通过一类三轴稳定卫星姿态控制问题的算例和仿真验证了所提方法的有效性.     

Design of robust model predictive control with input constraints

This article addresses the problem of designing a robust output feedback model predictive control (MPC) with input constraints, which ensures a parameter-dependent quadratic stability and guaranteed cost for the case of linear polytopic systems. A new heuristic method is introduced to guarantee input constraints for the MPC. To reject disturbances and maintain the process at the optimal operating conditions or setpoints, the integrator is added to the controller design procedure. Finally, some numerical examples are given to illustrate the effectiveness of the proposed method.     

Synthesis of Mixed Objective Output Feedback Robust Model Predictive Control

Aiming at the constrained polytopic uncertain system with energy‐bounded disturbance and unmeasurable states, a novel synthesis scheme to design the output feedback robust model predictive control(MPC)is put forward by using mixed H₂/H_∞ design approach. The proposed scheme involves an offline design of a robust state observer using linear matrix inequalities(LMIs)and an online output feedback robust MPC algorithm using the estimated states in which the desired mixed objective robust output feedback controllers are cast into efficiently tractable LMI‐based convex optimization problems. In addition, the closed‐loop stability and the recursive feasibility of the proposed robust MPC are guaranteed through an appropriate reformulation of the estimation error bound (EEB). A numerical example subject to input constraints illustrates the effectiveness of the proposed controller.     

Exponential stabilization for nonlinear coupled dynamical systems via impulsive and sampled‐data control with input constraints

In this paper, exponential stabilization for nonlinear coupled dynamical systems by considering sampled‐data controller and impulsive controller with input constraints is investigated. Based on polytopic representation approach, some linear matrix inequalities are established to guarantee local exponential stabilization of nonlinear coupled dynamical systems. Moreover, by using polytopic differential inclusion, for the case of the partial input saturation involving in the impulsive controller, we also obtain several sufficient conditions ensuring local exponential stabilization. Finally, three examples are presented to show the effectiveness of theoretical analysis.     

On stability and stabilization of periodic discrete-time systems with an application to satellite attitude control

An alternative stability analysis theorem for nonlinear periodic discrete-time systems is presented. The developed theorem offers a trade-off between conservatism and complexity of the corresponding stability test. In addition, it yields a tractable stabilizing controller synthesis method for linear periodic discrete-time systems subject to polytopic state and input constraints. It is proven that in this setting, the proposed synthesis method is strictly less conservative than available tractable synthesis methods. The application of the derived method to the satellite attitude control problem results in a large region of attraction.     

A model predictive control framework for constrained uncertain positive systems

In this paper, a new model predictive control framework is proposed for positive systems subject to input/state constraints and interval/polytopic uncertainty. Instead of traditional quadratic performance index, simple linear performance index, linear Lyapunov function, cone invariant set with linear form and linear computation tool are first adopted. Then, a control law that can handle the constraints and robustly stabilise the systems is proposed. The advantages of the new framework lie in the following facts: (1) an equivalent linear problem is formulated that can be easily solved than other problems including the quadratic ones, (2) simple linear index and linear tool can be used based on the essential property of positive systems to achieve the desired control performance and (3) a general model predictive control law without sign restriction is designed. Finally, an attempt of application on mitigating viral escape is provided to verify the effectiveness of the proposed approach.     

Event‐triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach

This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject to input saturation. A new probabilistic solution framework for robust control analysis and synthesis problems is addressed by a scenario optimization approach, in which the uncertainties are not assumed to be norm bounded. Furthermore, by expressing the saturated linear feedback law on a convex hull of a group of auxiliary linear feedback laws, we establish conditions under which the closed‐loop system is probabilistic stable. Based on these conditions, the problem of designing the state feedback gains for achieving the largest size of the domain of attraction is formulated and solved as a constrained optimization problem with linear matrix inequality constraints. The results are then illustrated by a numerical example.     

Robust MPC for polytopic uncertain systems with time-varying delays

This paper addresses robust model predictive control (MPC) for time-delay systems with polytopic uncertainty. Uncertain time-varying input delay and state delays are considered, and the infinite horizon control moves are parametrised into an augmented state feedback law at each time instant. A receding horizon implementation of this state feedback law renders satisfaction of input/state constraints and closed-loop stability. For time-invariant delays and known delays, simplified results are obtained. A numerical example and a benchmark problem on continuous stirred tank reactor (CSTR) are given to illustrate the effectiveness of the proposed techniques.     

On robust state derivative feedback control for uncertain polytopic systems with uncertain sampling period and network-induced delay

This paper addresses the state derivative feedback control problem for uncertain polytopic systems subject to an uncertain sampling period and network-induced delay. The distinctive contribution relies on the direct design of a robust state derivative feedback controller employing an augmented discretized model derived in terms of the state derivative feedback such that network-induced delay and uncertain sampling periods can be incorporated from the original continuous-time state-space representation into the discretized model. Two augmented models are provided to handle longer input time delays, as well as delays less or equal to the sampling period. In this work, all the uncertain parameters are modeled as a polytopic form whose resulting discrete-time model has matrices with polynomial dependence on the uncertain parameters and an additive norm-bounded term featuring the discretization residual error. Moreover, synthesis conditions are derived using a set of linear matrix inequalities (LMI) to solve the stabilization problem for this class of systems under different input time delays. Finally, numerical simulations are carried out to evaluate the effectiveness of the proposed method.     

Dynamic Output Feedback Robust MPC Using General Polyhedral State Bounds for the Polytopic Uncertain System With Bounded Disturbance

This paper considers the dynamic output feedback robust model predictive control (MPC) for a system with both polytopic model parametric uncertainty and bounded disturbance. For this topic, the techniques for handling the unknown true state are crucial, and the strict guarantee of the input/output/state constraints favors replacing the true state by its bound in the optimization problems. The previous utilized polyhedral bounds, constructed by virtue of the error signals which are some linear combinations of the true state, the estimated state and the output, are generalized, where a bias item is utilized. Based on this unified bounding approach, new techniques for handling the unknown true state are given for both the main and the auxiliary optimization problems. As before, the main optimization problem calculates the control law parameters conditionally, and the auxiliary optimization problem determines the time to refresh these parameters. By applying the proposed method, the augmented state of the closed‐loop system is guaranteed to converge to the neighborhood of the equilibrium point. A numerical example is given to illustrate the effectiveness of the new method.     

Robust RHC for wheeled vehicles with bounded disturbances

The robust receding horizon control (RHC) synthesis approach is developed in this paper, for the simultaneous tracking and regulation problem (STRP) of wheeled vehicles with bounded disturbances. Considering the bounded disturbances, we firstly provide a robust positively invariant (RPI) set and associated feedback controller for the perturbed vehicles, which contribute to the foundation of the robust RHC synthesis approach. Then, by extending the tube‐based approach introduced in the article of Mayne et al (robust model predictive control of constrained linear systems with bounded disturbances in Automatica, 2005, vol. 41) to the STRP of wheeled vehicles, we employ the designed RPI set to determine the robust tube and terminal state region, and further construct a nominal optimal control problem. The actual control input is implemented by correcting the solved nominal input with the designed feedback controller. Following the contributed properties of the developed RPI set and extended tube‐based approach, a robust RHC algorithm is finally proposed with the guarantees of recursive feasibility and robust convergence, which can also be adapted for real‐time implementation. Additionally, due to the elaborate control design, the effect of disturbances can be completely nullified to achieve better tracking performance. The effectiveness and advantage of the proposed approach are illustrated by two simulation examples.