ROBUST NETWORKED OUTPUT FEEDBACK PREDICTIVE CONTROLLER DESIGN

Robust Output Feedback MPC for Linear Systems via Interpolation Technique

In this paper, a synthesis of model predictive control (MPC) algorithm is presented for uncertain systems subject to structured time‐varying uncertainties and actuator saturation. The system matrices are not exactly known, but are affine functions of a time varying parameter vector. To deal with the nonlinear actuator saturation, a saturated linear feedback control law is expressed into a convex hull of a group of auxiliary linear feedback laws. At each time instant, a state feedback law is designed to ensure the robust stability of the closed‐loop system. The robust MPC controller design problem is formulated into solving a minimization problem of a worst‐case performance index with respect to model uncertainties. The design of controller is then cast into solving a feasibility of linear matrix inequality (LMI) optimization problem. Then, the result is further extended to saturation dependent robust MPC approach by introducing additional variables. A saturation dependent quadratic function is used to reduce the conservatism of controller design. To show the effectiveness, the proposed robust MPC algorithms are applied to a continuous‐time stirred tank reactor (CSTR) process.

This paper presents a self-triggered model predictive control (MPC) algorithm for constrained linear discrete-time systems in the presence of bounded state and output disturbances, when only the system output can be measured at triggering instants. The proposed algorithm mainly relies on a state estimator whose estimation error is bounded by an invariant set, and on the self-triggered robust model predictive control of a nominal system, in which the cost function penalizes the nominal system state and control effort as well as the triggering interval. The proposed algorithm is proved to drive the system to an invariant set with respect to the disturbances. Numerical examples are also provided to validate the proposed algorithm and to further illustrate better performance compared with the periodic MPC.

This paper presents dynamic output feedback model predictive control (DOFMPC) for nonlinear systems represented by a Hammerstein–Wiener model. Compared with a previous work (IET-OFMPC: output feedback model predictive control for nonlinear systems represented by Hammerstein–Wiener model. IET Control Theory & Applications, 2007, 1 (5) pp. 1302–1310), this paper uses the notion of quadratic boundedness to specify the closed-loop stability and guarantees the recursive feasibility of the optimization problems. By optimizing all the parameters of the dynamic output feedback law within a single optimization problem, the computational burden is very huge. Hence, an alternative formulation is also proposed with much lower on-line computational burden. Numerical examples are given to illustrate the effectiveness of the controllers.

Frequency Domain Tuning Method for Unconstrained Linear Output Feedback Model Predictive Control

This paper addresses the problem of robust output feedback controller design with guaranteed cost and multiple quadratic Lyapunov function for linear switched systems in the continuous-time domain. The approach followed in this paper looks at the existence of switched quadratic Lyapunov function to check asymptotic stability of the switched system under consideration. Two different linear matrix inequality based conditions allow checking the existence of a family of quadratic Lyapunov functions. Then, a switched robust output feedback design method is established. The proposed method is illustrated on numerical example.

This paper proposes a robust output feedback model predictive control (MPC) framework for constrained linear networked control systems (NCSs) subject to random packet dropouts and external disturbances. The proposed output feedback MPC scheme consists of a state observer that accommodates the random measurement loss and a state feedback model predictive controller that stabilizes the perturbed system. According to the proposed observer, the estimation error dynamics can be represented by a switched system. By developing a generalized robust positive invariant (GRPI) set under the switched system formulation, the estimation error can be confined in this invariant set, which serves as the explicit error bound of state estimation. Then, the GRPI set is utilized to tighten the state and input constraints in the MPC optimization problem to alleviate the effects of random packet dropouts and disturbances. As a result, the system can be stabilized by the proposed output feedback model predictive controller while both state and input constraints are fulfilled. Simulation results are provided to validate the effectiveness of the proposed method.

This article addresses the problem of robust output feedback model predictive control for discrete-time, constrained, linear parameter-varying systems subject to (bounded) state and measurement disturbances. The vector of scheduling parameters is assumed to be an unmeasurable signal taking values in a given compact set. The proposed controller incorporates an interval observer, that uses the available measurement to update the set-membership estimation of the states, and an interval predictor, used in the prediction step of the model predictive control (MPC) algorithm. The resulting MPC scheme offers guarantees on recursive feasibility, constraint satisfaction, and input-to-state stability in the terminal set. Furthermore, this novel algorithm shows low computation complexity and ease of implementation (similar to conventional MPC schemes).

SummaryIn this paper, a distributed output feedback model predictive control (OFMPC) algorithm is presented for the polytopic uncertain system subject to randomly occurring actuator saturation and packet loss. Compared with the intensively applied state feedback control in MPC, the OFMPC is more feasible to the real world because the system states are often unmeasurable. With taking both actuator saturation and packet loss into account, the presented OFMPC algorithm is more practical. Moreover, by splitting the controller inputs into two independent parts, the presented dynamic output feedback control (DOFC) strategy provides more freedom to the controller design. With the global system decomposed into some subsystems, the computation complexity is reduced, thus the online designing time can be saved. By defining the estimation error function and forming an augmented system to handle the DOFC and by transforming the nonlinear feedback law into a convex hull of linear feedback laws, the distributed controllers are obtained by solving a linear matrix inequality (LMI) optimization problem. Finally, some simulation examples are employed to show the effectiveness of the techniques proposed in this paper. Copyright © 2015 John Wiley & Sons, Ltd.

This paper studies the synthesis approach of dynamic output feedback model predictive control (OFMPC) for nonlinear networked control systems (NCSs), which can be described by interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model. The Bernoulli random binary distribution is exploited to describe the process of packet loss in NCSs. Compared with the existed output feedback model predictive controller based on traditional state observer, the parameter dependent dynamic OFMPC which minimizes the upper bound of a quadratic objective function in an infinite horizon is designed by applying the notion of quadratic boundedness. A numerical example is given to demonstrate the effectiveness of the proposed new design techniques.

A formulation of the coupled modeling and robust control design problem is presented. The distinguishing feature is the treatment of the control design model as a degree of design freedom. A simple example highlights the potential improvement in closed-loop performance under the coupled approach. Quantification of the strength of the coupling in terms of performance boundaries is also described. >

This article focuses on establishing a general robust actor‐critic online learning control structure for disturbed nonlinear continuous systems with input constraints. It enriches the existing studies for the robustness of input constraint systems. First, the problem of robust controller design is successfully transformed into optimal controller design, and this process is proven, in which a particular nonquadratic discount cost function is defined. Then, build two neural networks (NNs) to estimate the cost function together and update each other. In the update process of actor NN, a robust term related to the state is introduced, which can guarantee the system's stability during the online learning process, and the state information is more fully utilized. Furthermore, using Lyapunov's direct method, it is proved that the estimated weights of the closed‐loop optimal control system and the actor‐critic NNs are uniformly ultimately bounded (UUB). It also provides extended discussions and a simulation example to demonstrate the robustness verification results of the novel algorithm.

This article develops an adaptive finite‐time stable control algorithm for an accurate manipulation of robot arms considering unknown model parameters and external disturbances. First, a state‐dependent twisting control algorithm is developed for the robust manipulation of robot arms under the consideration of bounded unknown uncertainties conditions. The more challenging problem of robust control design under consideration of totally unknown dynamical parameters is investigated in a second stage by supporting the control law with a suitable online parameters estimator. The objective of the parameters estimator is to drive the vector of estimated parameters to some level that ensures the establishment of real second‐order sliding mode without overloading the discontinuous controller gains. This approach based on combining both techniques of robust and adaptive algorithms allows overcoming large levels of uncertainties while preserving a high level of tracking accuracy without exciting the unwanted phenomena of chattering. The system finite‐time stability is proven by using Lyapunov theory and the weighted homogeneity principle. Practical tests are reported to support theoretical study results.

Robust control for nonlinear systems with structured [formula omitted]2-gain bounded uncertainty

This paper is concerned with the problem of robust controller design for electro-hydraulic servo systems driven by double-rod hydraulic actuators. Firstly, by introducing a linear varying parameter, an electro hydraulic servo system is described as a linear parameter varying(LPV) model. Secondly, based on this LPV model and a parameter-dependent Lyapunov approach, two parameter-dependent controllers are established for the stabilization and tracking problems, respectively. The obtained controllers are simple and their gains can vary with the parameter measured online. Finally, the numerical simulations demonstrate the satisfactory performance of the proposed controllers.