On Designing the Event-Triggered Multistep Model Predictive Control for Nonlinear System Over Networks With Packet Dropouts and Cyber Attacks.

In this article, the event-triggered multistep model predictive control for the discrete-time nonlinear system over communication networks under the influence of packet dropouts and cyber attacks is studied. First, the interval type-2 Takagi-Sugeno fuzzy model is applied to express the discrete-time nonlinear system and an event-triggered mode, which is capable of determining whether the sampled signal ought to be delivered into the unreliable network, is designed to economize communication resources. Second, two Bernoulli processes are introduced to represent the randomly happening packet dropouts in the unreliable network and the randomly occurring deception attacks on the actuator side from the adversaries. Third, under the assumption that the system states are unmeasurable, a multistep parameter-dependent model predictive controller is synthesized via optimizing one series of feedback laws for a given period of time, which leads to improved control performance than that of the one-step approach. Moreover, the results on the recursive feasibility and closed-loop stability related to the networked system are achieved, which explicitly consider the external disturbance and input constraint. Finally, simulation experiments on the mass-spring-damping system are carried out to illustrate the rationality and effectiveness of the provided control strategy.

Tube-Based Output Feedback Robust MPC for LPV Systems With Scaled Terminal Constraint Sets.

This article provides a solution to tube-based output feedback robust model predictive control (RMPC) for discrete-time linear parameter varying (LPV) systems with bounded disturbances and noises. The proposed approach synthesizes an offline optimization problem to design a look-up table and an online tube-based output feedback RMPC with tightened constraints and scaled terminal constraint sets. In the offline optimization problem, a sequence of nested robust positively invariant (RPI) sets and robust control invariant (RCI) sets, respectively, for estimation errors and control errors is optimized and stored in the look-up table. In the online optimization problem, real-time control parameters are searched based on the bounds of time-varying estimation error sets. Considering the characteristics of the uncertain scheduling parameter in LPV systems, the online tube-based output feedback RMPC scheme adopts one-step nominal system prediction with scaled terminal constraint sets. The formulated simple and efficient online optimization problem with fewer decision variables and constraints has a lower online computational burden. Recursive feasibility of the optimization problem and robust stability of the controlled LPV system are guaranteed by ensuring that the nominal system converges to the terminal constraint set, and uncertain state trajectories are constrained within robust tubes with the center of the nominal system. A numerical example is given to verify the approach.

Multivariable offset-free MPC with steady-state target calculation and its application to a wind tunnel system.

This paper addresses a computationally efficient MPC approach for set-point optimization and its application to an intermittent transonic wind tunnel (ITWT). In the presented method, the open-loop prediction, which adopts the Kalman filter to obtain the open-loop dynamic/steady state predictions of manipulated/controlled variables (MVs/CVs), is presented. Based on the open-loop prediction, a linearization steady-state model featured by the total pressure and the Mach number is used for set-points optimization in steady-state target calculation (SSTC), being formulated as a linear programming (LP) problem. Based on these set-points of MVs/CVs, the dynamic control computes the optimal control moves by solving a quadratic programming (QP) problem. The effectiveness of the proposed method is illustrated on ITWT, and satisfactory performances are obtained.

Event-triggered distributed receding horizon control for tracking and formation of homogeneous multi-agent systems.

This paper proposes an event-triggered distributed receding horizon control (DRHC) approach for the formation and tracking problems of homogeneous multi-agent systems. For each agent, an event-triggering condition, based on assumed predictive information of the neighbours, is derived from stability analysis. Considering the uncertain deviation between the assumed and true predictive information, we design a time-varying compatibility constraint for the individual optimization problem. In the event-triggered DRHC algorithm, each agent solves the optimization problem and communicates with its neighbours only when the event-triggering condition is satisfied, so the communication and computation burden are reduced. Moreover, guarantees for the recursive feasibility and asymptotic stability of the overall system are proved. A simulation example is provided to illustrate effectiveness of the proposed approach.

Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics.

This paper considers the distributed model predictive control (MPC) of nonlinear large-scale systems with dynamically decoupled subsystems. According to the coupled state in the overall cost function of centralized MPC, the neighbors are confirmed and fixed for each subsystem, and the overall objective function is disassembled into each local optimization. In order to guarantee the closed-loop stability of distributed MPC algorithm, the overall compatibility constraint for centralized MPC algorithm is decomposed into each local controller. The communication between each subsystem and its neighbors is relatively low, only the current states before optimization and the optimized input variables after optimization are being transferred. For each local controller, the quasi-infinite horizon MPC algorithm is adopted, and the global closed-loop system is proven to be exponentially stable.

Design and analysis of constrained nonlinear quadratic regulator.

A suboptimal dual-mode solution to constrained nonlinear quadratic regulator (CNLQR) problem is studied. In a neighborhood of the origin, the controller is formulated as an LQR based on a model obtained by linearizing the original model at the origin. Outside this neighborhood, the control law is obtained by solving a finite horizon optimization problem (FHOP) with additional terminal inequality constraints. The terminal inequality constraints make the terminal states of FHOP be driven into the neighborhood of the origin, which is a specially designed control invariant set with respect to LQR control law. The overall control law is obtained by combining that obtained by solving FHOP and that obtained form LQR. The feasibility aspect is analyzed and asymptotic stability is proven. The effectiveness of this suboptimal controller is demonstrated by simulation studies.