This paper considers security control under the network environment for system with deception attacks, polytopic uncertainty and persistent bounded disturbance. Since the state is immeasurable, and the physical constraint is considered, the output feedback robust model predictive control (MPC) is utilized. The previous results on the output feedback robust MPC are extended to address the deception attacks. The mean-square quadratic boundedness (MSQB) is defined in order to characterize the closed-loop stability. An optimization problem is proposed, which can be solved by linear matrix inequality techniques. By a simple refreshment of the state estimation error set (EES), the optimization problem is shown to be recursively feasible, and the augmented state converges to the neighborhood of equilibrium point. The effectiveness of the proposed theoretical approach is demonstrated by a numerical simulation example.

For the tracking and formation problem of multi-agent systems with collision avoidance, a synchronous distributed model predictive control (DMPC) algorithm is proposed. We consider the deterministic, linear, time-invariant, and homogeneous dynamics for all agents. In the synchronous DMPC, all the agents solve their optimisation problems synchronously, taking advantage of their neighbours’ assumed predictive information, to obtain the current optimal inputs. Considering the uncertain deviation existing between the assumed and actual predictive information of each agent, we contribute to design a deviation-dependent collision avoidance constraint, which is imposed in the individual optimisation problem to guarantee the safety of each agent. We constrain the uncertain deviation by designing a time-varying compatibility constraint in the two-norm form, which is imposed in the individual optimisation problem to play an important role in both the collision avoidance and exponential stability. By applying the proposed algorithm, the guarantees for the recursive feasibility, exponential stability and collision avoidance are all proved. A simulation example is provided to illustrate the practicability and effectiveness of this approach.

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.

The output feedback robust model predictive control (MPC), for the linear parameter varying (LPV) system with norm-bounded disturbance, is addressed, where the model parametric matrices are only known to be bounded within a polytope. The previous techniques of norm-bounding technique, quadratic boundedness (QB), dynamic output feedback, and ellipsoid (true-state bound; TSB) refreshment formula for guaranteeing recursive feasibility, are fused into the newly proposed approaches. In the notion of QB, the full Lyapunov matrix is applied for the first time in this context. The single-step dynamic output feedback robust MPC, where the infinite-horizon control moves are parameterised as a dynamic output feedback law, is the main topic of this paper, while the multi-step method is also suggested. In order to strictly guarantee the physical constraints, the outer bound of the true state replaces the true state itself, so tightness of this bound has a major effect on the control performance. In order to tighten the TSB, a procedure for refreshing the real-time ellipsoid based on that of the last sampling instant is given. This paper is conclusive for the past results and far-reaching for the future researches. Two benchmark examples are given to show the effectiveness of the novel results.