This paper investigates the output feedback model predictive control (OFMPC) for Takagi-Sugeno fuzzy networked control systems with bounded disturbance, where data quantization and data loss occur simultaneously. The quantization error is treated as sector bound uncertainties by using the sector bound approach and the data loss process is modeled as a time-homogeneous Markov chain. Invoking S-procedure and the notion of quadratic boundedness which can specify closed-loop stability for system with disturbance, the state observer is offline designed and the networked output feedback model predictive controller is provided which explicitly considers the satisfaction of input constraints. Two online synthesis algorithms of OFMPC are presented, one parameterizing the infinite horizon control moves into a single feedback law, the other into one free control move followed by the single feedback law based on the state observer. A new formula is introduced to refresh the ellipsoidal bound of estimation error which can guarantee the recursive feasibility of optimization problem. An example is given to demonstrate the effectiveness of the proposed new design techniques.

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.

In this paper, the problem of event-triggered dynamic output feedback model predictive control (OFMPC) for nonlinear networked control systems (NCSs) with packet loss and bounded disturbance is studied. Interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model is exploited to represent the nonlinear plant with parameter uncertainties which can be captured by the lower and upper membership functions. Whether the measured output should be released into unreliable network links or not is determined by the error between the current measured output and the latest event-triggered output. The Bernoulli random binary distribution is used to describe the process of packet loss in NCSs. The synthesis approach, including the design of the parameter dependent dynamic output feedback controller by solving an online MPC optimization problem which minimizes the upper bound of an infinite time horizon quadratic objective function respecting input and state constraints, and the guarantee of recursive feasibility and quadratical stability of the closed-loop system by applying the quadratic boundedness (QB) technique, is proposed. Moreover, an algorithm of tightening the ellipsoidal bounds of state error is added to improve the control performance. The simulation and comparison studies are performed to demonstrate the usefulness and availability of the presented new techniques.

This paper investigates the adaptive event-triggered (AET) output feedback model predictive control (MPC) method for the load frequency control (LFC) problem of hybrid power system involving the thermal power and the photovoltaic power. First, an AET communication mechanism, which possesses the ability to dynamically adjust the trigger threshold parameter according to an adaptive law, is addressed to reducing the communication overhead, while keeping the required control performance; Second, the output feedback model predictive controller which is composed of an off-line designed state observer and an on-line solved optimization problem, is proposed using the techniques of quadratic boundedness and S-procedure. The state observer gain is obtained by off-line maximizing the trace of a matrix which is closely related to minimal positively invariant set; While the output feedback MPC strategy is acquired by expressing the infinite horizon control moves as a free control move and a linear feedback law based on the estimated state; Third, the recursive feasibility of the on-line optimization problem and the stability of the closed-loop system are analyzed by the fact that the estimated state and estimation error will converge to the presented minimal positively invariant set. Finally, MATLAB and its linear matrix inequality (LMI) toolbox are used to solve the optimization problem, and the LFC system with load disturbance is simulated and analyzed. The simulation results show that the proposed method is feasible and effective.

In order to optimize the control performance of permanent magnet synchronous motor (PMSM) servo system, an improved model predictive control (MPC) scheme based on neural network is investigated in this paper. First, the dynamic characteristics of the PMSM are approximated by echo state network (ESN) to predict the future speed. Particle swarm optimization (PSO) is used to train ESN output weights to solve the problem that instability of output weights caused by pseudo-inverse matrix in ESN weight solving algorithm, called PSO-ESN, which enhances the stability and the accuracy of ESN speed prediction. That provides future plant output for control optimization of the predictive control. Furthermore, in order to reduce the computational cost and improve the response performance of the controller, a fast gradient method (GM) is applied to minimize the quadratic performance index and solve the optimal control input sequences. The simulation results under three different working conditions show that the PSO-ESNMPC controller designed in this paper reduces the overshoot by 5.87% and the rise time by 0.036 s compared with the reference controllers and has better robustness under parameter changes and load disturbances.

For resource-constrained wireless sensor networks (WSNs), designing a lightweight intrusion detection technology has been a hot and difficult issue. In this paper, we proposed a lightweight intrusion detection method that was able to directly map the network status into sensor monitoring data received by base station, so that base station can sense the abnormal changes in the network. Our method is highlighted by the fusion of fuzzy c-means algorithm, one-class SVM, and sliding window procedure to effectively differentiate network attacks from abnormal data. Finally, the proposed method was tested on the wireless sensor network simulation software EXata and in real applications. The results showed that the intrusion detection method in this paper could effectively identify whether the abnormal data came from a network attack or just a noise. In addition, extra energy consumption can be avoided in all sensor monitoring nodes of the sensor network where our method has been deployed.