Dynamic output‐feedback fuzzy MPC for Takagi‐Sugeno fuzzy systems under event‐triggering–based try‐once‐discard protocol

Abstract

Summary The fuzzy model predictive control (FMPC) problem is studied for a class of discrete‐time Takagi‐Sugeno (T‐S) fuzzy systems with hard constraints. In order to improve the network utilization as well as reduce the transmission burden and avoid data collisions, a novel event‐triggering–based try‐once‐discard (TOD) protocol is developed for networks between sensors and the controller. Moreover, due to practical difficulties in obtaining measurements, the dynamic output‐feedback method is introduced to replace the traditional state feedback method for addressing the FMPC problem. Our aim is to design a series of controllers in the framework of dynamic output‐feedback FMPC for T‐S fuzzy systems so as to find a good balance between the system performance and the time efficiency. Considering nonlinearities in the context of the T‐S fuzzy model, a “min‐max” strategy is put forward to formulate an online optimization problem over the infinite‐time horizon. Then, in light of the Lyapunov‐like function approach that fully involves the properties of the T‐S fuzzy model and the proposed protocol, sufficient conditions are derived to guarantee the input‐to‐state stability of the underlying system. In order to handle the side effects of the proposed event‐triggering–based TOD protocol, its impacts are fully taken into consideration by virtue of the S‐procedure technique and the quadratic boundedness methodology. Furthermore, a certain upper bound of the objective is provided to construct an auxiliary online problem for the solvability, and the corresponding algorithm is given to find the desired controllers. Finally, two numerical examples are used to demonstrate the validity of proposed methods.

Robust model predictive control for multirate systems with model uncertainties and circular scheduling

“…Proof This process of proof can be obtained by taking the similar line with Reference 7; thus, it is omitted.…”

“…Nevertheless, from the perspective of the practical engineering, model uncertainties are often encountered due mainly to the circumstance influence and the apparatus malfunction, thus the aforementioned traditional MPC algorithms exclusively for nominal systems might be invalid under the consideration of uncertainties. Hence, in order to cope with the negative effect of parameter‐uncertainties on system performance, the robust MPC (RMPC) 5 was proposed and has been increasingly becoming an attractive topic, 6,7 whose main method is to formulate a worst‐case optimization so as to guarantee the system robustness against parameter‐uncertainties. Using this method, the RMPC problem has been discussed for a class of discrete‐time Takagi‐Sugeno fuzzy systems 8 with structured uncertainties, for the switched linear systems 9 and for a group of systems 10 with saturated inputs.…”

Protocol‐based collaborative design for discrete‐time switched systems with sojourn probabilities

“…In what follows, in order to deal with the couple term (28), the following inequality with an adjustable scalar > 0 is introduced,…”

“…Therefore, some communication protocols are considered in networked controlled systems so as to improve the reliability of the data information and mitigate the communication burden. The representative protocols include the round‐Robin (RR) protocol, 27 the TOD protocol, 28‐30 the stochastic communication protocol, 31,32 and the event‐triggered protocol 33‐35 . To be specific, the TOD protocol is that only one sensor node could be granted the access right to transmit information according to certain prescribed principles.…”

Secure sliding mode control of interval type‐2 fuzzy systems against intermittent denial‐of‐service attacks

“…Its key feature is the strong robustness to parameter uncertainties and external disturbances. More recently, the SMC technique has been successfully utilized to Markov jumping systems, 14 networked control systems, 15 fuzzy systems, 16 and so on.…”