Memory State Feedback Control for Time-Varying Delay Switched Fuzzy Systems

Consider the problem of memoryless state feedback controller for time-delay system, which cannot consider both the memoryless and the memory items in the system. Therefore, the memoryless state feedback controller has certain limitations and is more conservative. This paper addresses the memory state feedback control for the time-varying delay switched fuzzy systems based on T-S fuzzy model to overcome the problem discussed above. The state vector and input of the time-varying delay systems contain unknown time-varying delay with known bounds. The designed controller whose parameters are solvable can introduce past state information and reduce the system conservativeness. The more general Lyapunov-Krasovskii functional is selected and the switching law is designed in order to analyze the open-loop system stability, and the memory state feedback controller is designed for the closed-loop system and the criterion for its asymptotic stability. Discuss the solvability of the above two criteria. Finally, a numerical example is given. The simulation results show that the proposed method is more feasible and effective.

Constrained H∞ control for a half-car model of an active suspension system with actuator time delay by predictor feedback

In this paper, constrained memory state-feedback H∝ control for a half-car model of an active vehicle suspension system with input time-delay in the presence of external disturbance has been investigated. Its prime goal is to improve the inherent trade-offs among power consumption, handling performance, ride quality, and suspension travel. The tire deflections and the suspension deflections are constrained by their peak response values in time domain using the generalized H2 ( GH∝) norm (energy-to-peak) performance, while the ride comfort performance of the suspension system is optimized by notion of the H∝ control (energy-to-energy) to measure the body accelerations including both the heaving and the pitching motions. Similar to the well-known prediction-based methods, the prediction vector of the system is achieved to construct the memory state-feedback controller. Using the prediction vector, sufficient conditions guaranteeing closed-loop system stability as well as disturbance attenuation are obtained as some delay-dependent linear matrix inequalities (LMIs). In addition, some LMIs are added to limit the gain of the controller. In the case of feasibility, obtained LMIs provide the stabilizing gain of the memory controller. The proposed approach is applied to a half-car model of an active suspension system considering the actuator time-delay to illustrate the effectiveness of the proposed method.

Optimal guaranteed cost control of uncertain 2-D discrete state-delayed systems described by the Roesser model via memory state feedback

This paper is concerned with the problem of optimal guaranteed cost control via memory state feedback for a class of uncertain two-dimensional (2-D) discrete state-delayed systems described by the Roesser model with norm-bounded uncertainties. A linear matrix inequality (LMI)-based sufficient condition for the existence of memory state feedback guaranteed cost controllers is established and a parameterized representation of such controllers (if they exist) is given in terms of feasible solutions to a certain LMI. Furthermore, a convex optimization problem with LMI constraints is formulated to select the optimal guaranteed cost controllers that minimize the upper bound of the closed-loop cost function. The proposed method yields better results in terms of least upper bound of the closed-loop cost function as compared with a previously reported result.