Lei Liu

TL;DR: It is proved that under the presented control strategy, the system output tracks the reference signal in the sense of finite-time stability, the first time to handle the fault tolerant problem for switched system while the finite- time stability is also necessary.

TL;DR: The Lyapunov stability theory is introduced to demonstrate that the adaptive controller achieves the desired control goals and a numerical simulation is performed which verifies the significance and feasibility of the presented control scheme.

TL;DR: A framework of adaptive control for a switched nonlinear system with multiple prescribed performance bounds is established using an improved dwell time technique and all signals appearing in the closed-loop system are bounded.

TL;DR: An adaptive neural network (NN) control scheme is proposed for a quarter-car model, which is the active suspension system (ASS) with the time-varying vertical displacement and speed constraints and unknown mass of car body and it can prove the stability of the closed-loop system.

TL;DR: A reinforcement learning-based adaptive tracking control technique to tolerate faults for a class of unknown multiple-input multiple-output nonlinear discrete-time systems with less learning parameters can reduce the cost in the procedure of tolerating fault and can decrease the number of learning parameters and thus reduce the computational burden.