Event-triggered-based adaptive sliding mode control for networked linear control systems

This paper investigates the robust stabilization of the adaptive sliding mode control for a class of linear systems subjected to external disturbance via event-triggered communication (ETC) scheme. First, in order to reduce the bandwidth utilization, a discrete ETC scheme is proposed and the networked sliding mode function is derived using the ETC scheme. Based on the derived sliding mode function, a reduced-order networked sliding mode dynamics with communication delay is established. Second, by constructing a Lyapunov–Krasovskii functional (LKF), asymptotic stability and stabilization criteria of the reduced-order sliding mode dynamics are given in the form of linear matrix inequalities. According to the stabilization result, a novel event-triggered-based adaptive sliding mode controller is designed while guaranteeing the reachability of the sliding surface. Finally, simulation results illustrate the effectiveness and merit of the developed method.

Neural network-based adaptive sliding mode control for underactuated dual overhead cranes suffering from matched and unmatched disturbances

To improve transportation capacity, dual overhead crane systems (DOCSs) are playing an increasingly important role in the transportation of large/heavy cargos and containers. Unfortunately, when trying to deal with the control problem, current methods fail to fully consider such factors as external disturbances, input dead zones, parameter uncertainties, and other unmodeled dynamics that DOCSs usually suffer from. As a result, dramatic degradation is caused in the control performance, which badly hinders the practical applications of DOCSs. Motivated by this fact, this paper designs a neural network-based adaptive sliding mode control (SMC) method for DOCS to solve the aforementioned issues, which achieves satisfactory control performance for both actuated and underactuated state variables, even in the presence of matched and mismatched disturbances. The asymptotic stability of the desired equilibrium point is proved with rigorous Lyapunov-based analysis. Finally, extensive hardware experimental results are collected to verify the efficiency and robustness of the proposed method.

An adaptive sliding mode fault-tolerant control for semi-active suspensions with magnetorheological dampers based on T-S fuzzy vehicle models

This paper investigates an improved adaptive sliding mode fault-tolerant control strategy for a magnetorheological semi-active suspension system with parametric uncertainties and actuator faults. Using the experimental data collected by a quarter-vehicle test rig, an adaptive-network-based fuzzy inference system is employed to establish a learning-based magnetorheological damper model firstly. The Takagi-Sugeno fuzzy approach is introduced to deal with the uncertainties of sprung mass and pitch rotary inertia and then the corresponding Takagi-Sugeno faulty semi-active suspension system is constructed. An adaptive sliding mode fault-tolerant controller is proposed, in which the magnetorheological damper fault gain is observed by the designed estimation law, and the asymptotical stability of the system is further analyzed. Finally, numerical simulation tests are conducted to demonstrate the effectiveness of the designed control scheme.

Generalized regression neural network modeling based on inverse Duhem operator and adaptive sliding mode control for hysteresis in piezoelectric actuators

A dynamic generalized regression neural network model based on inverse Duhem operator is proposed to characterize the rate-dependent hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural network can only model the system with one-to-one mapping. An inverse Duhem operator is proposed to extract the dynamic property of the hysteresis. Moreover, it can transform the multi-valued mapping of the hysteresis into a one-to-one mapping to suit the input of neural network. In order to compensate the effect of the hysteresis in piezoelectric actuator, the adaptive sliding mode controller with a feedforward hysteresis compensator is developed for the tracking control of the piezoelectric actuator. Experimental results demonstrate superior tracking performance, which validate the practicability and effectiveness of the presented approach.