Event-Triggered Adaptive Output Constraint Tracking Control of Uncertain MIMO Nonlinear Systems With Sensor and Actuator Faults

This paper develops an event-triggered fault-tolerant tracking strategy for block-triangular MIMO uncertain nonlinear systems with sensors and actuators polluting by multiplicative/additive faults and unknown control directions, to address the time-varying asymmetric output constraint control. By combining an adaptive fault-tolerant control law with event-triggered mechanisms (ETMs), the presented method possesses the properties of low structure and calculation complexity, and can effectively conserve the system resources of communication and computation. To solve the issues of unknown control directions and time-varying asymmetric output constraints, the proposed method utilizes the integrated design of the Nussbaum function and barrier Lyapunov function (BLF) to realize a novel constrained tracking control with strong robustness, and can eliminate the adverse effects of the output tracking caused by all state (except for output) sensor faults. The proposed controller operates without having to use any approximating techniques, has the capability to handle the coupling uncertain terms derived from unknown system functions, sensor and actuator faults, and ETMs, and avoids the issue of explosion of complexity as in traditional backstepping procedure. The closed-loop stability can be guaranteed based on Lyapunov stability analysis with contradiction, while ensuring the boundedness of all signals, maintaining the output constraint, and preventing the Zeno behavior. Finally, the potential of application is investigated by means of experiments on a Linear Motor system, illustrating the effectiveness. Note to Practitioners— Due to the existence of output and bandwidth constraints, sensor and actuator faults, and unknown system models in practical plants, the operational performance of the system may inevitably degraded. To address this issue, this study primarily focuses on developing a low-complexity adaptive control method that aim to guarantee the overall performance of the system. Existing approaches mainly rely on system dynamic functions or adopt the approximation technology, our methodology can only utilizes the state signals of the system, which possesses advantages of low-complexity design and easy implementation. Preliminary experiments conducted through physical experiments demonstrate the applicability of this method to practical linear-motor platforms, yielding satisfactory control performance.