Tracking Control of Self-Restructuring Systems: A Low-Complexity Neuroadaptive PID Approach With Guaranteed Performance
研究了一类结构可变的自重构系统的跟踪控制问题，提出了一种基于神经网络的自适应PID控制器，无需模型且能保证瞬态和稳态性能，仿真验证了其有效性。
This article investigates the tracking control problem for a class of self-restructuring systems. Different from existing studies on systems with fixed structure, this work focuses on systems with varying structures, arising from, for instance, biological self-developing, unconsciously switching, or unexpected subsystem failure. As the resultant dynamic model is complicated and uncertain, any model-based control is too costly and seldom practical. Here, we explore a nonmodel-based low-complexity proportional-integral-derivative (PID) control. Unlike traditional PID with fixed gains, the proposed one is embedded with neural-network (NN)-based self-tuning adaptive gains, where the tuning strategy is analytically built upon system stability and performance specifications, such that transient behavior and steady-state performance are ensured. Both square and nonsquare systems are addressed by using the matrix decomposition technique. The benefits and feasibility of the proposed control method are also validated and confirmed by the simulations.