RISE-Based Asymptotic Prescribed Performance Tracking Control of Nonlinear Servo Mechanisms
针对伺服机构未知动态和扰动，提出一种鲁棒自适应控制，保证稳态渐近跟踪误差收敛，并通过改进预设性能函数规定瞬态响应，实验验证了有效性。
Most function approximator (e.g., neural network or fuzzy system) based control designs can only prove uniform ultimate boundedness of the controlled system due to the unavoidable approximation errors. Moreover, the transient response of conventional adaptive control may be sluggish because high-gain learning is not preferable for guaranteeing system safety. To address these issues, this paper proposes and experimentally validates an alternative robust adaptive control for servo mechanisms with unknown dynamics and bounded disturbances. This control can guarantee asymptotic tracking error convergence in the steady-state, while the transient response can also be prescribed by using an improved prescribed performance function. An echo state network augmented by a smooth friction model is used to accommodate the unknown nonlinearities. The residual approximation error and other bounded disturbances are compensated by using a robust integral of sign of the error term. Comparative experiments based on a practical turntable servo mechanism are conducted to validate the effectiveness of the proposed control scheme and show improved control performance.