Time-Optimal Constrained Adaptive Robust Control of Single-DOF Mechanical Systems: A Comparative Study with BLF-based Methods

The high-accuracy tracking and time-optimal response of mechanical systems are very important for modern manufacturing. However, the inevitable parametric uncertainties, disturbances, and constraints make achieving these high performances difficult. In this paper, a two-loop control algorithm combining online planning and adaptive robust control (ARC) is proposed for the 1-DOF mechanical systems to improve tracking accuracy and efficiency. In the outer loop, a time-optimal reference trajectory starting from the initial state of the plant is planned without violating the constraints. In the inner loop, the ARC law is synthesized to track the planned trajectory with high accuracy in the presence of parametric uncertainties and disturbances. The proposed method has a simpler inner-loop design compared with the existing work, and achieves the prescribed convergence of the tracking error. The widely used barrier Lyapunov function (BLF) based methods are compared with the proposed method in this paper. The experimental results verify the superiority of the proposed method in achieving fast response and high tracking accuracy. The limitations of the feasibility conditions of BLF on the tracking performance are analyzed.