Abstract
This article investigates the tracking problem of an uncertain <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-link robot manipulator with guaranteed safety and performance. To tackle parametric uncertainties, the torque filtering-augmented concurrent learning (CL) method is introduced for online identification of the unknown system without requirements of joints acceleration. By using CL, the parameter convergence is guaranteed by exploiting the current and historical data simultaneously. This technique enjoys practicability compared with common methods that need to incorporate external noises to satisfy the persistence of excitation condition for the parameter convergence. Based on the estimated model, we design a barrier Lyapunov function (BLF)-based adaptive control law by the backstepping technique and Lyapunov analysis. By ensuring the boundness of the BLF, the system output and the tracking error are proved to lie in the safety set and performance set, respectively. Numerical simulation results and experiment tests validate the proposed strategy.
Highlights
S AFETY issues due to physical human–robot interactions and parametric uncertainties caused by environmental influences are inevitable in robotic systems
The torque filtering technique is integrated into concurrent learning (CL) to get rid of joints acceleration for the parameter estimation, and the guaranteed parameter convergence to the actual value is achieved for the function approximation-based barrier Lyapunov function (BLF) control strategy
Remark 2: From the perspective of the guaranteed performance represented by (8), prescribed performance control (PPC) [48] is closely related to our work, which exploits the prescribed performance function (PPF)-based system transformation technique to guarantee that the tracking error converges to an explicit residual set, the convergence rate is no less than a predefined value, and a maximum overshoot is less than a prespecified constant
Summary
S AFETY issues due to physical human–robot interactions and parametric uncertainties caused by environmental influences are inevitable in robotic systems. For given tasks in real applications, uncertain robot manipulators need to work in consideration of requirements for both safety and performance. Control strategies that partially focus on performance without safety guarantee [1] often result in a lack of practicability and vice verse [2]. This motivates us to develop an effective control strategy for a robot manipulator such that safety, performance, and uncertainty could be considered together
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