An acceleration-based hybrid learning-adaptive controller for robot manipulators

Abstract

The robust periodic trajectory tracking problem is tackled by employing acceleration feedback in a hybrid learning-adaptive controller for n-rigid link robotic manipulators subject to parameter uncertainties and unknown periodic dynamics with a known period. Learning and adaptive feedforward terms are designed to compensate for periodic and aperiodic disturbances. The acceleration feedback is incorporated into both learning and adaptive controllers to provide higher stiffness to the system against unknown periodic disturbances and robustness to parameter uncertainties. A cascaded high gain observer is used to obtain reliable position, velocity and acceleration signals from noisy encoder measurements. A closed-loop stability proof is provided where it is shown that all system signals remain bounded and the proposed hybrid controller achieves global asymptotic position tracking. Results obtained from a high fidelity simulation model demonstrate the validity and effectiveness of the developed hybrid controller.

Forward and Inverse Dynamics of a Six-Axis Accelerometer Based on a Parallel Mechanism

“…The robustness of the control system can be improved by introducing acceleration feedback in the robot control system [ 1 ]. The motion of an object in space is generally a six-degree-of-freedom motion.…”

Observer-based robust control for flexible-joint robot manipulators: A state-dependent Riccati equation-based approach

“…Controller design for rigid robot manipulators has been well understood and numerous researches related to this problem have been addressed – for example: simple robust nonlinear control using a modified Leitmann approach (Spong, 1992); absolutely continuous robust controllers based on a new non-singular terminal sliding mode manifold (Galicki, 2015); separation approach to adaptive control with uncertain kinematics and dynamics (Wang, 2017); modified nonlinear output feedback PD plus gravity using a simple nonlinear filter (Wang and Su, 2017); decentralized robust control using an extended high gain observer (Sun et al, 2019); robust control using the novel Zhang dynamics method (Li and Huang, 2020); RBF-neural network-based adaptive robust controller design to cope with communication time- delay and uncertainties (Chen et al, 2019); acceleration-based hybrid learning-adaptive controller for robust periodic trajectory tracking utilizing a cascaded high gain observer (Evren Han and Unel, 2018); finite horizon linear and nonlinear optimal control with a fast numerical solution( Nekoo and Irani Rahaghi, 2018). However, joint flexibility should be considered in both modeling and control if a high performance needs to be achieved (Chang and Yen, 2012; Spong and Vidyasagar, 1989; Spong et al, 1987).…”