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.

Abstract-This paper extends the previous work on LPV modeling of a pan-tilt system [1] and tackles the robust stabilization problem by employing angular acceleration feedback in an LMI based optimal LQR controller. The state vector of the LPV model is augmented to include the integral of the position errors in addition to joint angles and velocities. Therefore, an extended polytopic quasi-LPV model of the pantilt system is derived. The LMI based optimal LQR controller that utilizes acceleration feedback is synthesized based on the extended LPV model. Since the time varying parameter vector is 4 dimensional, the proposed controller is synthesized by interpolating LMIs at 16 vertices of the polytope. A cascaded nonlinear high gain observer is also designed to estimate reliable positions, velocities and accelerations from noisy encoder measurements. Simulation results show that the proposed LMI based optimal LQR controller outperforms the classical LMI based LQR controller.

Abstract-A new acceleration based learning control approach is developed to tackle the robust periodic trajectory tracking problem for robot manipulators. The acceleration feedback is incorporated into the learning feedforward term to provide high stiffness to the system against unknown periodic dynamics with a known period. 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 learning controller achieves global asymptotic position tracking. Simulation results obtained from a high fidelity model show that the proposed controller outperforms the learning controller that does not utilize the acceleration feedback.