Frequency domain synthesis of trajectory learning controllers for robot manipulators

Trajectory learning control is a method for generating near to optimal feedforward control for systems that are controlled along a reference trajectory in repeated cycles. Iterative refinements of a stored feedforward control sequence corresponding to one cycle of the control trajectory is computed based upon the recorded trajectory error from the previous cycle. Several learning operators have been proposed in earlier work, and convergence proofs are developed for certain classes of systems, but no satisfactory method for design and analysis of learning operators under the presence of uncertainties in the system model have been presented. This article presents frequency domain methods for analyzing the convergence properties and performance of the learning controller when the amplitude and phase of the system transfer function is assumed to be within specified windows. Experimental results with an industrial robot manipulator confirm the theoretical results.

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