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Experimental Evaluation Of Two Robust Control Algorithms On An Industrial Manipulator

Industrial manipulators are typically controlled by proportional-derivative algorithms implemented at the individual joints. Recent theoretical results have shown that robust controllers which take into account the system dynamics can provide more accurate trajectory tracking and greater robustness to unmodeled disturbances. This research implements two such controllers - a sliding mode algorithm, and an acceleration feedback algorithm - on an industrial manipulator and evaluates their performance in comparison with a proportional-derivative algorithm implemented in the same manner. The objective of robust control designs is to make the system insensitive to disturbances and modelling uncertainties. The manipulator equations of motion are non-linear and cross-coupled, and in practice the joint actuators experience significant frictional disturbances due to high gear ratios. The robust algorithms implemented do not rely on an accurate model of the dynamics, and are computationally efficient in comparison to model-based (feedforward) controllers. Experimental results are presented for sliding-mode and acceleration feedback controllers, and these are compared with conventional proportional-derivative under the same and operating conditions. Tracking of position trajectories is shown to improve markedly when a particular sliding-mode scheme is used.