Enhanced Generalised Predictive Control with Disturbance Compensation
Generalised Predictive Control (GPC) is an optimal model-based control algorithm that uses an explicit dynamic plant model to predict and optimise the future response of the plant. This thesis investigates the application of GPC in the tracking control of a two-link rigid robotic manipulator. Simula...
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| Format: | Thesis |
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2014
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| Summary: | Generalised Predictive Control (GPC) is an optimal model-based control algorithm that uses an explicit dynamic plant model to predict and optimise the future response of the plant. This thesis investigates the application of GPC in the tracking control of a two-link rigid robotic manipulator. Simulation results reveal that unconstrained GPC attains one order of magnitude improvement in terms of mean square trajectory tracking errors (MSE) as compared to a conventional proportional-derivative (PD) controller. Through constrained optimisation procedure, GPC keeps the control actions within some predefined realisable range while providing satisfactory tracking performance. Motivated by the fact that most industrial robots are required to perform repetitive tasks, the applicability of GPC is extended to a repetitive setting. Exploiting the repetitive nature of the disturbances acting on the robot system, disturbance compensation schemes based on iterative learning control (ILC) are integrated into the GPC framework. Two control strategies, i.e., constrained GPC with learning-based disturbance compensator (CGPC-ILC), and GPC with a robust learning mechanism based on a least mean square error estimator (GPCLMSE), are proposed to provide bounded solutions. While complying with input constraint, CGPC-ILC suffers slight performance degradation when the input constraint becomes active. GPC-LMSE demonstrates superior tracking performance while maintaining smooth and bounded control actions. In term of MSE, marked improvement rates of 55.8% with CGPC-ILC and 65.2% with GPC-LMSE over nominal GPC are acquired. |
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