Design and experimental evaluation of iterative learning controllers on a multivariable test facility

Abstract

Iterative learning control (ILC) algorithms are employed in many applications, especially these involving single-input and single-output plants undertaking repeated tasks with finite-time interval. ILC is applicable to systems executing a repeated trajectory tracking task, and uses data recorded over previous trials in the construction of the next control input. The objective is to sequentially improve tracking accuracy as the trial number increases. This method has been shown to operate well in the presence of significant modeling uncertainty and exogenous disturbances. However, for MIMO (multiple input -multiple output) systems, there exist far fewer applications reported in the literature, and minimal benchmarking and evaluation studies have been undertaken. To tackle this shortcoming, this thesis focuses on designing an electromechanical test-bed which can verify the weaknesses and the advantages of various ILC methods on a purpose-built platform. The system has two inputs and two outputs and enables variation of the interaction between inputs and outputs through simple and rapid parameter modification. This interaction variation permits the control problem to be modified, allowing stipulation over the challenge presented to the ILC controller. The system is made up of two back-to-back differential gearboxes with mass-spring damper components to increase the system order and control difficulty. In its standard configuration, two motors provide torque to the two input ports and the two outputs are measured using encoders. This work enables a comparative summary of ILC approaches for MIMO systems, together with modifications for improved performance and robustness, and the development of new control schemes incorporating input and output constraints and point-to point tracking capability. The system can also be configured in a variety of other arrangements, varying the number of inputs and outputs, and allowing noise to be injected using a dc motor. Models of the system are derived using a lumped parameter system representation, as well as purely from experimental input and output data. Simple structure controllers such as proportional-type ILC, derivative-type ILC and phase-lead ILC are then applied to test the combined performance of the controller and the MIMO system, and establish its efficacy as a benchmarking platform. Advanced controllers are then derived and applied and experimental data are used to confirm theoretical findings concerning the link between interaction and convergence rate, input norm and robustness.

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