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Title: On the adaptive and learning control design for systems with repetitiveness
Keywords: iterative learning control, adaptive control, repetitiveness, convergence
Issue Date: 28-Jul-2010
Citation: HUANG DEQING (2010-07-28). On the adaptive and learning control design for systems with repetitiveness. ScholarBank@NUS Repository.
Abstract: The control of dynamic systems in the presence of all kinds of repetitiveness is of great interest and a challenge. Repetitiveness lied in systems includes the repetitiveness of system uncertainties, the repetitiveness of control processes, and the repetitiveness of control objectives, etc, either in the time domain or in the spatial domain. In this thesis, the attention is concentrated on the analysis and design of learning-type control strategies for dynamic systems with repetitiveness. In the first part of the thesis, adaptive control (AC) is considered for systems with periodic parametric repetitiveness. Specifically, a new spatial periodic control approach is proposed to deal with nonlinear rotary machine systems with a class of state-varying parametric repetitiveness. The new adaptive controller updates the parameters and the control signal periodically in a point-wise manner over one entire period along the position axis, in the sequel achieves the asymptotic tracking convergence. Subsequently, a concise AC approach is further presented to deal with parametric repetitiveness in discrete-time systems. The underlying idea is to convert the periodic parameters into an augmented constant parametric vector by a lifting technique. As such, the well-established discrete-time AC schemes can be easily applied to various control problems with periodic parameters. The second part of the thesis is tried to extend iterative learning control (ILC) design for systems having repetitive characteristics. Firstly, an initial state ILC approach is proposed for final state control of motion systems. ILC is applied to learn the desired initial states in the presence of system uncertainties; Secondly, a dual-loop ILC scheme is designed for a class of nonlinear systems with hysteresis input uncertainty. The two ILC loops are applied to the nominal part and the hysteresis part respectively, to learn their unknown dynamics. Thirdly, ILC scheme is further developed for a class of nonlinear partial differential equation processes with parametric/non-parametric uncertainties, which iteratively tunes the velocity boundary condition on one side such that the boundary output on the other side can be regulated to a desired level. At last, an optimal tuning method for PID controller is proposed by means of iterative learning, where the time domain performance or requirements are incorporated directly into the objective function to be minimized. Overall, the control process and the system uncertainties are repetitive in the above systems. Our research reveals that ILC is highly efficient to these kinds of repetitiveness.
Appears in Collections:Ph.D Theses (Open)

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