Font Size: a A A

Adaptive inverse control of plants with disturbances

Posted on:1999-10-09Degree:Ph.DType:Thesis
University:Stanford UniversityCandidate:Plett, Gregory LFull Text:PDF
The goal of control theory is to make a given dynamical system (the "plant") behave in a user-specified manner as accurately as possible. This objective may be broken down into three separate tasks: stabilization of the plant dynamics; control of plant dynamics; and control of plant disturbance. Conventionally, one uses feedback to treat all three problems simultaneously. Compromises are necessary to achieve good solutions.; Adaptive inverse control is a method to treat the three control tasks separately. First, the plant is stabilized; secondly, the plant is controlled using a feedforward controller; thirdly, a disturbance canceller is used to reject plant disturbances. Adaptive filters are used as controller and disturbance canceller, and algorithms adapt the transfer functions of the filters to achieve excellent control.; Prior work in adaptive inverse control has focused mainly on feedforward control and disturbance cancelling for single-input single-output linear plants, and on feedforward control for single-input single-output nonlinear plants. This dissertation extends the prior work to encompass feedforward control and disturbance cancelling for single-input single-output or multi-input multi-output, linear or nonlinear plants.; An important part of this work is the development of a gradient-descent based algorithm for updating the weights of either the controller or the disturbance cancelling filters. The algorithm decouples nicely, allowing separate implementation of the adaptive controller, plant model and disturbance canceller; only local information is needed for the weight update. Very general user-specified constraints on the control effort may be satisfied, and excellent disturbance rejection can be achieved. Additionally, it is shown how to compensate for the effects of non-ideal sensors.; The added functionality does not come at the expense of algorithmic or structural complexity. The final control architecture in this dissertation is much simpler than any previously reported. Simulation results are presented to verify the analysis and synthesis methods. Overall, excellent results are obtained.
Keywords/Search Tags:Plant, Adaptive inverse control, Disturbance
Related items