Robust Hybrid State-Space Self-Tuning Control Using Dual-Rate Sampling.
Abstract
This report presents a hybrid state-space self-tuner using a new dual-rate sampling scheme for digital adaptive control of continuous-time uncertain linear systems. A state-space-based recursive least-squares algorithm, together with a variable forgetting factor, is used for direct estimation of both the equivalent discrete-time uncertain linear system parameters and associated discrete-time state of a continuous-time uncertain linear system from the sampled input and output data. An analog optimal regional pole-placement design method is used for designing an optimal observer-based analoque controller. A sub-optimal observer-based digital controller is then designed from the designed analoque controller using digital redesign technique. To enhance the robustness of parameter identification and state estimation algorithms, a dynamic bound for a class of uncertain bilinear parameters and a fast-rate digital controller are developed at each fast-sampling period. Also, to accommodate computation loads and computation delay for developing the advance hybrid self-tuner, the designed analoque controller and observer gains are both updated at each slow-sampling period. This control technique has been successfully applied to benchmark control problems.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 31, 1994
- Accession Number
- ADA288359
Entities
People
- C. K. Koc
- L. S. Shieh
Organizations
- University of Houston