Adaptive Output Feedback Model Predictive Control
| Autor: | Anchita Dey, Abhishek Dhar, Shubhendu Bhasin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Control and Optimization
Optimization and Control (math.OC) Control and Systems Engineering FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Mathematics - Optimization and Control |
| Popis: | Model predictive control (MPC) for uncertain systems in the presence of hard constraints on state and input is a non-trivial problem, and the challenge is increased manyfold in the absence of state measurements. In this paper, we propose an adaptive output feedback MPC technique, based on a novel combination of an adaptive observer and robust MPC, for single-input single-output discrete-time linear time-invariant systems. At each time instant, the adaptive observer provides estimates of the states and the system parameters that are then leveraged in the MPC optimization routine while robustly accounting for the estimation errors. The solution to the optimization problem results in a homothetic tube where the state estimate trajectory lies. The true state evolves inside a larger outer tube obtained by augmenting a set, invariant to the state estimation error, around the homothetic tube sections. The proof for recursive feasibility for the proposed `homothetic and invariant' two-tube approach is provided, along with simulation results on an academic system. 6 pages, 4 figures, 1 table |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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