Abstract
In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology.
Original language | English |
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Pages (from-to) | 1199-1208 |
Number of pages | 10 |
Journal | Iranian Journal of Science and Technology - Transactions of Electrical Engineering |
Volume | 48 |
Issue number | 3 |
DOIs | |
Publication status | Published - Sept 2024 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive licence to Shiraz University 2024.
Keywords
- Constraint handling
- Convex optimization
- Noise
- Stochastic model predictive control