Stochastic model predictive control for linear systems with unbounded additive uncertainties |
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| Institution: | 1. School of Automation, Nanjing University of Science and Technology, Nanjing, China;2. School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China;3. School of Science, Huzhou University, Huzhou 313000, China;1. Department of Mathematics, Harbin Institute of Technology at Weihai, Weihai, 264209, PR China;2. Control and Simulation Center, Harbin Institute of Technology, Harbin 150080, China |
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| Abstract: | This paper presents two stochastic model predictive control methods for linear time-invariant systems subject to unbounded additive uncertainties. The new methods are developed by formulating the chance constraints into deterministic form, which are treated in analogy with robust constraints, by using the probabilistic reachable set. The first one is the time-varying tube-based stochastic model predictive control algorithm, which is designed by employing the time-varying probabilistic reachable sets as tubes. The second one is the constant tube-based stochastic model predictive control algorithm, which is developed by enforcing a constant tightened constraint in the entire prediction horizon. In addition, the soft constraints are proposed to associate with the state initialization in the algorithms to enhance the feasibility. The algorithm feasibility and closed-loop stability results are provided. The efficacy of the approaches is demonstrated by means of numerical simulations. |
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