Distributed MPC-based adaptive control for linear systems with unknown parameters |
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| Authors: | Yan Song Kaiqun Zhu Guoliang Wei Jianhua Wang |
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| Institution: | 1. Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;2. College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China |
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| Abstract: | This paper is concerned with the adaptive control problem for a class of linear discrete-time systems with unknown parameters based on the distributed model predictive control (MPC) method. Instead of using the system state, the state estimate is employed to model the distributed state estimation system. In this way, the system state does not have to be measurable. Furthermore, in order to improve the system performance, both the output error and its estimation are considered. Moreover, a novel Lyapunov functional, comprised of a series of distributed traces of estimation errors and their transposes, has been presented. Then, sufficient conditions are obtained to guarantee the exponential ultimate boundedness of the system as well as the asymptotic stability of the error system by solving a nonlinear programming (NP) problem subject to input constraints. Finally, the simulation examples is given to illustrate the effectiveness and the validity of the proposed technique. |
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| Keywords: | Corresponding author |
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