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Multiple model adaptive control for a class of nonlinear systems with unknown control directions |
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| Authors: | Miao Huang Xin Wang Zheming Lu Hongye Su Lang Wang |
| Affiliation: | 1. Institute of Cyber-Systems and Control, Zhejiang University , Hangzhou, China;2. College of Information Science and Engineering, Ningbo Institute of Technology, Zhejiang University , Ningbo, China;3. Center of Electrical and Electronic Technology, Shanghai Jiao Tong University , Shanghai, China;4. Institute of Cyber-Systems and Control, Zhejiang University , Hangzhou, China;5. College of Information Science and Engineering, Ningbo Institute of Technology, Zhejiang University , Ningbo, China |
| Abstract: | This study introduces an improved multiple model adaptive control (MMAC) algorithm for a class of nonlinear discrete-time systems. The controller consists of a linear direct adaptive controller, a neural network-based nonlinear direct adaptive controller and a switching mechanism. The assumption of the nonlinear term is relaxed by incorporating a parameter estimator with an augmented error. The control direction of the system is not required to be known by employing a linear direct adaptive controller with the discrete Nussbaum gain and future output predictions. The stability of the closed-loop systems applying the proposed MMAC method is proved and the improved transient performance of the system is illustrated by the simulation results. |
| Keywords: | Unknown control directions discrete-time nonlinear systems multiple models adaptive controller |