Predictive iterative learning control with experimental validation |
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| Affiliation: | 1. School of Electrical and Computer Engineering, RMIT University, Melbourne, Victoria 3000, Australia;2. Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;1. School of Automation & Electronics Engineering, Qingdao University of Science & Technology, Qingdao 266042, PR China;2. School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266042, PR China;3. Advanced Control Systems Lab, School of Electronics & Information Engineering, Beijing Jiaotong University, Beijing 100044, PR China;4. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada |
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| Abstract: | This paper develops an iterative learning control law that exploits recent results in the area of predictive repetitive control where a priori information about the characteristics of the reference signal is embedded in the control law using the internal model principle. The control law is based on receding horizon control and Laguerre functions can be used to parameterize the future control trajectory if required. Error convergence of the resulting controlled system is analyzed. To evaluate the performance of the design, including comparative aspects, simulation results from a chemical process control problem and supporting experimental results from application to a robot with two inputs and two outputs are given. |
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| Keywords: | Iterative learning control Predictive control Experimental benchmarking |
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