A novel auto-tuning PID control mechanism for nonlinear systems |
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| Affiliation: | 1. Pamukkale University, Department of Computer Engineering, Kinikli Campus, 20070 Denizli, Turkey;2. Pamukkale University, Department of Electrical and Electronics Engineering, Kinikli Campus, 20070 Denizli, Turkey;1. Department of Electrical Engineering, Visvesvaraya National Institute of Technology, Nagpur, India;2. Department of Electrical Engineering, National Institute of Technology, Uttarakhand, India;1. Departamento de Engenharia Elétrica, Universidade Federal do Ceará, Brazil, Caixa postal 6001, Campus do Pici, CEP: 60455-760, Fortaleza, CE, Brazil;2. Yokogawa Italia srl, Milan, Italy;3. Dipartimento di Ingegneria Meccanica e Industriale, University of Brescia, Italy, Via Branze 38, I-25123 Brescia, Italy;4. Departamento de Automação e Sistemas, Universidade Federal de Santa Catarina, Brazil, CEP: 88040-900, Florianópolis, SC, Brazil;1. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China;2. Department of Mathematics, Nanjing University, Nanjing 210093, China |
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| Abstract: | In this paper, a novel Runge–Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. |
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| Keywords: | Model-based predictive control Auto-tuning PID controller MIMO PID controller design Real-time control |
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