Chaos particle swarm optimization and T–S fuzzy modeling approaches to constrained predictive control
► T-S fuzzy modeling approach, chaos optimization algorithm (COA) and particle swarm optimization (PSO) have been used to perform predictive control. ► Chaos particle swarm optimization (CPSO) and T-S fuzzy modeling approaches are proposed to perform constrained predictive control. ► Predictive cont...
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| Vydané v: | Expert systems with applications Ročník 39; číslo 1; s. 194 - 201 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
2012
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | ► T-S fuzzy modeling approach, chaos optimization algorithm (COA) and particle swarm optimization (PSO) have been used to perform predictive control. ► Chaos particle swarm optimization (CPSO) and T-S fuzzy modeling approaches are proposed to perform constrained predictive control. ► Predictive control of temperature of continued hyperthermic celiac perfusion based on the proposed approaches was conducted. ► Test results of the case indicate T-S fuzzy model based on CPSO outperforms models based on generalized predictive control, COA, and PSO.
Predictive control of systems is very much related to the efficiency and cost of systems, as well as to the quality of systems outcomes. However, it is difficult to achieve optimal predictive control because most predictive controls for systems have characteristics of randomness, strong and complex constraints, large delay time, fuzziness, and nonlinearity. Conventional methods of solving constrained nonlinear optimization problems for predictive control are mainly based on quadratic programming, which is quite sensitive to initial values, easy to trap in local minimal points, and requires large computational effort. In recent years, T–S fuzzy modeling has been found to be an effective approach in performing predictive control. Intelligent optimization algorithms, such as chaos optimization algorithm (COA) and particle swarm optimization (PSO), have been shown to have faster convergence and higher iterative accuracy than those based on conventional optimization methods. In this paper, chaos particle swarm optimization (CPSO), which involves combining the strengths of COA and PSO, and T–S fuzzy modeling are proposed as approaches to perform constrained predictive control. Predictive control of temperature of continued hyperthermic celiac perfusion for medical treatment based on the proposed approaches was carried out. Simulation tests were conducted to evaluate the performance of temperature control based on T–S fuzzy modeling and CPSO. Test results indicate that the T–S fuzzy model based on CPSO outperforms models based on generalized predictive control, COA, and PSO. |
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| AbstractList | Predictive control of systems is very much related to the efficiency and cost of systems, as well as to the quality of systems outcomes. However, it is difficult to achieve optimal predictive control because most predictive controls for systems have characteristics of randomness, strong and complex constraints, large delay time, fuzziness, and nonlinearity. Conventional methods of solving constrained nonlinear optimization problems for predictive control are mainly based on quadratic programming, which is quite sensitive to initial values, easy to trap in local minimal points, and requires large computational effort. In recent years, T-S fuzzy modeling has been found to be an effective approach in performing predictive control. Intelligent optimization algorithms, such as chaos optimization algorithm (COA) and particle swarm optimization (PSO), have been shown to have faster convergence and higher iterative accuracy than those based on conventional optimization methods. In this paper, chaos particle swarm optimization (CPSO), which involves combining the strengths of COA and PSO, and T-S fuzzy modeling are proposed as approaches to perform constrained predictive control. Predictive control of temperature of continued hyperthermic celiac perfusion for medical treatment based on the proposed approaches was carried out. Simulation tests were conducted to evaluate the performance of temperature control based on T-S fuzzy modeling and CPSO. Test results indicate that the T-S fuzzy model based on CPSO outperforms models based on generalized predictive control, COA, and PSO. ► T-S fuzzy modeling approach, chaos optimization algorithm (COA) and particle swarm optimization (PSO) have been used to perform predictive control. ► Chaos particle swarm optimization (CPSO) and T-S fuzzy modeling approaches are proposed to perform constrained predictive control. ► Predictive control of temperature of continued hyperthermic celiac perfusion based on the proposed approaches was conducted. ► Test results of the case indicate T-S fuzzy model based on CPSO outperforms models based on generalized predictive control, COA, and PSO. Predictive control of systems is very much related to the efficiency and cost of systems, as well as to the quality of systems outcomes. However, it is difficult to achieve optimal predictive control because most predictive controls for systems have characteristics of randomness, strong and complex constraints, large delay time, fuzziness, and nonlinearity. Conventional methods of solving constrained nonlinear optimization problems for predictive control are mainly based on quadratic programming, which is quite sensitive to initial values, easy to trap in local minimal points, and requires large computational effort. In recent years, T–S fuzzy modeling has been found to be an effective approach in performing predictive control. Intelligent optimization algorithms, such as chaos optimization algorithm (COA) and particle swarm optimization (PSO), have been shown to have faster convergence and higher iterative accuracy than those based on conventional optimization methods. In this paper, chaos particle swarm optimization (CPSO), which involves combining the strengths of COA and PSO, and T–S fuzzy modeling are proposed as approaches to perform constrained predictive control. Predictive control of temperature of continued hyperthermic celiac perfusion for medical treatment based on the proposed approaches was carried out. Simulation tests were conducted to evaluate the performance of temperature control based on T–S fuzzy modeling and CPSO. Test results indicate that the T–S fuzzy model based on CPSO outperforms models based on generalized predictive control, COA, and PSO. |
| Author | Jiang, Huimin Ysim, Y.C. Kwong, C.K. Chen, Zengqiang |
| Author_xml | – sequence: 1 givenname: Huimin surname: Jiang fullname: Jiang, Huimin organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China – sequence: 2 givenname: C.K. surname: Kwong fullname: Kwong, C.K. email: mfckkong@polyu.edu.hk organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China – sequence: 3 givenname: Zengqiang surname: Chen fullname: Chen, Zengqiang organization: Department of Automation, Nankai University, Tianjin, China – sequence: 4 givenname: Y.C. surname: Ysim fullname: Ysim, Y.C. organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China |
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| Cites_doi | 10.1007/BF02739235 10.1016/0005-1098(87)90087-2 10.1016/0957-4174(96)00026-7 10.1016/j.chaos.2007.09.063 10.1109/4235.910467 10.1016/j.chaos.2004.11.095 10.1016/j.arcontrol.2005.01.001 10.1016/j.eswa.2007.08.088 10.1016/j.eswa.2006.12.004 10.1016/j.eswa.2009.04.015 10.1016/S1004-9541(07)60121-9 10.1119/1.15345 10.1134/S1061920809010087 10.1016/j.applthermaleng.2008.11.001 |
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| Keywords | Constrained predictive control COA Chaos particle swarm optimization PSO T–S fuzzy models |
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| Snippet | ► T-S fuzzy modeling approach, chaos optimization algorithm (COA) and particle swarm optimization (PSO) have been used to perform predictive control. ► Chaos... Predictive control of systems is very much related to the efficiency and cost of systems, as well as to the quality of systems outcomes. However, it is... |
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| SubjectTerms | Chaos particle swarm optimization Chaos theory COA Constrained predictive control Constraints Fuzzy Fuzzy logic Fuzzy set theory Mathematical models Optimization Predictive control PSO T–S fuzzy models |
| Title | Chaos particle swarm optimization and T–S fuzzy modeling approaches to constrained predictive control |
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