This paper presents an application of an Ant Colony Optimization （ACO） algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorith...This paper presents an application of an Ant Colony Optimization （ACO） algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm, an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.展开更多
An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed algorithm is improved by revising the...An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed algorithm is improved by revising the inertia weight of global optimal particles and the introduction of D-Tent chaotic sequence. Through the test of typical function and the autotuning test of proportionalintegral-derivative （PID） parameter, finally a simulation is made to the servo control system of a permanent magnet synchronous motor （PMSM） under double-loop control of rotating speed and current by utilizing the chaotic particle swarm algorithm. Studies show that the proposed algorithm can reduce the iterative times and improve the convergence rate under the condition that the global optimal solution can be got.展开更多
a new strategy combining an expert system and improved genetic algorithms is presented for tuning proportional-integral-derivative （PID） parameters for petrochemical processes. This retains the advantages of genetic...a new strategy combining an expert system and improved genetic algorithms is presented for tuning proportional-integral-derivative （PID） parameters for petrochemical processes. This retains the advantages of genetic algorithms, namely rapid convergence and attainment of the global optimum. Utilization of an orthogonal experiment method solves the determination of the genetic factors. Combination with an expert system can make best use of the actual experience of the plant operators. Simulation results of typical process systems examples show a good control performance and robustness.展开更多
Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by co...Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and thesimulation results show that satisfactory performances are obtained.展开更多
文摘This paper presents an application of an Ant Colony Optimization （ACO） algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm, an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.
基金supported by the National Natural Science Foundation of China(61301011)the Fundamental Research Funds for the Central Universities(HIT.NSRIF.2012010)+1 种基金the China Postdoctoral Science Foundation(2013M540279)the Heilongjiang Postdoctoral Financial Assistance(LBH-Z11157)
文摘An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed algorithm is improved by revising the inertia weight of global optimal particles and the introduction of D-Tent chaotic sequence. Through the test of typical function and the autotuning test of proportionalintegral-derivative （PID） parameter, finally a simulation is made to the servo control system of a permanent magnet synchronous motor （PMSM） under double-loop control of rotating speed and current by utilizing the chaotic particle swarm algorithm. Studies show that the proposed algorithm can reduce the iterative times and improve the convergence rate under the condition that the global optimal solution can be got.
文摘a new strategy combining an expert system and improved genetic algorithms is presented for tuning proportional-integral-derivative （PID） parameters for petrochemical processes. This retains the advantages of genetic algorithms, namely rapid convergence and attainment of the global optimum. Utilization of an orthogonal experiment method solves the determination of the genetic factors. Combination with an expert system can make best use of the actual experience of the plant operators. Simulation results of typical process systems examples show a good control performance and robustness.
文摘Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and thesimulation results show that satisfactory performances are obtained.
