The aim of the paper is to compare three different controllers tuned with a particle swarm optimi... more The aim of the paper is to compare three different controllers tuned with a particle swarm optimization (PSO) for robot trajectory control. For this reason, Fractional order PID controller (FOPID) tuned with PSO was studied primarily for a 2 DOF planar robot. As the parameters of FOPID controller were optimized by PSO for a given trajectory, three different cost functions were used for better comparison. In order to compare the performance of the optimized FOPID controller with other controllers, the Fuzzy logic controller (FLC) and the PID controller were also tuned with PSO. In order to test the robustness of the tuned controllers, the model parameters and the given trajectory were changed and the white noise was added to the system. All of the trajectory simulation results show that FOPID controller tuned by PSO has good performance and it is better than the FLC and the PID tuned by PSO. Also, the results obtained from robustness test indicate the superior robustness of the FOPID controller on the others.
This paper proposes a new PID parameter tuning method using particle swarm optimization (PSO) wit... more This paper proposes a new PID parameter tuning method using particle swarm optimization (PSO) without tuning operatorpsilas know-how. The method searches the PID parameter that realizes the expected step response of the plant. The expected response is defined by the overshoot ratio, the rising time, the settling time. The method is implemented into the PID tuning tool on a personal computer. The plant model represented by the transfer function is obtained by system identification on the PID tuning tool. The PID parameter is computed by PSO-based PID tuning method according to the obtained model. The numerical result and the experiment result show the effectiveness of the proposed tuning method and the developed tool.
In this paper, a novel method for tuning PID controller of automatic gantry crane control using p... more In this paper, a novel method for tuning PID controller of automatic gantry crane control using particle swarm optimization (PSO) is proposed. PSO is one of the most recent optimization techniques based on evolutionary algorithm. PSO is also known as computationally efficient method. This work presents in detail how to apply PSO method in finding the optimal PID gains of gantry crane system in the fashion of min-max optimization. The simulation results show that with proper tuning a satisfactory PID control performance can be achieved to drive nonlinear plant. The controller is able to effectively move the trolley of the crane in short time while canceling the swing angle of the payload hanging on the trolley at the end position. The robustness of the controller is also tested.
The PID controller is one of the most popular controllers, due to its remarkable effectiveness, s... more The PID controller is one of the most popular controllers, due to its remarkable effectiveness, simplicity of implementation and broad applicability. However, the conventional approach for parameter optimization in PID controller is easy to produce surge and big overshoot, and therefore heuristics optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO) are employed to enhance the capability of traditional techniques. One major problem of these algorithms is that they may be trapped in the local optima of the objective and lead to poor performance. In this paper, a novel stochastic optimization technique named particle filter optimization (PFO) is proposed to achieve better performance in dealing with local optima while reduce the computation complexity of PID parameter tuning process. Simulation results indicate that the proposed algorithm is effective and efficient, and demonstrate that the proposed algorithm exhibits a significant performance improvement over several other benchmark methods.
The aim of this paper is to study the tuning of a PID controller using soft computing techniques.... more The aim of this paper is to study the tuning of a PID controller using soft computing techniques. The methodology and efficiency of the proposed method are compared with that of traditional methods. Determination or tuning of the PID parameters continues to be important as these parameters have a great influence on the stability and performance of the control system. The results obtained reflect that use of soft computing based controller improves the performance of process interms of time domain specifications, set point tracking, regulatory changes and also provides an optimum stability.
The aim of the paper is to compare three different controllers tuned with a particle swarm optimi... more The aim of the paper is to compare three different controllers tuned with a particle swarm optimization (PSO) for robot trajectory control. For this reason, Fractional order PID controller (FOPID) tuned with PSO was studied primarily for a 2 DOF planar robot. As the parameters of FOPID controller were optimized by PSO for a given trajectory, three different cost functions were used for better comparison. In order to compare the performance of the optimized FOPID controller with other controllers, the Fuzzy logic controller (FLC) and the PID controller were also tuned with PSO. In order to test the robustness of the tuned controllers, the model parameters and the given trajectory were changed and the white noise was added to the system. All of the trajectory simulation results show that FOPID controller tuned by PSO has good performance and it is better than the FLC and the PID tuned by PSO. Also, the results obtained from robustness test indicate the superior robustness of the FOPID controller on the others.
This paper proposes a new PID parameter tuning method using particle swarm optimization (PSO) wit... more This paper proposes a new PID parameter tuning method using particle swarm optimization (PSO) without tuning operatorpsilas know-how. The method searches the PID parameter that realizes the expected step response of the plant. The expected response is defined by the overshoot ratio, the rising time, the settling time. The method is implemented into the PID tuning tool on a personal computer. The plant model represented by the transfer function is obtained by system identification on the PID tuning tool. The PID parameter is computed by PSO-based PID tuning method according to the obtained model. The numerical result and the experiment result show the effectiveness of the proposed tuning method and the developed tool.
In this paper, a novel method for tuning PID controller of automatic gantry crane control using p... more In this paper, a novel method for tuning PID controller of automatic gantry crane control using particle swarm optimization (PSO) is proposed. PSO is one of the most recent optimization techniques based on evolutionary algorithm. PSO is also known as computationally efficient method. This work presents in detail how to apply PSO method in finding the optimal PID gains of gantry crane system in the fashion of min-max optimization. The simulation results show that with proper tuning a satisfactory PID control performance can be achieved to drive nonlinear plant. The controller is able to effectively move the trolley of the crane in short time while canceling the swing angle of the payload hanging on the trolley at the end position. The robustness of the controller is also tested.
The PID controller is one of the most popular controllers, due to its remarkable effectiveness, s... more The PID controller is one of the most popular controllers, due to its remarkable effectiveness, simplicity of implementation and broad applicability. However, the conventional approach for parameter optimization in PID controller is easy to produce surge and big overshoot, and therefore heuristics optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO) are employed to enhance the capability of traditional techniques. One major problem of these algorithms is that they may be trapped in the local optima of the objective and lead to poor performance. In this paper, a novel stochastic optimization technique named particle filter optimization (PFO) is proposed to achieve better performance in dealing with local optima while reduce the computation complexity of PID parameter tuning process. Simulation results indicate that the proposed algorithm is effective and efficient, and demonstrate that the proposed algorithm exhibits a significant performance improvement over several other benchmark methods.
The aim of this paper is to study the tuning of a PID controller using soft computing techniques.... more The aim of this paper is to study the tuning of a PID controller using soft computing techniques. The methodology and efficiency of the proposed method are compared with that of traditional methods. Determination or tuning of the PID parameters continues to be important as these parameters have a great influence on the stability and performance of the control system. The results obtained reflect that use of soft computing based controller improves the performance of process interms of time domain specifications, set point tracking, regulatory changes and also provides an optimum stability.
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Papers by Neeraj Jain