Error analysis and optimal design of a class of translational parallel kinematic machine using particle swarm optimization

Robotica ◽  
2009 ◽  
Vol 27 (1) ◽  
pp. 67-78 ◽  
Author(s):  
Qingsong Xu ◽  
Yangmin Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Architectural Design ◽  
Particle Swarm ◽  
Search Method ◽  
Parallel Kinematic Machine ◽  
Error Performance ◽  
Swarm Optimization ◽  
Error Amplification ◽  
Parallel Kinematic

SUMMARYIn this paper, the optimization of architectural parameters for a class of translational parallel kinematic machine (PKM) is performed with the particle swarm optimization (PSO) to achieve the best accuracy characteristics. The conventional error transformation matrix (ETM) is derived based on the differentiation of kinematic equations, and a new error amplification index (EAI) over a usable workspace is proposed as an error performance index for the optimization. To validate the efficiency of the PSO method, both the traditional direct search method and the genetic algorithm (GA) are implemented as well. The simulation results not only show the advantages of PSO method for the architectural optimization, but also reveal the necessity to introduce the EAI for the optimal design. And the results are valuable for architectural design of the PKM for machine tool applications.

scholarly journals Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2000
Author(s):  
Jin-Hwan Lee ◽  
Woo-Jung Kim ◽  
Sang-Yong Jung
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Cost Function ◽  
Particle Swarm ◽  
Torque Ripple ◽  
Rate Of Change ◽  
Swarm Optimization ◽  
Traction Motor ◽  
Target Performance ◽  
The Cost

This paper proposes a robust optimization algorithm customized for the optimal design of electric machines. The proposed algorithm, termed “robust explorative particle swarm optimization” (RePSO), is a hybrid algorithm that affords high accuracy and a high search speed when determining robust optimal solutions. To ensure the robustness of the determined optimal solution, RePSO employs the rate of change of the cost function. When this rate is high, the cost function appears as a steep curve, indicating low robustness; in contrast, when the rate is low, the cost function takes the form of a gradual curve, indicating high robustness. For verification, the performance of the proposed algorithm was compared with those of the conventional methods of robust particle swarm optimization and explorative particle swarm optimization with a Gaussian basis test function. The target performance of the traction motor for the optimal design was derived using a simulation of vehicle driving performance. Based on the simulation results, the target performance of the traction motor requires a maximum torque and power of 294 Nm and 88 kW, respectively. The base model, an 8-pole 72-slot permanent magnet synchronous machine, was designed considering the target performance. Accordingly, an optimal design was realized using the proposed algorithm. The cost function for this optimal design was selected such that the torque ripple, total harmonic distortion of back-electromotive force, and cogging torque were minimized. Finally, experiments were performed on the manufactured optimal model. The robustness and effectiveness of the proposed algorithm were validated by comparing the analytical and experimental results.

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