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RISS 인기검색어
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- 저자 : Haozhen Dong (검색결과 3 건)
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Haozhen Dong,Xinyu Li,Pi Shen,Liang Gao,Haorang Zhong 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.2
Interval type-2 fuzzy logic controller (IT2FLC) owns good performance under uncertainty and nonlinearity environments while its optimization is hard and complicated. In this work, we propose an optimization method based on differential evolution with better and nearest option (NbDE) for interval type-2 fuzzy logic PID controller (IT2FL-PID-C) in order to control the position of hydraulic serial elastic actuator (SEA). Firstly, a simplified IT2FLPID-C structure with fewer parameters is proposed to reduce the difficulty of the optimization of IT2FL-PID-C. To balance its frequency and step performance, an objective function with weighted integral time absolute error and integral square error is given. Secondly, to investigate the performance of NbDE based IT2FL-PID-C, three experiments are conducted. A set of experiments is taken to determine the weight for fitness function. Then we compare NbDE with other algorithms. In addition, NbDE-IT2FL-PID-C is also compared with other optimization methods. At last, NbDE-IT2FL-PID-C is applied to hydraulic SEA and compared with PID. And a range for the weight of fitness function is given. The results have shown the superiority of NbDE with proposed fitness function to optimizeIT2FL-PID-C and the superiority of NbDE-IT2FL-PID-C to control the position of hydraulic SEA.
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Haozhen Dong,Liang Gao,Xinyu Li,Jingyuan Li,Haorang Zhong 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.5
System modeling of engineering problems is an important task, and it’s very difficult because most engineering problems are of great nonlinearity and input variables selection is difficult. Self-organizing cascade neural network (SCNN) is a new network which inserts the hidden unit into network layer by layer, while current training methods are still of low efficiency. In addition, most neural networks’ inputs units should be provided before training and related input units analysis is of great time-cost. In this paper, a new meta-heuristic algorithm, called as differential evolution with better and nearest option (NbDE), is introduced to SCNN training. In NbDE-SCNN, the orthogonal least square method is applied to evaluate the network contribution of candidate hidden unit and input unit, and NbDE is used to find the best hidden units. Four benchmarks, including the Henon chaotic series prediction, a nonlinear dynamic system, a hydraulic system and a nonlinearity impedance control strategy are used to test the performance of NbDE-SCNN. Simulation and experiment results show that the NbDE-SCNN can select proper input units for system modeling and shows better efficiency in system modeling compared with conventional training methods.
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Haoran Zhong,Xinyu Li,Liang Gao,Haozhen Dong 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.7
Series elastic actuators (SEAs) have emerged as promising devices to enhance the safety of human-robot interactions in the manufacturing industry. However, the control of a hydraulic SEA under disturbance remains an unexplored issue. To address this problem, an admittance control method with parameter self-optimization is developed in this study. The hydraulic SEA and its dynamic model are first developed, and then, an admittance controller that combines a passive disturbance observer (DOB) and a feedback compensator is developed based on load movement dynamics. The control law of the framework is made independent of the hydraulic dynamics by considering the uncertainty and tracking error as disturbances. This simplifies the controller computation, enhances system robustness, and facilitates practical application. Next, the control performance is further improved by optimizing the control parameters using an improved crowding-based dynamic population size differential evolution (crowdingbased dynNP-DE) algorithm. Benchmark and optimization experiments are performed to verify the superiority of the modified algorithm and obtain the control parameters. Finally, the optimized parameters are applied to practical experiments to validate the improved performance of the proposed admittance control scheme. The results show that the proposed method effectively reduces the SEA stiffness tracking error, with respect to the external contact force.
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