To meet the requirements of high performance, low cost, and easy operation of the robot, a brushless motor drive and control system for the robot joint is designed, including CAN bus, WPF upper host computer developme...To meet the requirements of high performance, low cost, and easy operation of the robot, a brushless motor drive and control system for the robot joint is designed, including CAN bus, WPF upper host computer development, and magnetic encoders, and other sensors, in which the STM32 F103 chip is used as the main control chip, and the DRV8323 is a brushless motor drive chip. The principle of field-oriented control(FOC) brushless motor drive is elaborated.Meanwhile, the drive and control system design is completed from both hardware and software aspects. Finally, the PID algorithm is used for the closed-loop speed test of the robot joint. The experimental result shows that the designed robot joints and control system run smoothly and reliably, have the characteristics of modularization and miniaturization, and are suitable for the control of micro-service robots and manipulators.展开更多
Taking three-phase electrode adjusting system of submerged arc furnace as study object which has nonlinear, time-variant, multivariable and strong coupling features, a neural adaptive PSD(proportion, sum and different...Taking three-phase electrode adjusting system of submerged arc furnace as study object which has nonlinear, time-variant, multivariable and strong coupling features, a neural adaptive PSD(proportion, sum and differential) dispersive decoupling controller was developed by combining neural adaptive PSD algorithm with dispersive decoupling network. In this work, the production technology process and control difficulties of submerged arc furnace were simply introduced, the necessity of establishing a neural adaptive PSD dispersive decoupling controller was discussed, the design method and the implementation steps of the controller are expounded in detail, and the block diagram of the controlled system is presented. By comparison with experimental results of the conventional PID controller and the adaptive PSD controller, the decoupling ability, adaptive ability, self-learning ability and robustness of the neural adaptive PSD dispersive decoupling controller have been testified effectively. The controller is applicable to the three-phase electrode adjusting system of submerged arc furnace, and it will play an important role for achieving the power balance of three-phrase electrodes, saving energy and reducing consumption in the process of smelting.展开更多
The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the req...The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the requirements. A robust adaptive neural network controller （RANNC） for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 ℃/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller （INNC） and PID controller （PIDC）.展开更多
基金Project(51805368) supported by the National Natural Science Foundation of ChinaProject(2018QNRC001) supported by the Young Elite Scientists Sponsorship Program by China Association for Science and TechnologyProject(DMETKF2021017) supported by Open Fund of State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology,China。
文摘To meet the requirements of high performance, low cost, and easy operation of the robot, a brushless motor drive and control system for the robot joint is designed, including CAN bus, WPF upper host computer development, and magnetic encoders, and other sensors, in which the STM32 F103 chip is used as the main control chip, and the DRV8323 is a brushless motor drive chip. The principle of field-oriented control(FOC) brushless motor drive is elaborated.Meanwhile, the drive and control system design is completed from both hardware and software aspects. Finally, the PID algorithm is used for the closed-loop speed test of the robot joint. The experimental result shows that the designed robot joints and control system run smoothly and reliably, have the characteristics of modularization and miniaturization, and are suitable for the control of micro-service robots and manipulators.
基金Project(61174132) supported by the National Natural Science Foundation of ChinaProject(09JJ6098) supported by the Natural Science Foundation of Hunan Province, China
文摘Taking three-phase electrode adjusting system of submerged arc furnace as study object which has nonlinear, time-variant, multivariable and strong coupling features, a neural adaptive PSD(proportion, sum and differential) dispersive decoupling controller was developed by combining neural adaptive PSD algorithm with dispersive decoupling network. In this work, the production technology process and control difficulties of submerged arc furnace were simply introduced, the necessity of establishing a neural adaptive PSD dispersive decoupling controller was discussed, the design method and the implementation steps of the controller are expounded in detail, and the block diagram of the controlled system is presented. By comparison with experimental results of the conventional PID controller and the adaptive PSD controller, the decoupling ability, adaptive ability, self-learning ability and robustness of the neural adaptive PSD dispersive decoupling controller have been testified effectively. The controller is applicable to the three-phase electrode adjusting system of submerged arc furnace, and it will play an important role for achieving the power balance of three-phrase electrodes, saving energy and reducing consumption in the process of smelting.
基金Project(N100604002) supported by the Fundamental Research Funds for Central Universities of ChinaProject(61074074) supported by the National Natural Science Foundation of China
文摘The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the requirements. A robust adaptive neural network controller （RANNC） for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 ℃/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller （INNC） and PID controller （PIDC）.
