Composite control based on active disturbance rejection for a fast tool servo system
LIU Wentao1,XIONG Weili1,2
1.School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
Abstract:Fast Tool Servo (FTS) system is a key component to realize the machining of micromachined parts. Taking the piezoelectric ceramic type FTS system as the research object, the hysteresis state delay model, the time varying delay model and the unmolded dynamic nonlinear model are introduced into the FTS system model design to describe the chattering phenomena. Based on neural network control, an active disturbance rejection composite control scheme with hysteresis delay compensation function is proposed to realize the chattering control of FTS system. The linear active disturbance rejection control (LADRC) considers the internal uncertainty, delay nonlinearity and other disturbances as total disturbance and estimates compensation in real time, and the adaptive BP neural network is used to approximate the disturbance estimation error among them. Compared with the traditional model inversion method, the composite control scheme is easy to be designed initially without precise mathematical model. Compared with the LADRC, the composite control scheme reduces the number of parameters that need to be adjusted, has higher tracking accuracy under the same bandwidth. The simulation results show that the designed composite control has better robustness, can effectively realize fast and precise tracking control of piezoelectric ceramic type FTS system.
刘文韬1,熊伟丽1,2. 基于自抗扰的快速刀具伺服系统复合控制[J]. 振动与冲击, 2023, 42(12): 39-47.
LIU Wentao1,XIONG Weili1,2. Composite control based on active disturbance rejection for a fast tool servo system. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(12): 39-47.
[1] Zhao D, Zhu Z, Huang P, et al. Development of a piezoelectrically actuated dual-stage fast tool servo[J]. Mechanical Systems and Signal Processing, 2020, 144: 106873.
[2] 吕凯波, 娄培生, 谷丰收, 等. 基于声压信号能量峭度的早期切削颤振预警技术研究[J]. 振动与冲击, 2021, 40(20): 50-55.
Lv Kaibo, LOU Peisheng, GU Fengshou, et al. A study on early chatter monitoring based on energy kurtosis index of acoustic signals[J]. Journal of Vibration and Shock, 2021, 40(20): 50-55.
[3] 谢锋云, 江炜文, 陈红年, 等. 基于广义BP神经网络的切削颤振识别研究[J]. 振动与冲击, 2018, 37(5): 65-70.
XIE Fengyun, JIANG Weiwen, CHEN Hongnian, et al. Cutting chatter recognition based on generalized BP neural network[J]. Journal of Vibration and Shock, 2018, 37(5): 65-70.
[4] Ding F, Chen T. Parameter estimation of dual-rate stochastic systems by using an output error method[J]. IEEE Transactions on Automatic Control, 2005, 50(9): 1436-1441.
[5] Ding F, Chen T. Performance analysis of multi-innovation gradient type identification methods[J]. Automatica, 2007, 43(1): 1-14.
[6] Taylor F W. On the art of cutting metals[M]. American Society of Mechanical Engineers, 1901.
[7] Merchant, M E. Mechanics of the metal cutting process, II. Plasticity conditions in orthogonal cutting[J]. Joumal of Applied Physics, 1945, 16: 318-324.
[8] Hahn R S. On the theory of regenerative chatter in precision-grinding operations[J]. Transaction of ASME, 1954, 76(1): 593-597.
[9] 刘显波, 何恩元, 龙新华, 等. 时滞作用下切削系统的时频响应特性研究[J]. 振动与冲击, 2020, 39(6): 8-14.
Liu Xianbo, He Enyuan, Long Xinhua, et al. Time and frequency domain characteristics of a cutting system with time-delay effects[J]. Journal of Vibration and Shock, 2020, 39(6): 8-14.
[10] 师汉民. 金属切削理论及其应用新探[M]. 武汉:华中科技大学出版社, 2003: 235-328.
Shi Hanmin. Metal cutting theory and practice : A new perspective[M]. Wuhan: Huazhong University of Science and Technology Press, 2003: 235-328.
[11] 王安. 车削颤振的动力学建模与分析[D]. 兰州: 兰州理工大学, 2020: 29-42.
Wang An. Dynamics modeling and analysis of turning chatter[D]. Lanzhou: Lanzhou University of Technology, 2020: 29-42.
[12] Pan J, Su C Y. STEPANENKO Y. Modeling and robust adaptive control of metal cutting mechanical system[C]. Proceeding of American Control Conference. Arlington, VA, USA: IEEE, 2001: 1268–1273.
[13] 胡佳明,朱晓锦,方昱斌,等. 基于迟滞观测器的压电堆补偿控制[J]. 振动与冲击, 2021, 40(4): 81-86.
HU Jiaming, ZHU Xiaojin, FANG Yubin, et al. Compensation control for piezoelectric stack based on a hysteretic observer[J]. Journal of Vibration and Shock, 2021, 40(4): 81-86.
[14] 胡俊峰,何建康,杨明立. 压电式二维微定位平台的率相关迟滞建模[J]. 振动与冲击, 2020, 39(6): 104-110.
HU Junfeng, HE Jiankang, YANG Mingli. Rate-dependent modeling of a piezoelectric two-dimensional micro positioning stage[J]. Journal of Vibration and Shock, 2020, 39(6): 104-110.
[15] Ding F, Chen T. Combined parameter and output estimation of dual-rate systems using an auxiliary model[J]. Automatica, 2004, 40(10): 1739-1748.
[16] 周淼磊, 张敬爱, 赵宇, 等. 压电微定位平台神经网络与专家模糊复合控制方法[J]. 控制与决策, 2018, 33(1): 95-100.
Zhou Miaolei, Zhao Jingai, Zhao Yu, et al. Hybrid control for piezoelectric micro positioning platform based on BP neural network and expert fuzzy control[J]. Control and Decision, 2018, 33(1): 95-100.
[17] Abro K A, Atangana A. A comparative analysis of electromechanical model of piezoelectric actuator through caputo-fabrizio and atangana-baleanu fractional derivatives[J]. Mathematical Methods in the Applied Sciences, 2020, 43(17): 9681-9691.
[18] 丁锋. 系统辨识:多新息辨识理论与方法[M]. 北京: 科学出版社, 2016.
Ding Feng. System Identification: Multi-Innovation Identification Theory and Methods[M]. Beijing: Science Press, 2016.
[19] Zhang X, Ding F. Optimal adaptive filtering algorithm by using the fractional-order derivative[J]. IEEE Signal Processing Letters, 2022, 29: 399-403.
[20] 李自成, 张赛, 王后能, 等. 基于混合差分遗传算法的Bouc-Wen迟滞模型辨识策略[J]. 控制与决策, 2021, 36(2): 371-378.
Li Zicheng, Zhang Sai, Wang Houneng, et al. Bouc-Wen hysteresis model identification strategy based on hybrid differential genetic algorithm[J]. Control and Decision, 2021, 36(2): 371-378.
[21] Su Q, Chen W, Deng J, et al. A 3-DOF sandwich piezoelectric manipulator with low hysteresis effect: Design, modeling and experimental evaluation[J]. Mechanical Systems and Signal Processing, 2021, 158(1): 107768.
[22] 刘晓琳,姜梦馨. 基于WOA的飞机舵机电动加载系统双环复合控制研究[J]. 振动与冲击, 2021, 40(12): 246-253.
LIU Xiaolin, JIANG Mengxin. A study on double loop composite control based on WOA for an aircraft rudder electric loading system[J]. Journal of Vibration and Shock, 2021, 40(12): 246-253.
[23] 李伟平, 魏静, 邬平波, 等. 高速列车谐波转矩振动分析及自抗扰控制[J]. 振动与冲击, 2022, 41(1): 98-106.
LI Weiping, WEI Jing, WU Pingbo, et al. Harmonic torque vibration analysis and active disturbance rejection control of high-speed train[J]. Journal of Vibration and Shock, 2022, 41(1): 98-106.
[24] Han J. From PID to active disturbance rejection control[J]. IEEE Transactions on Industrial Electronics 2009, 56(3): 900–906.
[25] Wu D, Xie X D, Zhou S Y. Design of a normal stress electromagnetic fast linear actuator[J]. IEEE Transactions on Magnetics, 2010, 46(4): 1007–1014.
[26] 韩文杰, 谭文. 基于PID参数整定的线性自抗扰控制参数整定[J]. 控制与决策, 2021, 36(7): 1592-1600.
Han Wenji, Tan Wen. Tuning of linear active disturbance rejection controllers based on PID tuning rules[J]. Control and Decision, 2021, 36(7): 1592-1600.
[27] Gao Z. On the centrality of disturbance rejection in automatic control[J]. ISA Transactions, 2014, 53(4): 850–857.
[28] Yao J, Deng W. Active disturbance rejection adaptive control of hydraulic servo systems[J]. IEEE Transactions on Industrial Electronics, 2017, 64(10): 8023-8032.
[29] 吴丹, 赵彤, 陈恳. 快速刀具伺服系统自抗扰控制的研究与实践[J]. 控制理论与应用, 2013, 30(12): 1534-1542.
Wu Dan, Zhao Tong, Chen Ken. Research and industrial applications of active disturbance rejection control to fast tool servos[J]. Control Theory and Applications, 2013, 30(12): 1534-1542.
[30] Zhu Z, Chen L, Huang P, et al. Design and control of a piezoelectrically actuated fast tool servo for diamond turning of microstructured surfaces[J]. IEEE Transactions on Industrial Electronics, 2020, 67(8): 6688-6697.
[31] Meng H, Kang Y, Chen Z, et al. Stability analysis and stabilization of a class of cutting systems with chatter suppression[J]. IEEE/ASME Transactions on Mechatronics, 2015, 20(2): 991-996.
[32] 赵彤. 基于Preisach迟滞非线性建模与神经网络自适应控制方案设计[D]. 上海: 上海交通大学, 2005: 26-38.
Zhao Tong. Modeling for hysteresis nonlinearity based on preisach and designing of the schene of neural networks adaptive control[D]. Shanghai: Shanghai Jiao Tong University, 2005: 26-38.
[33] Zhang X, Jing R, Li Z, et al. Adaptive pseudo inverse control for a class of nonlinear asymmetric and saturated nonlinear hysteretic systems[J]. IEEE/CAA Journal of Automatica Sinica, 2021, 8(4): 916-928.
[34] Ding F, Liu G, Liu X P. Partially coupled stochastic gradient identification methods for non-uniformly sampled systems[J]. IEEE Transactions on Automatic Control, 2010, 55(8): 1976-1981.
[35] Ding F. Coupled least squares identification for multivariable systems[J]. IET Control Theory and Applications, 2013, 7(1): 68-79.
[36] 张秀宇, 刘翠平, 林岩, 等. 具有磁滞输入的可调金属切削系统鲁棒自适应动态面控制[J]. 控制理论与应用, 2014, 31(9): 1274-1282.
Zhang Xiuyu, Liu Cuiping, Lin Yan, et al. Robust adaptive dynamic surface control for adjustable metal cutting system with hysteresis input[J]. Control Theory and Applications, 2014, 31(9): 1274-1282.
[37] Wang X, Li X, Wu Q, Yin X. Neural network based adaptive dynamic surface control of nonaffine nonlinear systems with time delay and input hysteresis nonlinearities[J]. Neurocomputing, 2018, 333:53-63
[38] Zhang X, Xu Z, Su C, et al. Fuzzy approximator based adaptive dynamic surface control for unknown time delay nonlinear systems with input asymmetric hysteresis nonlinearities[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(8): 2218-2232.
[39] Zhu Z, Chen L, Huang P, et al., Design and control of a piezoelectrically actuated fast tool servo for diamond turning of microstructured surfaces[J]. IEEE Transactions on Industrial Electronics, 2020, 67(8): 6688-6697.