基于神经网络的逆变器约束自适应PI控制

施建强; 李双; 赵宁宁; 徐梦溪

doi:10.19912/j.0254-0096.tynxb.2022-0859

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (10) : 19-27. DOI: 10.19912/j.0254-0096.tynxb.2022-0859

基于神经网络的逆变器约束自适应PI控制

施建强, 李双, 赵宁宁, 徐梦溪

作者信息 +

CONSTRAINED ADAPTIVE PI CONTROL OF INVERTER BASED ON NEURAL NETWORK

Shi Jianqiang, Li Shuang, Zhao Ningning, Xu Mengxi

Author information +

文章历史 +

摘要

针对负载扰动以及输入输出约束条件下逆变器的电压跟踪控制问题,根据同步旋转（dq）坐标系下的电压电流时域数学模型,提出一种基于神经网络的约束自适应PI控制策略。首先,利用状态转换函数将带约束的系统转换为等价的无约束系统,同时引入动态辅助系统处理输入约束问题;其次,通过反步法进行控制器设计,并为解决传统反步法“微分爆炸”问题,运用神经网络逼近包括虚拟控制律导数在内的非线性项;最后,所设计的虚拟控制律和控制律由PI项和前馈项组成,且PI控制器的增益可在线调整;理论分析表明,所提控制策略能保证闭环系统的所有误差信号最终一致有界,Matlab/Simulink平台仿真结果则进一步验证了该方案的可行性和有效性。

Abstract

According to the voltage and current time domain model in the synchronous rotating dq reference frame, a constrained adaptive PI control strategy is proposed to address the inverter voltage tracking problem with input and output constraints under load disturbance. Firstly, a state translation function is used to transform the system with output constraints into an equivalent unconstrained one. At the same time, an auxiliary dynamic system is introduced to tackle the effects of the input constraints. Secondly, the controller is designed by using back-stepping method. Wherein, neural network is utilized to approximate the nonlinear term including the time derivative of the virtual control input, and then “explosion of complexity” in the traditional backstepping method is avoided. Finally, the designed virtual control law and control law are both composed of PI term and feedforward term, and the gain of the PI controller is continuously adjusted along with the adaptive law. The Lyapunov stability theory is used to prove that all the error signals of the closed loop system are uniformly ultimately bounded under the proposed control strategy. Besides, simulation results in Matlab/Simulink platform demonstrate the feasibility and effectiveness of this method.

导出引用

施建强, 李双, 赵宁宁, 徐梦溪. 基于神经网络的逆变器约束自适应PI控制[J]. 太阳能学报. 2023, 44(10): 19-27 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0859

Shi Jianqiang, Li Shuang, Zhao Ningning, Xu Mengxi. CONSTRAINED ADAPTIVE PI CONTROL OF INVERTER BASED ON NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(10): 19-27 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0859

中图分类号： TM464

参考文献

[1] 郭磊磊, 孙怡舒, 李琰琰, 等. PMSM无权重系数转矩预测控制方法[J]. 太阳能学报, 2021, 42(8): 426-433.
GUO L L, SUN Y S, LI Y Y, et al.Torque predictive control method for pmsm unweighted factor[J]. Acta energiae solaris sinica, 2021, 42(8): 426-433.
[2] 郑峰, 林祥群, 邓长虹, 等. 计及降阶混合控制算法多逆变器并联系统谐振抑制策略[J]. 电力自动化设备, 2021, 41(4): 48-55.
ZHENG F, LIN X Q, DENG C H, et al.Resonance suppression strategy of multi-inverter parallel system considering hybrid control algorithm for order reduction[J]. Electric power automation equipment, 2021, 41(4): 48-55.
[3] LI J, SUN Y, LI X, et al.Observer-based adaptive control for single-phase UPS inverter under nonlinear load[J]. IEEE transactions on transportation electrification, 2022, 8(2): 2785-2796.
[4] HUSSAIN M N, MELATH G, AGARWAL V.An active damping technique for PI and predictive controllers of an interlinking converter in an islanded hybrid microgrid[J]. IEEE transactions on power electronics, 2021, 36(5): 5521-5529.
[5] MOHAMED I S, ROVETTA S, DO T D, et al.A neural-network-based model predictive control of three-phase inverter with an output - filter[J]. IEEE access, 2019, 7: 124737-124749.
[6] BASIT B A, REHMAN A U, CHOI H H, et al.A robust iterative learning control technique to efficiently mitigate disturbances for three-phase standalone inverters[J]. IEEE transactions on industrial electronics, 2022, 69(4): 3233-3244.
[7] KIM J, CHOI H H, JUNG J W.MRAC-based voltage controller for three-phase CVCF inverters to attenuate parameter uncertainties under critical load conditions[J]. IEEE transactions on power electronics, 2020, 35(1): 1002-1013.
[8] 曹永锋, 武玉衡, 叶永强, 等. 基于微分前馈自抗扰的逆变器控制策略[J]. 电力系统自动化, 2019, 43(5): 136-142, 166.
CAO Y F, WU Y H, YE Y Q, et al.Active disturbance rejection control strategy with differential feedforward for inverters[J]. Automation of electric power systems, 2019, 43(5): 136-142, 166.
[9] 杨林, 曾江, 马文杰, 等. 基于改进二阶线性自抗扰技术的微网逆变器电压控制[J]. 电力系统自动化, 2019, 43(4): 146-153.
YANG L, ZENG J, MA W J, et al.Voltage control of microgrid inverter based on improved second-order linear active disturbance rejection control[J]. Automation of electric power systems, 2019, 43(4): 146-153.
[10] 李志华, 曾江, 黄骏翅, 等. 基于线性自抗扰控制的微网逆变器时-频电压控制策略[J]. 电力系统自动化, 2020, 44(10): 145-154.
LI Z H, ZENG J, HUANG J C, et al.Time-frequency voltage control strategy of microgrid inverter based on linear active disturbance rejection control[J]. Automation of electric power systems, 2020, 44(10): 145-154.
[11] 张宝群, 胡长斌, 马龙飞, 等. 基于小信号稳定性分析的含VSG微电网电能质量主动控制[J]. 电力系统自动化, 2019, 43(23): 210-216.
ZHANG B Q, HU C B, MA L F, et al.Active power quality control for microgrid with virtual synchronous generator based on small-signal stability analysis[J]. Automation of electric power systems, 2019, 43(23): 210-216.
[12] 温春雪, 黄耀智, 胡长斌, 等. 虚拟同步发电机接口变换器并联运行虚拟阻抗自适应控制[J]. 电工技术学报, 2020, 35(S2): 494-502.
WEN C X, HUANG Y Z, HU C B, et al.Adaptive control of virtual impedance in parallel operation of virtual synchronous generator interface converter[J]. Transactions of China Electrotechnical Society, 2020, 35(S2): 494-502.
[13] 张雪妍, 付立军, 马凡, 等. 基于虚拟阻抗的逆变器输出电压动态性能优化[J]. 太阳能学报, 2020, 41(11): 38-45.
ZHANG X Y, FU L J, MA F, et al.Dynamic performance optimization of inverter output voltage based on virtual impendance[J]. Acta energiae solaris sinica, 2020, 41(11): 38-45.
[14] 曹文远, 韩民晓, 谢文强, 等. 基于扰动观测器的电压源型逆变器负载电流前馈控制及参数设计方法[J]. 电工技术学报, 2020, 35(4): 862-873.
CAO W Y, HAN M X, XIE W Q, et al.A disturbance-observer-based load current feedforward control and parameter design method for voltage-sourced inverter[J]. Transactions of China Electrotechnical Society, 2020, 35(4): 862-873.
[15] MOHAMMED S A Q, RAFAQ M S, CHOI H H, et al. A robust adaptive PI voltage controller to eliminate impact of disturbances and distorted model parameters for 3-phase CVCF inverters[J]. IEEE transactions on industrial informatics, 2020, 16(4): 2168-2176.
[16] FU M Y, WANG T Q, WANG C L.Adaptive neural-based finite-time trajectory tracking control for underactuated marine surface vessels with position error constraint[J]. IEEE access, 2019, 7: 16309-16322.
[17] 丁岩, 于志刚. 考虑输入输出受限的无人机自适应滑模容错控制[J]. 系统工程与电子技术, 2020, 42(10): 2340-2347.
DING Y, YU Z G.Adaptive sliding mode fault tolerant control of UAV considering input and output constraints[J]. Systems engineering and electronics, 2020, 42(10): 2340-2347.
[18] 林孟豪, 张蕾, 李鹏飞, 等. 考虑输出约束的机械臂命令滤波反步控制[J]. 西安交通大学学报, 2021, 55(12): 70-78.
LIN M H, ZHANG L, LI P F, et al.A backstepping control strategy of command filtering for two-link flexible joint manipulator[J]. Journal of Xi’an Jiaotong University, 2021, 55(12): 70-78.
[19] 沈智鹏, 毕艳楠, 王宇, 等. 输入输出受限船舶的轨迹跟踪自适应递归滑模控制[J]. 控制理论与应用, 2020, 37(6): 1419-1427.
SHEN Z P, BI Y N, WANG Y, et al.Adaptive recursive sliding mode control for surface vessel trajectory tracking with input and output constraints[J]. Control theory & applications, 2020, 37(6): 1419-1427.
[20] 马悦萌, 周荻, 邹昕光. 状态/输入约束下飞-推一体化的保性能安全控制[J]. 宇航学报, 2022, 43(4): 496-507.
MA Y M, ZHOU D, ZOU X G.Integrated flight-propulsion performance-guaranteed safety control with state/input constraints[J]. Journal of astronautics, 2022, 43(4): 496-507.
[21] 陈峣, 谭立国, 魏毅寅, 等. 考虑状态约束的弹性高超声速飞行器自适应饱和容错控制[J]. 宇航学报, 2021, 42(7): 850-861.
CHEN Y, TAN L G, WEI Y Y, et al.Adaptive saturated fault-tolerant tracking control of flexible hypersonic vehicle considering state constraints[J]. Journal of astronautics, 2021, 42(7): 850-861.
[22] 陈中天, 陈强, 孙明轩, 等. 航天器全状态约束输出反馈控制[J]. 控制理论与应用, 2020, 37(2): 355-364.
CHEN Z T, CHEN Q, SUN M X, et al.Full state constrained output feedback control for rigid spacecraft[J]. Control theory & applications, 2020, 37(2): 355-364.
[23] 郑文昊, 贾英民. 具有状态约束与输入饱和的全向移动机器人自适应跟踪控制[J]. 工程科学学报, 2019, 41(9): 1176-1186.
ZHENG W H, JIA Y M.Adaptive tracking control for omnidirectional mobile robots with full-state constraints and input saturation[J]. Chinese journal of engineering, 2019, 41(9): 1176-1186.
[24] LU Y, ZHANG G Q, SUN Z J, et al.Adaptive cooperative formation control of autonomous surface vessels with uncertain dynamics and external disturbances[J]. Ocean engineering, 2018, 167: 36-44.
[25] LIU Y J, LI J, TONG S C, et al.Neural network control-based adaptive learning design for nonlinear systems with full-state constraints[J]. IEEE transactions on neural networks and learning systems, 2016, 27(7): 1562-1571.
[26] SONG Y D, SHEN Z Y, HE L, et al.Neuroadaptive control of strict feedback systems with full-state constraints and unknown actuation characteristics: an inexpensive solution[J]. IEEE transactions on cybernetics, 2018, 48(11): 3126-3134.