Robust control for nonlinear motor-mechanism coupling system using wavelet neural network

A robust controlled toggle mechanism, which is driven by a permanent magnet (PM) synchronous servo motor is studied in this paper. First, based on the principle of computed torque control, a position controller is developed for the motor-mechanism coupling system. Moreover, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a wavelet neural network (WNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Furthermore, based on the Lyapunov stability a robust control system, which combines the computed torque controller, the WNN uncertainty observer and a compensated controller is proposed to control the position of the motor-mechanism coupling system. The computed torque controller with WNN uncertainty observer is the main tracking controller, and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer. Finally, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed robust control system are robust with regard to parametric variations and external disturbances.     

Adaptive interconnected observer-based backstepping control design for sensorless induction motor

In this paper, an observer-based controller scheme is designed to robustly drive a sensorless Induction Motor (IM) even for the case of low frequencies with unknown load torque. Combining the field oriented control strategy with the backstepping control method, we introduce additional integral terms to improve the robustness properties of the controller in spite of uncertainties and perturbations. The estimation of the fluxes, the speed, the load torque and moreover the stator resistance is given by an adaptive interconnected observer. The practical stability of the proposed observer–controller scheme is proved. Finally, some experimental test results are given on the framework of a specific sensorless induction motor benchmark.     

A robust sensorless output feedback controller of the induction motor drives: new design and experimental validation

In this article, a sensorless output feedback controller is designed in order to drive the induction motor (IM) without the use of flux and speed sensors. First, an observer that uses only the measured stator currents is synthesised to estimate the mechanical variables (speed and load torque) and the magnetic variables (fluxes) by structurally taking into account the unobservability phenomena of the sensorless IM (SIM) and the parametric uncertainties. Second, a current-based field-oriented sliding mode control that uses the flux and the speed estimates given by the former observer is developed so as to steer the estimated speed and flux magnitude to the desired references. Since the observer error dynamic is independent from the known input control and depends on the IM parametric uncertainties, a kind of separation principle is introduced to guarantee the practical stability of the whole closed-loop system ‘observer–controller’ (‘O-C’) according to observability and unobservability time variation. A significant benchmark taking into account the unobservability phenomena of the SIM is presented to show the performances of the whole control scheme against experimental set-up.     

基于滑模模型参考自适应观测器的无速度传感器三相永磁同步电机模型预测转矩控制

针对三相永磁同步电机(PMSM)驱动系统,基于滑模变结构模型参考自适应(MRAS)技术,提出了一种新颖的无速度传感器模型预测转矩控制(MPTC)策略.采用滑模变结构模型参考自适应方法构造电机转速观测器,以改善速度估计精度并提高系统鲁棒性;利用模型预测转矩控制策略,以达到减小转矩和磁链纹波并提高系统控制性能的目的.仿真结果表明:就滑模MRAS观测器与MRAS观测器比较而言,基于前者的PMSM无速度传感器MPTC系统比基于后者的PMSM无速度传感器MPTC系统具有较强的鲁棒性和更好的动态性能;就MPTC与直接转矩控制(DTC)和磁场定向控制(FOC)比较而言,采用前者策略的无速度传感器电机驱动系统能够降低逆变器开关频率、减少相电流总谐波失真(THD),从而提高系统可靠性.     

Adaptive interconnected observer for sensorless induction motor

An adaptive interconnected observer for induction motor (IM) drive without mechanical sensors (speed sensor and load torque sensor) is presented. The observer estimates the fluxes, the angular velocity, the load torque and the stator resistance even under or near unobservable conditions. Practical stability based on Lyapunov theory is proved to guarantee the strongly uniformly practical stability of the estimation error dynamics. A contribution of this article is the experimental validation of the observer on reference trajectories of a sensorless IM observer benchmark. The trajectories of this benchmark are chosen to test the motor near and under conditions of unobservability. Robustness with respect to parameters variations is proved and experimentally verified.     

SLIDING‐MODE LINEARIZATION TORQUE CONTROL OF AN INDUCTION MOTOR

This paper proposes a sliding‐mode linearization torque control (SMLTC) for an induction motor (IM). An ideal feedback linearization torque control method is firstly adopted in order to decouple the torque and flux amplitude of the IM. However, the system parameters are uncertainties, which will influence the control performance of the IM in practical applications. Hence, to increase the robustness of the IM drive for high‐ performance applications, this SMLTC aims to improve the immunity of those uncertainties. We modify the flux observer of Benchaib and Edwards [15] by means of the adaptive sliding‐mode method. This not only eliminates the estimation of the uncertainty bounds, but also improves the performance of sliding control. In addition, a practical application of the proposed SMLTC, with a model reference adaptive control (MRAC) scheme incorporated as the inner and outer loop controller used for position control, is also presented. Some experiments are presented to verify the control theory and demonstrate the robustness and effectiveness of the proposed SMLTC.     

基于DSP的自适应速度辨识直接转矩控制系统研究

异步电机直接转矩控制能产生快速且良好的鲁棒性响应,采用自适应磁链观测器,取代传统的积分器,构造了新型的速度估计器,并结合模糊控制器,实现对定子磁链准确观测和系统无速度传感器运行状态。基于DSP(TMS320LF2407A)核心芯片建立数字化控制系统。仿真与实验表明,该系统对电机定子磁链的观测精度高,转速估算准确,尤其在低速下能保持很高的性能。     

Adaptive control strategy with flux reference optimization for sensorless induction motors

Avoiding mechanical (speed, torque) sensors in electric motor control entails cost reduction and reliability improvement. Furthermore, sensorless controllers (also referred to output-feedback) are useful, even in the presence of mechanical sensors, to implement fault tolerant control strategies. In this paper, we deal with the problem of output-feedback control for induction motors. The solutions proposed so far have been developed based on the assumption that the machine magnetic circuit characteristic is linear. Ignoring magnetic saturation makes it not possible to meet optimal operation conditions in the presence of wide range speed and load torque variations. Presently, an output-feedback control strategy is developed on the basis of a motor model that accounts for magnetic saturation. The control strategy includes an optimal flux reference generator, designed in order to optimize energy consumption, and an output-feedback designed using the backstepping technique to meet tight speed regulation in the presence of wide range changes in speed reference and load torque. The controller sensorless feature is achieved using an adaptive observer providing the controller with online estimates of the mechanical variables. Adaptation is resorted to cope with the system parameter uncertainty. The controller performances are theoretically analyzed and illustrated by simulation.     

无位置传感器的永磁同步电动机反演控制

设计了一种基于无位置传感器的永磁同步电动机(PMSM)反演控制器.该控制器包括基于定子电流检测的速度观测器与反演速度控制器.速度观测器代替位置传感器实现转速的在线估计;反演控制器的设计确保速度控制系统具有快速的转速跟踪与转矩响应.通过设计全局Lyapunov函数保证了控制系统的稳定性.仿真和试验结果表明:基于速度观测器...     

Sensorless Inverter‐Fed Compressor Drive System Using Back‐EMF Estimator with PIDNN Torque Observer

A saliency back‐EMF estimator with a proportional–integral–derivative neural network (PIDNN) torque observer is proposed in this study to improve the speed estimating performance of a sensorless interior permanent magnet synchronous motor (IPMSM) drive system for an inverter‐fed compressor. The PIDNN torque observer is proposed to replace the conventional proportional–integral–derivative (PID) torque observer in a saliency back‐EMF estimator to improve the estimating performance of the rotor flux angle and speed. The proposed sensorless control scheme use square‐wave type voltage injection method as the start‐up strategy to achieve sinusoidal starting. When the motor speed gradually increases to a preset speed, the sensorless drive will switch to the conventional saliency back‐EMF estimator using the PID observer or the proposed saliency back‐EMF estimator using the PIDNN observer for medium and high speed control. The theories of the proposed saliency back‐EMF rotor flux angle and speed estimation method are introduced in detail. Moreover, the network structure, the online learning algorithms and the convergence analyses of the PIDNN are discussed. Furthermore, a DSP‐based control system is developed to implement the sensorless inverter‐fed compressor drive system. Finally, some experimental results are given to verify the feasibility of the proposed estimator.     

A survey on sliding mode control strategies for induction motors

A state of the art review of control and estimation methods for induction motor (IM) based on conventional approaches, sliding mode control (SMC) and sensorless SMC is presented. The objective of this survey paper is to summarize the different control approaches for IMs including field oriented control (FOC), direct torque control (DTC), speed observer, observer based flux estimation, sliding mode (SM) flux and speed observer, current regulation by SMC, sensorless SMC, etc. The applications of SMC to IMs has been widespread in recent years. The increasing interest in SMC is because of its interesting features such as invariance, robustness, order reduction and control chattering. Particularly robustness of SM approach with respect to parameter variations and external disturbance is vital for the control system. The review covers the sensorless SMC schemes by integrating controller and observer design to guarantee convergence of the estimates to the real states. It also covers the chattering problems, encountered often in SMC area dealt by using an asymptotic observer.     

基于嵌套自适应观测器的 连铸结晶器振动位移系统有限时间容错控制

针对连铸结晶器振动位移系统存在伺服电机驱动单元等执行器故障和负载转矩扰动问题, 本文提出一种基于嵌套自适应观测器的有限时间容错策略. 首先, 设计一种嵌套自适应观测器在线估计由执行器故障和负载转矩扰动构成的综合不确定项; 其次, 采用分层设计与终端滑模相结合的方法, 分别对位移子系统和电流环子系统设计全阶滑模控制器(FOSMC)和终端滑模控制器补偿综合不确定项, 并通过引入一阶低通滤波器来提高控制信号的连续性. 理论分析表明, 本文所提容错控制策略能够保证闭环系统所有状态有限时间稳定; 最后, 通过仿真对比研究验证了本文所提控制策略的有效性.     

A new instantaneous reactive power based MRAS for sensorless induction motor drive

A model reference adaptive system (MRAS) based speed estimator for sensorless induction motor (IM) drive is proposed in this paper. The MRAS is formed with instantaneous reactive power and the estimated stator current vector. Current, being a vector quantity, is configured in terms of reactive power, which is a scalar quantity. The advantage is that we need not equate either or both the in-phase and quadrature components of the current vector. The performance of the estimator under regeneration is an important aspect, which is studied in this paper through the small signal analysis. Graphical representation in the speed–torque domain gives a clear idea about the stable and unstable zones of operation in the regenerating mode. Sensorless IM drive along with the proposed MRAS is verified through computer simulation.     

基于ESO的无速度传感器PMSM系统自适应滑模FCS-MPC

为了提高三相永磁同步电机(PMSM)控制系统的性能,基于反双曲正弦函数的扩张状态观测器(ESO)技术,提出一种新颖的无速度传感器自适应滑模有限控制集模型预测控制(FCS-MPC)策略,采用ESO技术构造PMSM系统转速和反电动势的观测器,实现对电机转速和反电动势快速准确估计.用带有负载ESO的自适应滑模控制作为系统的转速调节器,以提高系统的鲁棒性;利用基于快速矢量选择的FCS-MPC策略,达到减少转矩脉动、降低系统算法计算量的目的.仿真结果表明,基于ESO的无速度传感器自适应滑模FCS-MPC策略能够使PMSM系统可靠稳定运行,达到满意的转矩和转速控制效果.与基于积分型滑模面的自适应滑模FCS-MPC策略相比,所提出的控制策略能使系统具有良好的动态性能和抗负载干扰能力.     

Hybrid supervisory control using recurrent fuzzy neural network fortracking periodic inputs

A hybrid supervisory control system using a recurrent fuzzy neural network (RFNN) is proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive for the tracking of periodic reference inputs. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMLSM. Then, a hybrid supervisory control system, which combines a supervisory control system and an intelligent control system, is proposed to control the mover of the PMLSM for periodic motion. The supervisory control law is designed based on the uncertainty bounds of the controlled system to stabilize the system states around a predefined bound region. Since the supervisory control law will induce excessive and chattering control effort, the intelligent control system is introduced to smooth and reduce the control effort when the system states are inside the predefined bound region. In the intelligent control system, the RFNN control is the main tracking controller which is used to mimic a idea control law and a compensated control is proposed to compensate the difference between the idea control law and the RFNN control. The RFNN has the merits of fuzzy inference, dynamic mapping and fast convergence speed, In addition, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The proposed hybrid supervisory control system using RFNN can track various periodic reference inputs effectively with robust control performance.     

Adaptive hybrid control system using a recurrent RBFN-based self-evolving fuzzy-neural-network for PMSM servo drives

In this paper, an adaptive hybrid control system (AHCS) based on the computed torque control for permanent-magnet synchronous motor (PMSM) servo drive is proposed. The proposed AHCS incorporating an auxiliary controller based on the sliding-mode, a recurrent radial basis function network (RBFN)-based self-evolving fuzzy-neural-network (RRSEFNN) controller and a robust controller. The RRSEFNN combines the merits of the self-evolving fuzzy-neural-network, recurrent-neural-network and RBFN. Moreover, it performs the structure and parameter-learning concurrently. Furthermore, to relax the requirement of the lumped uncertainty, an adaptive RRSEFNN uncertainty estimator is used to adaptively estimate the non-linear uncertainties online, yielding a controller that tolerate a wider range of uncertainties. Additionally, a robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vector and higher order term in Taylor series. The online adaptive control laws are derived based on the Lyapunov stability analysis, so that the stability of the AHCS can be guaranteed. A computer simulation and an experimental system are developed to validate the effectiveness of the proposed AHCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the AHCS grants robust performance and precise dynamic response regardless of load disturbances and PMSM uncertainties.     

Adaptive Backstepping Control of Six‐Phase PMSM Using Functional Link Radial Basis Function Network Uncertainty Observer

An adaptive backstepping control (ABSC) using a functional link radial basis function network (FLRBFN) uncertainty observer is proposed in this study to construct a high‐performance six‐phase permanent magnet synchronous motor (PMSM) position servo drive system. The dynamic model of a field‐oriented six‐phase PMSM position servo drive is described first. Then, a backstepping control (BSC) system is designed for the tracking of the position reference. Since the lumped uncertainty of the six‐phase PMSM position servo drive system is difficult to obtain in advance, it is very difficult to design an effective BSC for practical applications. Therefore, an ABSC system is designed using an adaptive law to estimate the required lumped uncertainty in the BSC system. To further increase the robustness of the six‐phase PMSM position servo drive, an FLRBFN uncertainty observer is proposed to estimate the lumped uncertainty of the position servo drive. In addition, an online learning algorithm is derived using Lyapunov stability theorem to learn the parameters of the FLRBFN online. Finally, the proposed position control system is implemented in a 32‐bit floating‐point DSP, TMS320F28335. The effectiveness and robustness of the proposed intelligent ABSC system are verified by some experimental results.     

Hybrid control using recurrent fuzzy neural network for linearinduction motor servo drive

A hybrid control system using a recurrent fuzzy neural network (RFNN) is proposed to control a linear induction motor (LIM) servo drive. First, feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, a hybrid control system is proposed to control the mover of the LIM for periodic motion. In the hybrid control system, the RFNN controller is the main tracking controller, which is used to mimic a perfect control law, and the compensated controller is proposed to compensate the difference between the perfect control law and the RFNN controller. Moreover, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system     

模糊滑模控制和负载转矩补偿的异步电动机直接转矩控制

针对异步电动机(IM)转矩脉动以及抗干扰能力差的问题,设计了基于模糊滑模控制(FSMC)与负载转矩补偿的新型直接转矩控制(DTC),取代传统PID速度调节器的是一种滑模控制器.为解决滑模控制器中负载转矩脉动的问题,用模糊逻辑控制器取代了传统滑模控制律中的不连续部分,可以明显降低异步电动机在低速运转时的转矩脉动.提出了一种负载转矩观测器来估计未知的负载转矩.负载转矩观测器用来估计负载转矩扰动,估计作为速度环的前馈补偿.仿真结果表明:在低速负载转矩扰动时,该设计具有更好的动态响应和速度性能、更高鲁棒性和更强的抗干扰能力.     

Sensorless speed control of PMSM via adaptive interconnected observer

In this article, a robust sensorless speed observer–controller scheme for a surface permanent magnet synchronous motor (PMSM) is proposed. First-of-all, following preliminary results in the framework of the induction motor that is less sensible to the position estimation error, an adaptive high gain interconnected observer is designed. It is only supplied by the electrical measurement: the motor currents and voltages. This observer estimates the rotor speed and position, the stator resistance and the load torque. A nonlinear backstepping controller is developed. The above observer is associated with this controller and the complete scheme practical stability proof is given. The overall system is tested by simulation in the framework of an industrial benchmark with a trajectory that particularly checks the motor unobservability condition. Some robustness tests have been carried out. The controller–observer scheme shows good performances in spite of the uncertainties and the unknown load torque.