Hybrid control for speed sensorless induction motor drive

The dynamic response of a hybrid-controlled speed sensorless induction motor (IM) drive is introduced. First, an adaptive observation system, which comprises speed and flux observers, is derived on the basis of model reference adaptive system (MRAS) theory. The speed observation system is implemented using a digital signal processor (DSP) with a high sampling rate to make it possible to achieve good dynamics. Next, based on the principle of computed torque control, a computed torque controller using the estimated speed signal is developed. Moreover, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a recurrent fuzzy neural network (RFNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Furthermore, based on Lyapunov stability a hybrid control system, which combines the computed torque controller, the RFNN uncertainty observer and a compensated controller, is proposed to control the rotor speed of the sensorless IM drive. The computed torque controller with RFNN uncertainty observer is the main tracking controller and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rules of the RFNN. Finally, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results     

An adaptive recurrent-neural-network motion controller for X-Y table in CNC machine.

In this paper, an adaptive recurrent-neural-network (ARNN) motion control system for a biaxial motion mechanism driven by two field-oriented control permanent magnet synchronous motors (PMSMs) in the computer numerical control (CNC) machine is proposed. In the proposed ARNN control system, a RNN with accurate approximation capability is employed to approximate an unknown dynamic function, and the adaptive learning algorithms that can learn the parameters of the RNN on line are derived using Lyapunov stability theorem. Moreover, a robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vectors, higher-order terms in Taylor series, external disturbances, cross-coupled interference and friction torque of the system. To relax the requirement for the value of lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is investigated. Using the proposed control, the position tracking performance is substantially improved and the robustness to uncertainties including cross-coupled interference and friction torque can be obtained as well. Finally, some experimental results of the tracking of various reference contours demonstrate the validity of the proposed design for practical applications.     

Robust backstepping control for a class of nonlinear systems using generalized disturbance observer

This paper presents a new composite robust controller for a class of uncertain nonlinear systems through backstepping method and disturbance observer (DOB) technique. By designing a generalized disturbance observer (GDOB), the mismatched/matched uncertainty is estimated and compensated at each backstepping step. By virtue of the proposed GDOB, the proposed controller only requires the lumped uncertainties are differentiable and the higher-order derivatives are bounded. With the help of GDOB, the high-gain backstepping controller is also avoided completely. Asymptotical stability of the closed-loop system is established using direct Lyapunov method. An illustrative example of missile autopilot design is given and simulations are carried out to validate the proposed method.     

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 wavelet neural network control with hysteresis estimation for piezo-positioning mechanism

An adaptive wavelet neural network (AWNN) control with hysteresis estimation is proposed in this study to improve the control performance of a piezo-positioning mechanism, which is always severely deteriorated due to hysteresis effect. First, the control system configuration of the piezo-positioning mechanism is introduced. Then, a new hysteretic model by integrating a modified hysteresis friction force function is proposed to represent the dynamics of the overall piezo-positioning mechanism. According to this developed dynamics, an AWNN controller with hysteresis estimation is proposed. In the proposed AWNN controller, a wavelet neural network (WNN) with accurate approximation capability is employed to approximate the part of the unknown function in the proposed dynamics of the piezo-positioning mechanism, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to finite number of wavelet basis functions and disturbances, optimal parameter vectors, and higher order terms in Taylor series. Moreover, adaptive learning algorithms for the online learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. Finally, the command tracking performance and the robustness to external load disturbance of the proposed AWNN control system are illustrated by some experimental results.     

基于混合滑模控制器和反正切观测器的SPMSM直接转矩控制

针对基于传统PI控制的表贴式永磁同步电机(SPMSM)直接转矩控制系统抖振和相位延迟等问题,在转速环节设计新型趋近律,采用模糊自适应方法,实现趋近律参数的动态调节,并通过Lyapunov方法证明稳定性.利用super-twisting滑模策略生成参考电压矢量,完成混合滑模控制器的设计,建立基于反正切函数的滑模观测器,并对转子位置进行合理补偿.仿真实验表明,与PI控制、基于指数趋近律的滑模控制器相比,所设计的控制器在电机空载起动和外加干扰情况下均能有效提高系统响应,显著降低抖振,与其他模型参考自适应观测器相比,所设计观测器能有效减小相位延迟,转子位置辨识结果更准确.     

压电加筋壁板结构的多模态自抗扰振动控制

针对压电加筋壁板结构多模态主动控制时存在振动模型和外界干扰难以确定等问题, 提出一种不依赖结构数学模型的多模态自抗扰振动控制方法. 首先,采用多回路的扩张状态观测器实时估计其他模态的输出叠加、输入耦合、高次谐波以及外界激励等组成的集总干扰, 并将估计值通过前馈补偿的方式消除干扰对整个控制系统的影响. 然后, 针对每个控制模态设计独立的PD反馈控制器. 为了提高整个控制系统的振动抑制性能, 结合多模态振动控制的特点, 引入一种具有实际意义的性能指标函数. 并基于此性能函数, 提出基于logistic映射的自抗扰振动控制器参数自动优化方法. 最后, 利用dSPACE半实物仿真平台, 搭建了四面固支壁板结构的压电振动控制实验系统.最后, 多模态干扰激励的实验结果表明了所提的多模态自抗扰振动主动控制方法的有效性.     

Straight‐Line Tracking Control of an Agricultural Vehicle with Finite‐Time Control Technique

For the agricultural vehicle straight‐line tracking system, three control algorithms based upon the finite‐time control technique have been proposed to force the vehicle to track a straight line. Without considering the lumped disturbance, a backstepping‐like finite‐time state‐feedback controller is first developed. On this basis, an adaptive state‐feedback controller in conjunction with integral sliding mode is further developed in the presence of the lumped disturbance. Finally, a sliding mode disturbance observer is given to estimate the lumped disturbance, and the composite control scheme is presented. Under the composite controller, the lumped disturbance can be compensated and thus the disturbance rejection property has been significantly improved. Simulation results verify the proposed control algorithms.     

Robust nonlinear control of a three-tank system using finite-time disturbance observers

A finite-time disturbance observer-based robust control method is proposed for output tracking of the Inteco threetank system in the presence of mismatched uncertainties. The controller is designed in a backstepping manner. At each step of the virtual controller design, a robust feedback controller with some effective nonlinear damping terms is designed so that the system states remain in the feasible domain. The nonlinear uncertainty is compensated by a finite-time disturbance observer. And to avoid the shortcoming of “explosion of terms”, the dynamic surface control technique which employs a low-pass filter is adopted at each step of the virtual controller design. Attention is paid to reducing the measurement noise effects and to initialization technique of the system states and reference output trajectory. Theoretical analysis is performed to clarify the control performance. And the theoretical results are verified based on the experimental studies on the real Inteco three-tank system.     

Eso‐Based Adaptive Robust Control of Dual Motor Driving Servo System

Based on extended state observer (ESO), we propose an adaptive robust control (ARC) for a dual motor driving servo system, in which there exist nonlinearities affecting control performance. To apply ESO and estimate the lumped uncertainty online, backlash and friction are analyzed and the nonlinear model of the plant is derived. We achieve several control objectives. First, the bias torque is considered in order to eliminate the effect of backlash. Second, the speed feedback is used to maintain the speed synchronization of motors. Then, to achieve feedforward control, finite‐time ESO is designed to estimate the unknown nonlinearities online. Furthermore, the ESO‐based adaptive robust controller is designed to guarantee L_∞ of tracking error by an initialization method, maintaining the transient performance of tracking behavior. Finally, extensive experimental results on a practical test rig validate the effectiveness of our proposed method.     

Adaptive control of slider-crank mechanism motion: simulations and experiments

The dynamic motion of an adaptive controlled slider-crank mechanism, which is driven by a permanent magnet ( PM) synchronous servo-motor, is studied. First, the mathematical model of the motor-mechanism coupling system is developed, where Hamilton's principle and the Lagrange multiplier method are applied to formulate the equation of motion. Then, by using the stability analysis with inertia-related Lyapunov function, an adaptive controller for the motor-mechanism coupling system is obtained. Simulation and experimental results show that the dynamic behaviours of the proposed controller-motor-mechanism system are robust with regard to parametric variations and external disturbances.     

A study of robust controller and observer design method for a rolling object and beam system

This paper deals with a robust control problem for a ball and beam system with an uncertainty. At first, we clarify the effect of a measurement error in the beam rotational angle and its differential value. It is expressed as additional terms in the state and input matrix of the state equation, which is called as a structured uncertainty. For this system, a robust tracking controller and a robust state observer are applied. Both components are designed based on a robust control, which is called guaranteed cost control. This is a kind of cost minimization method for designing problem of a linear feedback system. The controller is obtained as the solution of the matrix algebraic Riccati like equation, which is referred to as stochastic algebraic Riccati equation. The nominal system performance is degraded due to the influence of the uncertainty, but it is able to design a system with robustness for disturbance. Next, we consider the design problem of a state observer to estimate all state variables form the output signal easy to measure, like rotation angle of the beam. For the design problem of the state observer, we consider dual problem of the original system. Consequently, our proposed system estimates the whole state vector, and using estimated state values, it controls the ball to the desired position. Both components have robustness for the uncertainty. Numerical result shows the validity of our proposed method.     

基于模糊滑模控制器和两级滤波观测器的永磁 同步电机无位置传感器混合控制

针对传统带有滑模观测器的永磁同步电机控制系统中的转矩脉动大、抖振明显、反电动势估计精度差等 问题, 在速度环提出了基于双曲正弦函数的新型趋近率, 结合模糊控制思想对趋近率参数实现自整定, 设计了一种 基于新型趋近率的模糊积分滑模速度环控制器; 同时, 在滑模观测器中提出基于变截止频率低通滤波器和修正反 电动势观测器的两级滤波结构来抑制反电动势中的高频分量和纹波分量, 并对转子位置进行合理补偿, 设计了两级 滤波滑模观测器; 通过Lyapunov判据对本文提出的控制策略的稳定性进行了推导证明. 仿真结果表明, 与传统滑模 观测器相比, 本文控制器可使电机在启动和受到外部扰动时系统响应良好.     

多星发射上面级主动抗扰姿态控制技术研究

针对多星发射的运载火箭上面级在入轨段由于卫星分离产生的质量偏移,从而引起的三轴姿态严重耦合问题展开了研究,提出了基于广义扩张状态观测器的改进预测函数控制姿态控制方法.该控制方法将质心偏移造成上面级结构参数偏差和引入的干扰力矩以及其他未知系统参数偏差、外界扰动和未建模动态视为集总扰动,并将该非匹配干扰通过等效输入扰动技术转换为匹配干扰,由广义扩张状态观测器对变化后的系统状态和未知集总扰动同时观测.将集总扰动在反馈回路予以补偿保证了预测模型与上面级真实动力学模型有较高匹配度,进一步保证预测函数控制有较高的控制跟踪精度和较快的响应速度,并对外界扰动和参数偏差有较强的鲁棒性.文中算例仿真和性能对比验证了该方法的有效性及可行性.     

High-order sliding-mode observer based output feedback adaptive robust control of a launching platform with backstepping

A kind of launching platform driven by two permanent magnet synchronous motor (PMSM) motors which is used to launch kinetic load to hit the target, always faces strong parameter uncertainties and strong external disturbance such as the air current impulsion, which would degrade their tracking accuracy greatly. In this paper, an adaptive robust nonlinear controller is proposed for high-accuracy motion control of the launching platform, in which the adaption law is designed to estimate the unknown coupling coefficients of torque disturbance and feed-forward cancellation technique is used to compensate the coupling torque disturbance and some other constant disturbances. In addition, a nonlinear robust feedback term is designed to inhibit the influence of the parameter estimation error and the other model uncertainty to stabilise the closed-loop system. Considering that some system states are immeasurable due to cost-reduction, volume/weight limitations and structure restriction or heavy measurement noise is usually associated with the measurements, which may also deteriorate the achievable performance of full-state feedback controllers; a high-order sliding-mode observer is used to estimate the unmeasured system states, and it is synthesised with the adaptive robust controller via feed-forward cancellation method. The intermediary virtual control law and the final control law are derived by integrating the backstepping method. Furthermore, the controller theoretically guarantees a prescribed tracking transient performance and final tracking accuracy while achieving asymptotic tracking performance in the presence of parametric uncertainties only, which is very important for high-accuracy tracking control of launching platform. Extensive comparative experimental results are obtained to verify the high performance nature of the proposed control strategy.     

Wavelet reduced order observer based adaptive tracking control for a class of uncertain nonlinear systems using reinforcement learning

This Paper investigates the mean to design the reduced order observer and observer based controllers for a class of uncertain nonlinear system using reinforcement learning. A new design approach of wavelet based adaptive reduced order observer is proposed. The proposed wavelet adaptive reduced order observer performs the task of identification of unknown system dynamics in addition to the reconstruction of states of the system. Reinforcement learning is used via two wavelet neural networks (WNN), critic WNN and action WNN, which are combined to form an adaptive WNN controller. The “strategic” utility function is approximated by the critic WNN and is minimized by the action WNN. Owing to their superior learning capabilities, wavelet networks are employed in this work for the purpose of identification of unknown system dynamics. Using the feedback control, based on reconstructed states, the behavior of closed loop system is investigated. By Lyapunov approach, the uniformly ultimate boundedness of the closed-loop tracking error is verified. A numerical example is provided to verify the effectiveness of theoretical development.     

一类高阶非线性系统的级联自抗扰控制

针对类似板球系统的一类高阶、强耦合、不确定非线性系统,利用backstepping算法的思想,提出以多个低阶自抗扰控制器级联实现控制的方法.通过各低阶自抗扰控制器的扩张状态观测器观测出各级对象的内外扰动,然后进行补偿,较好地解决了高阶非线性系统的不确定性、耦合性及干扰抑制问题.以板球系统的轨迹跟踪控制为例进行研究,所得结果表明,该方案适用于复杂高阶非线性级联系统的控制,具有良好的动态特性及鲁棒性.     

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 Robust Backstepping Sensorless Control for Interior Permanent Magnet Synchronous Motor Using a Super‐Twisting Based Torque Observer

In order to achieve high‐performance speed regulation for sensorless interior permanent magnet synchronous motors (IPMSMS), a robust backstepping sensorless control is presented in this paper. Firstly, instead of a real mechanical sensor, a robust terminal sliding mode observer is used to provide the rotor position. Then, a new super‐twisting algorithm (STA) based observer is designed to obtain estimates of load torque and speed. The proposed observer ensures finite‐time convergence, maintains robust to uncertainties, and eliminates the common assumption of constant or piece‐wise constant load torque. Finally, a sensorless scheme is designed to realize speed control despite parameter uncertainties, by combining the robust backstepping control with sliding mode actions and the presented sliding mode observers. The stability of the observer and controller are verified by using Lyapunov's second method to determine the design gains. Simulation results show the effectiveness of the proposed approach.     

Inverse dynamics based robust control method for position commanded servo actuators in robot manipulators

In this paper, a simple torque to position conversion method is proposed for position commanded servo actuators used in robot manipulators. The torque to position conversion is based on the low level controller of the servomotor. The proposed conversion law is combined with a backstepping sliding mode control method to realize a robust dynamic controller. The proposed torque based method can control a servomotor which can otherwise be operated only through position inputs. This method facilitates dynamic control for position controlled servomotors and it can be extended to position commanded robotic manipulators also. Simulation and experimental studies are conducted to validate the proposed torque to position conversion based robust control method.