Design of Total Sliding-Mode-Based Genetic Algorithm Control for Hybrid Resonant-Driven Linear Piezoelectric Ceramic Motor

This study presents a total sliding-mode-based genetic algorithm control (TSGAC) system for a linear piezoelectric ceramic motor (LPCM) driven by a newly designed hybrid resonant inverter. First, the motor configuration and driving circuit of an LPCM are introduced, and its hypothetical dynamic model is represented by a nonlinear function with unknown system parameters. In the hybrid resonant drive system, it has the merits of the high voltage gain from a parallel-resonant current source, and the invariant output characteristic from a two-inductance two-capacitance resonant driving circuit. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, a TSGAC system is therefore investigated based on direction-based genetic algorithm with the spirit of total sliding-mode control (TSC) and fuzzy-based evolutionary procedure to achieve high-precision position control under a wide operation range. In this control scheme, a GAC system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TSC without strict constraints and detailed system knowledge. In addition, the effectiveness of the proposed drive and control system is verified by numerical simulations and experimental results in the presence of uncertainties     

A fuzzy neural network controller for parallel-resonant ultrasonicmotor drive

A newly designed driving circuit for the traveling-wave-type ultrasonic motor (USM), which consists of a push-pull DC-DC power converter and a current-source two-phase parallel-resonant inverter, is presented in this study. Moreover, since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, a fuzzy neural network (NN) controller is proposed to control the USM drive system. In the proposed controller, a fuzzy model-following controller is implemented to control the rotor position of the USM, and an online trained NN with variable learning rates is implemented to tune the output scaling factor of the fuzzy controller. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the desired variable learning rates. From the experimental results, accurate tracking response can be obtained by the proposed controller, and the influences of parameter variations and external disturbances on the USM drive also can be reduced effectively     

Fuzzy adaptive model-following position control for ultrasonicmotor

A fuzzy adaptive model following mechanism for the position control of a traveling-wave-type ultrasonic motor (USM) is described in this study. Since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, fuzzy adaptive control is applied to design the position controller of the USM for high-performance applications. The driving circuit for the USM, which is composed of a high-frequency boost DC-DC chopper, a two-phase series-loaded resonant inverter and an inner speed loop, is built first. Next, the control algorithm for fuzzy adaptive control is discussed. Then, a simple linear position control loop is designed and augmented by the model-following error-driven fuzzy adaptive mechanism to reduce the influence of parameter variations. The effectiveness of the proposed controller is demonstrated by some experimental results     

基于e-修正神经网络的直接自适应控制设计

针对一般非线性不确定系统设计了一种e-修正神经网络直接自适应控制方法。首先采用虚拟控制量的方法,并将其分解成参考模型输出、线性动态补偿输出与神经网络自适应输出三项;然后针对传统σ-修正神经网络在权值更新时的不足,设计了一种基于e-修正方法的权值自适应更新律,并设计了输出反馈误差观测器用以对神经网络进行训练;最后对基于σ-修正与e-修正两种权值自适应更新律进行仿真对比。仿真结果表明基于e-修正神经网络方法在跟踪误差、不确定性逼近等效果上均优于基于σ-修正神经网络方法。     

Development of new training algorithms for neuro-wavelet systems on the robust control of induction servo motor drive

A robust wavelet neural network control (RWNNC) system is proposed to control the rotor position of an induction servo motor drive in this paper. In the proposed RWNNC system, a wavelet neural network controller is the main tracking controller that is used to mimic a computed torque control law, and a robust controller is designed to recover the residual approximation for ensuring the stable control performance. Moreover, to relax the requirement for a known bound on lumped uncertainty, which comprises a minimum approximation error, optimal network parameters and higher order terms in a Taylor series expansion of the wavelet functions, an RWNNC system with adaptive bound estimation was investigated for the control of an induction servo motor drive. In this control system, a simple adaptive algorithm was utilized to estimate the bound on lumped uncertainty. In addition, numerical simulation and experimental results due to periodic commands show that the dynamic behaviors of the proposed control systems are robust with regard to parameter variations and external load disturbance.     

Robust cerebellar model articulation controller design for unknown nonlinear systems

In this study, a robust cerebellar model articulation controller (RCMAC) is designed for unknown nonlinear systems. The RCMAC is comprised of a cerebellar model articulation controller (CMAC) and a robust controller. The CMAC is utilized to approximate an ideal controller, and the weights of the CMAC are on-line tuned by the derived adaptive law based on the Lyapunov sense. The robust controller is designed to guarantee a specified H/sup /spl infin// robust tracking performance. In the RCMAC design, the sliding-mode control method is utilized to derive the control law, so that the developed control scheme has more robustness against the uncertainty and approximation error. Finally, the proposed RCMAC is applied to control a chaotic circuit. Simulation results demonstrate that the proposed control scheme can achieve favorable tracking performance with unknown the controlled system dynamics.     

Adaptive gain scheduling with feedforward control system based on system model for PMSM

A feedforward controller for permanent magnet synchronous motor (PMSM) has been proposed in this study, and proportional and integral gain could be self-adaptive under different operating conditions. The control structure used in the feedforward system is the same as in the feedback control system. This control structure could guarantee independence of the speed command input to output with the disturbance input to output, which makes the system have better reference trajectory tracking and disturbances rejection. In order to obtain optimal control performance when the parameters are uncertain, a gain scheduling adaptive controller is used in the feedforward system. The proposed controller has been verified by the experimental and simulation results with less steady-state error and better dynamic response than the controllers without it under the condition of external load torque disturbance and PMSM parameter uncertainties.     

基于模糊自适应的智能车驱动与转向协同控制研究

为改善智能车驱动电机调速与舵机转向的协调性,简化调参适配步骤,提出了基于MK60FN1(MK60)芯片的驱动与转向协同控制的模糊自适应控制方案。MK60计算出摄像头拍摄图像中车体与车道中线的位置偏差和角度偏差,根据位置偏差与舵机角度、角度偏差与测量到的车速,采用局部参数优化理论设计模糊自适应控制算法实时调整驱动电机和舵机的可调增益实现协同控制。与驱动、转向分开独立控制的策略相比较,本方案减小了稳态误差,智能车能够更快完成自主循迹,稳定性更好。     

Adaptive Neuro-Wavelet Control for Switching Power Supplies

The switching power supplies can convert one level of electrical voltage into another level by switching action. They are very popular because of their high efficiency and small size. This paper proposes an adaptive neuro-wavelet (ANW) control system for the switching power supplies. In the ANW control system, a neural controller is the main controller used to mimic an ideal controller and a compensated controller is designed to recover the residual of the approximation error. In this study, an online adaptive law with a variable optimal learning-rate is derived based on the Lyapunov stability theorem, so that not only the stability of the system can be guaranteed but also the convergence of controller parameters can be speeded up. Then, the proposed ANW control system is applied to control a forward switching power supply. Experimental results show that the proposed ANW controller can achieve favorable regulation performance for the switching power supply even under input voltage and load resistance variations     

Robust adaptive control of sawyer motors without current measurements

We address nonlinear robust adaptive dynamic output feedback of voltage-fed dual-axis linear stepper (Sawyer) motors using a detailed motor model with electrical dynamics and significant uncertainties and disturbances. A coordinate transformation is proposed to decouple the model into three third-order subsystems along with an appended fifth-order subsystem. The controller utilizes only position and velocity measurements in each axis and achieves practical stabilization of position tracking errors. Adaptations are utilized so as not to require any knowledge of electromechanical system parameters. The controller is robust to load torques, friction, cogging forces, and other disturbances satisfying certain bounds. The controller corrects for the yaw rotation to achieve synchrony of motor and platen teeth.     

Robust control using neural network uncertainty observer for linearinduction motor servo drive

A robust controller, that combines the merits of integral-proportional (IP) position control and neural network (NN) observed technique, is designed for a linear induction motor (LIM) servo drive in this study. First, the secondary flux of the LIM is estimated using a sliding-mode flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust and the flux amplitude of the LIM. Then, the IP position controller is designed according to the estimated mover parameters to match the time-domain command tracking specifications. Moreover, a robust controller is formulated using the NN uncertainty observer, which is implemented to estimate the lumped uncertainty of the controlled plant, as an inner-loop force controller to increase the robustness of the LIM servo drive system. Furthermore, in the derivation of the online training algorithm of the NN, an error function is used in the Lyapunov function to avoid the real-time identification of the system Jacobian. In addition, to increase the speed and accuracy of the estimated flux, the sliding-mode flux observer is implemented using a 32 bit floating-point digital signal processor (DSP) with a high sampling rate. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results     

Adaptive backstepping control and friction compensation for AC servo with inertia and load uncertainties

An adaptive backstepping control with friction compensation scheme is presented. A third-order linear dynamic model is used for the AC motor control system design while the LuGre dynamic friction model with nonuniform friction force variations characterizes the friction force. Nonlinear adaptive control laws are designed to compensate the unknown system parameters and disturbances. System robustness and asymptotic position tracking performance are shown through simulation and experimental results.     

Adaptive backstepping control using recurrent neural network forlinear induction motor drive

An adaptive backstepping control system using a recurrent neural network (RNN) is proposed to control the mover position of a linear induction motor (LIM) drive to compensate the uncertainties including the friction force in this paper. First, the dynamic model of an indirect field-oriented LIM drive is derived. Then, a backstepping approach is proposed to compensate the uncertainties including the friction force occurred in the motion control system. With the proposed backstepping control system, the mover position of the LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the LIM drive, an RNN uncertainty observer is proposed to estimate the required lumped uncertainty in the backstepping control system. In addition, an online parameter training methodology, which is derived using the gradient-descent method, is proposed to increase the learning capability of the RNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results     

Robust adaptive tracking control of uncertain electrostatic micro-actuators with H-infinity performance

A novel adaptive robust tracking control scheme is proposed for a class of single-degree-of-freedom (1DOF) electrostatic micro-actuator systems in the presence of parasitics, parameter uncertainties and external disturbances. This method integrates the adaptive dynamic surface control and H-infinity control techniques. Based on this method, both the design procedure and the derived tracking controller itself are simplified, and the controller guarantees that the output tracking error satisfies the H-infinity tracking performance. In addition, the tracking accuracy can be adjusted by an appropriate choice of the design parameters of the controller. Simulation results show that prescribed transient output tracking performance can be achieved, and the closed-loop system exhibits good robustness to system uncertainties.     

Position Control of an Interior Permanent-Magnet Synchronous Motor Without Using a Shaft Position Sensor

This paper proposes a novel sensorless position control system for an interior permanent-magnet synchronous motor. In this paper, a novel rotor position/velocity estimation technique is proposed. This estimation technique only relates to the slopes of the stator currents and does not relate to the parameters or operating conditions of the motor. Neither an extra circuit nor an external high-frequency exciting signal is required here as compared to other position estimation techniques. In addition, the proposed estimator works well in transient, steady-state, and standstill conditions. As a result, the proposed method is very robust and useful. To improve the performance of the position-control system, an optimal controller is proposed. By using this controller, a fast transient response, good load disturbance rejection capability, and satisfactory tracking ability can be achieved. A digital signal processor, TMS-320-LF-2407, is used to execute the rotor position/velocity estimation, the current-loop control, the velocity-loop control, and the position-loop control. As a result, a fully digital position-control system is achieved. Several experimental results validate the theoretical analysis.     

Adaptive positioning control for a LPMSM drive based on adapted inverse model and robust disturbance observer

The adaptive robust positioning control for a linear permanent magnet synchronous motor drive based on adapted inverse model and robust disturbance observer is studied in this paper. First, a model following two-degrees-of-freedom controller consisting of a command feedforward controller (FFC) and a feedback controller (FBC) is developed. According to the estimated motor drive dynamic model and the given position tracking response, the inner speed controller is first designed. Then, the transfer function of FFC is found based on the inverse model of inner speed closed-loop and the chosen reference model. The practically unrealizable problem possessed by traditional feedforward control is avoided by the proposed FFC. As to the FBC, it is quantitatively designed using reduced plant model to meet the specified load force regulation control specifications. In dealing with the robust control, a disturbance observer based robust control scheme and a parameter identifier are developed. The key parameters in the robust control scheme are designed considering the effect of system dead-time. The identification mechanism is devised to obtain the parameter uncertainties from the observed disturbance signal. Then by online adapting the parameters set in the FFC according to the identified parameters, the nonideal disturbance observer based robust control can be corrected to yield very close model following position tracking control. Meanwhile, the regulation control performance is also further improved by the robust control. In the proposed identification scheme, the effect of a nonideal differentiator in the accuracy of identification results is taken into account, and the compromise between performance, stability, and control effort limit is also considered in the whole proposed control scheme.     

An adaptive fuzzy sliding-mode controller for servomechanismdisturbance rejection

A two-level spring-lumped mass servomechanism system was constructed for disturbance rejection control investigation. This dynamic absorber is similar to a model of the serial-type vehicle suspension system. The lower level is actuated by two DC servo motors, to provide the specified internal and external disturbances to the vibration control system. The upper level has another DC servo motor to control the main body balancing position. In order to tackle the system's nonlinear and time-varying characteristics, an adaptive fuzzy sliding-mode controller is proposed to suppress the main mass position variation due to external disturbance. This intelligent control strategy combines an adaptive rule with fuzzy and sliding-mode control technologies. It has online learning ability for responding to the system's time-varying and nonlinear uncertainty behaviors, and for adjusting the control rules and parameters. Only seven rules are required for this control system, and its control rules can be established and modified continuously by online learning. The experimental results show that this intelligent control approach effectively suppresses the vibration amplitude of the body, with respect to the external disturbance     

Neural-network-based adaptive control for induction servomotordrive system

A neural-network-based adaptive control (NNAC) design method is proposed to control an induction servomotor. In this NNAC design, a neural network (NN) controller is investigated to mimic a feedback linearization control law; and a compensation controller is designed to compensate for the approximation error between the feedback linearization control law and the NN controller. The interconnection weights of the NN can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the control system can be guaranteed. Additionally, in this NNAC system design, an error estimation mechanism is investigated to estimate the bound of approximation error so that the chattering phenomenon of the control effort can be reduced. Simulation and experimental results show that the proposed NNAC servomotor control systems can achieve favorable tracking and robust performance with regard to parameter variations and external load disturbances     

Robust Control of PM Spherical Stepper Motor Based on Neural Networks

There are many uncertainties and disturbances in the real dynamic system of a spherical stepper motor that make traditional control methods with lower precision, such as uncertain changes of magnetic field, load, and friction that generate speed ripple and deteriorate the 3-D tracking performance of the spherical motor system. In this paper, an available method is proposed to solve them by using neural networks (NNs) and a robust control scheme for improving the performance. First, a simplified torque calculation model based on finite-element method results can guarantee quick prediction of electromagnetic torque with lower error. Thus, the system model considering the friction, load, and disturbances is developed. Second, a robust NN (RNN) control scheme is presented to eliminate uncertainties to improve the tracking robust stability and overcome the undesired influence of uncertainties based on the nonlinear system dynamic model under continuous-trajectory tracking mode. Finally, as an example, the step-response and continuous-tracking processes of the motor using an RNN controller are simulated, and experiments, including the tracking using RNN proportional–differential control, are carried out to confirm the usefulness of the proposed control scheme. The simulation and experimental results of the proposed control scheme on the spherical stepper motor system demonstrate the effectiveness on satisfactory tracking performance.      

基于新型CMAC控制方法的电动加载系统

在电动加载系统中,多余力矩强扰动和其他非线性因素直接影响力矩跟踪精度,传统的控制方法难以得到满意的控制效果。文中分析了传统CMAC算法不稳定的原因,提出了一种新型CMAC控制策略,并对其结构及算法进行了研究。在控制结构上以系统的指令输入和实际输出作为CMAC的激励信号,采用误差作为训练信号,并根据激励信号的特点,提出了非均匀量化的思想。动态仿真结果表明,该方法有效抑制了加载系统的多余力矩及摩擦等非线性因素干扰,提高了电动加载系统的控制精度,且增强了系统的稳定性。