Adaptive repetitive control of PWM inverters for very low THDAC-voltage regulation with unknown loads

An adaptive repetitive control scheme is proposed and applied to the control of a pulsewidth-modulated (PWM) inverter used in a high-performance AC power supply. The proposed control scheme can adaptively eliminate periodic distortions caused by unknown periodic load disturbances in an AC power supply. The proposed adaptive repetitive controller consists of a voltage regulator using state feedback control, a repetitive controller with tuning parameters and an adaptive controller with a recursive least-squares estimator (LSE). This adaptive repetitive controller designed for AC voltage regulation has been realized using a single-chip digital signal processor (DSP) TMS320C14 from Texas Instruments. Experimental verification has been carried out on a 2 kVA PWM inverter. Simulation and experimental results show that the DSP-based adaptive repetitive controller can achieve both good dynamic response and low total harmonic distortion (THD) under large-load disturbances and uncertainties     

An Improved Deadbeat Current Control Scheme With a Novel Adaptive Self-Tuning Load Model for a Three-Phase PWM Voltage-Source Inverter

In this paper, an improved deadbeat current control scheme with a novel adaptive self-tuning load model for a three-phase pulsewidth-modulated (PWM) voltage-source inverter is proposed. First, to achieve high-bandwidth current control characteristics, an improved deadbeat current controller with delay compensation is adopted. The compensation method forces the delay elements, which are caused by voltage calculation, PWM, and synchronous frame rotation, to be equivalently placed outside the closed-loop control system. Hence, their effect on the closed-loop stability is eliminated, and the current controller can be designed with a higher bandwidth. Second, to relax the parameter sensitivity issue of the deadbeat controller and to realize a control scheme with reduced sensors, a novel adaptive self-tuning load model is emerged in the control structure. The adaptive model is designed with low computational demand to estimate in real time the load parameters (R,L) and the back-electromotive-force voltage simultaneously. A unified solution to the present nonlinear estimation problem is presented by adopting a parallel observer structure. Furthermore, the adaptive model has the necessary phase advance of the estimated quantities, which compensates for the total system's delay. Comparative evaluation results are presented to demonstrate the validity and effectiveness of the proposed control scheme     

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.     

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.     

Adaptive control of a class of nonlinear systems with a first-orderparameterized Sugeno fuzzy approximator

In this paper, an adaptive fuzzy control scheme for tracking a class of continuous-time plants is presented. A parameterized Sugeno fuzzy approximator is used to adaptively compensate for the plant nonlinearities. All parameters in the fuzzy approximator are tuned using a Lyapunov-based design. In the fuzzy approximator, first-order Sugeno consequents are used in the IF-THEN rules of the fuzzy system, which has a better approximation capability than those using constant consequents. Global boundedness of the adaptive system is established. Finally, a simulation is used to demonstrate the effectiveness of the proposed controller     

超混沌复系统的自适应广义组合复同步及参数辨识

该文针对含未知参数的异结构超混沌复系统,基于自适应控制及Lyapunov稳定性理论,提出一种新的自适应广义组合复同步方法 (GCCS)。首先给出广义组合复同步的定义,将驱动-响应系统的同步问题转化为误差系统零解的稳定性问题;然后从理论上设计了非线性反馈同步控制器及参数辨识更新律,并引入误差反馈增益,以控制同步的收敛速度;最后以超混沌复Lorenz系统、超混沌复Chen系统、超混沌复L系统的广义组合复同步与参数估计为例,从数值仿真角度验证了所提方法的正确性和有效性。     

Contact transition control via joint acceleration feedback

Stable and controllable transition from free motion to constrained motion is of central importance for robots in contact with the environment in many applications. In this paper, a joint acceleration feedback control scheme of high bandwidth is employed to damp oscillations during the contact transition when the approaching speed does not vanish. In this control scheme, a classical integral force controller is refined by means of joint acceleration and velocity feedback. This is intended to achieve a stable contact transition without need of adjusting the controller parameters adaptive to the unknown or changing environments. Extensive experiments are conducted on the third joint of a three-link direct-drive robot to verify the proposed scheme for the environments of various stiffnesses, including elastic (sponge), less-elastic (cardboard), and hard (steel plate) surfaces. Results are also compared with those by the transition control without the acceleration feedback. The proposed scheme is shown to be promising in terms of robustness, stability and adaptability     

Damping of Torsional Vibrations in Two-Mass System Using Adaptive Sliding Neuro-Fuzzy Approach

In this paper, a new robust control system with the adaptive sliding neuro-fuzzy speed controller for the drive system with the flexible joint is proposed. A model reference adaptive control structure (MRAC) is used in this drive system. The torsional vibrations are successfully suppressed in the control structure with only one basic feedback from the motor speed. The damping ability of the proposed system has been confirmed for a wide range of the system parameters and compared with the other control concepts, like the adaptive Pi-type neuro-fuzzy controller and the classical cascade PI structure.     

Adaptive Fuzzy Controller of the Overhead Cranes With Nonlinear Disturbance

Overhead cranes are common industrial structures that are used in many factories and harbors. They are usually operated manually or by some conventional control methods, such as the optimal and PLC-based methods. The theme of this paper is to provide an effective all-purpose adaptive fuzzy controller for the crane. This proposed method does not need the complex dynamic model of the crane system, but it uses trolley position and swing angle information instead to design the fuzzy controller. An adaptive algorithm is provided to tune the free parameters in the crane control system. The ways to speed the transportation and reduce the computational efforts are also given. Therefore, the designing procedure of the proposed controller will be very easy. External disturbance, such as the wind and the hit, which always deteriorates the control performance, is also discussed in this paper to verify the robustness of the proposed adaptive fuzzy algorithm. At last, several experimental results with different wire length and payload weight compare the feasibility and effectiveness of the proposed scheme with conventional methods     

A nonlinear speed control for a PM synchronous motor using a simpledisturbance estimation technique

A nonlinear speed control for a permanent-magnet (PM) synchronous motor using a simple disturbance estimation technique is presented. By using a feedback linearization scheme, the nonlinear motor model can be linearized in the Brunovski canonical form, and the speed controller can be easily designed based on the linearized model. This technique, however, gives an undesirable output performance under the mismatch of the system parameters and load conditions. An adaptive linearization technique and a sliding-mode control technique have been reported. Although good performance can be obtained, the controller designs are quite complex. To overcome this drawback, the controller parameters are estimated by using a disturbance observer theory where the disturbance torque and flux linkage are estimated. Since only the two reduced-order observers are used for the parameter estimation, the observer designs are considerably simple and the computational load of the controller for parameter estimation is negligibly small. The nonlinear disturbances caused by the incomplete linearization can be effectively compensated by using this control scheme. Thus, a desired dynamic performance and a zero steady-state error can be obtained. The proposed control scheme is implemented on a PM synchronous motor using a digital signal processor (TMS320C31) and the effectiveness is verified through the comparative simulations and experiments     

An MRAC-based nonlinear speed control of an interior PM synchronous motor with improved maximum torque operation

A model reference adaptive control (MRAC)-based nonlinear speed control strategy of an interior permanent magnet (IPM) synchronous motor with an improved maximum torque operation is presented. In most servo systems, the controller is designed under the assumption that the electrical dynamics are neglected by the field-oriented control. This requires a high-performance inner-loop current control strategy. However, the separate designs for a high-performance current regulator and a robust speed controller need considerable effort. To overcome this limitation, an MRAC-based nonlinear speed control strategy for the IPM synchronous motor is presented, considering the whole nonlinear dynamics. Nonlinear speed control is achieved by an input–output linearization scheme. This scheme, however, gives an unsatisfactory performance under the mismatch of the system parameters and load conditions. For the robust output response, the controller parameters are estimated by an MRAC technique in which the disturbance torque and flux linkage are estimated. The adaptation laws are derived from Lyapunov stability theory. In view of the drive efficiency, the motor has to provide the maximum torque for a given input. To drive the IPM synchronous motor under improved maximum torque operation, the estimated flux linkage is employed for the generation of the d-axis current command. The robustness and output performance of the proposed control scheme are verified through simulation results.     

Rejection of Limit Cycles Induced From Disturbance Observers in Motion Control

A disturbance observer (DOB) is generally introduced into motion control systems to eliminate the unwanted disturbance and plant uncertainty. Due to the quantization error from the sensors and D/A converter, DOB-based controller might generate limit cycles which severely decreases positioning accuracy as the system reaches steady-state positioning. This paper presents a sufficient condition for removing limit cycles and explores the condition to design controller parameters. In addition, since the parameters based on the condition make the system sluggish, an adaptive mechanism is introduced not only to maintain the system performance but also to eliminate limit cycles. The experimental results validate the analytical results and also illustrate the effectiveness of the proposed method     

PID-type sliding mode-based adaptive motion control of a 2-DOF piezoelectric ultrasonic motor driven stage

This paper presents a new scheme of adaptive sliding mode control (ASMC) for a piezoelectric ultrasonic motor driven X–Y stage to meet the demand of precision motion tracking while addressing the problems of unknown nonlinear friction and model uncertainties. The system model with Coulomb friction and unilateral coupling effect is first investigated. Then the controller is designed with adaptive laws synthesized to obtain the unknown model parameters for handling parametric uncertainties and offsetting friction force. The robust control term acts as a high gain feedback control to make the output track the desired trajectory fast for guaranteed robust performance. Based on a PID-type sliding mode, the control scheme has a simple structure to be implemented and the control parameters can be easily tuned. Theoretical stability analysis of the proposed novel ASMC is accomplished using a Lyapunov framework. Furthermore, the proposed control scheme is applied to an X–Y stage and the results prove that the proposed control method is effective in achieving excellent tracking performance.     

Adaptive Synchronization of R?ssler System Based on Dynamic Surface Control

A synchronization scheme for R?ssler system based on Dynamic Surface Control (DSC) is proposed in this paper. The DSC method is a recursive design procedure like conventional backstepping methods. Different from the backstepping design, a first-order filter is introduced in every DSC design step. For this introduced filter, the derivative of the selected virtual control is avoided and then the drawback of "explosion of complexity" existing in backstepping design is overcome. Moreover, adaptive method is used for controller design when the system parameters are unknown. Finally, a numerical example is given to illustrate the effectiveness and performance of the proposed method.     

A Robust Hybrid Intelligent Position/Force Control Scheme for Cooperative Manipulators

We examine in this paper the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive hybrid intelligent control scheme is proposed here. The controller makes use of a multi-input multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed controller does not require a precise dynamical model of the system's dynamics. As a matter of fact, the controller can achieve its control objectives starting from partial or no a priori knowledge of the system's dynamics. The ability to incorporate the already acquired knowledge about the system's dynamics is among what distinguishes the proposed controller from its predecessor adaptive fuzzy controllers. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying intensity levels of the aforementioned uncertainties. The position and the internal force errors are also shown to asymptotically converge to zero under such conditions     

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.     

A Hybrid-Type Variable-Structure Instantaneous Torque Control With a Robust Adaptive Torque Observer for a High-Performance Direct-Drive PMSM

This paper describes a novel instantaneous torque control scheme for a high-performance direct-drive permanent-magnet synchronous motor. The scheme consists of a robust adaptive instantaneous torque observer and a hybrid-type variable-structure instantaneous torque controller. First, to robustly obtain the instantaneous electromagnetic torque information, a robust adaptive torque observer is designed by considering all possible current model uncertainties. The observation gains and uncertainties prediction rules are derived in the sense of Lyapunov theory so that the stability of the proposed estimation scheme is fulfilled. Second, to ensure perfect tracking of the output torque and providing means in eliminating torque ripples, the frequency modes of the disturbances to be eliminated should be included in the stable closed-loop system. To achieve this objective, a hybrid-type variable-structure controller with internal model, for the flux harmonics and system uncertainties, is adopted. The hybrid controller shows better disturbance rejection without control chattering. Comparative evaluation results are presented to demonstrate the validity and effectiveness of the proposed instantaneous torque control scheme.     

Adaptive incremental sliding mode control for a robot manipulator

An adaptive incremental sliding mode control (AISMC) scheme for a robot manipulator is presented in this paper. Firstly, an incremental backstepping (IBS) controller is designed using time-delay estimation (TDE) to reduce dependence on the mathematical model. After substituting IBS controller into the nonlinear system, a linear system w.r.t. tracking errors is obtained while TDE error is the disturbance. Then, the AISMC scheme, including a nominal controller and an SMC, is developed for the resulted linear system to improve control performance. According to the equivalent control method, the SMC in the AISMC scheme is to handle TDE error. To receive optimal control performance at the sliding manifold, an LQR controller is selected as the nominal controller. The SMC is designed based on positive semi-definite barrier function (PSDBF) since it prevents switching gains from being over/under-estimated, and two practical problems are addressed in this paper: A new PSDBF is designed and conservative (large) setting bounds affecting tracking precision and/or system stability are avoided; An improved PSDBF based SMC is developed where the PSDBF and an adaptive parameter are used simultaneously to regulate switching gains, and the system is still stable when sliding variable occasionally exceeds the predefined vicinity. Moreover, finite-time convergence property of the sliding variable is strictly analyzed. Finally, real-time experiments are conducted to verify the effectiveness of the proposed control method.     

Adaptive neural network based controller for robots

A new Adaptive Neural Network (ANN) controller for robot trajectory trackingproblem is developed. A novel and efficient training algorithm for the proposed controller ispresented in this paper. The proposed training algorithm is based on updating the weights of thenetwork each step by minimizing the quadrant tracking errors and their derivatives.A simulation study is carried out on a polar robot manipulator to assure the effectivenessof the proposed trajectory tracking robot control system. The effects of the new controllerparameters and noisy external load disturbances on the control performance are studied. Thesimulation results of the proposed adaptive ANN controller are compared with those of aconventional ANN controller. The obtained results assured the robustness of the proposed ANNcontroller for: (i) uncertainties of the robot arm dynamic model and/or parameters, (ii) variousnoisy external load disturbances. Also, the simulation results assure the effectiveness of theproposed adaptive ANN controller against the conventional ANN one.     

基于投影法的不确定分数阶混沌系统自适应同步

针对一类具有未知参数、未知非线性函数及外部扰动的分数阶混沌系统,基于分数阶系统稳定性理论和Lyapunov稳定性理论,该文提出一种基于滑模自适应和投影法的同步控制策略。首先选取一类稳定的分数阶积分滑模面,运用自适应技术对不确定项进行估计,设计了同步控制器。然后对自适应设计中容易出现的增长型自适应律运用投影法进行修正,以保证参数有界,从而也保证控制输入有界。最后数值仿真证明了所设计控制器的正确性和有效性。