Permanent Magnet Linear Synchronous Motor Servo Research Articles

Adaptive Jerk Control and Modified Parameter Estimation for PMLSM Servo System With Disturbance Attenuation Ability

The direct-drive industrial technology has put forward much higher requirements for the application of high-performance permanent magnet linear synchronous motor (PMLSM) servo system. To improve the control accuracy and transient performance, a novel disturbance attenuation approach is proposed based on adaptive jerk control (AJC) and modified parameter estimation for PMLSM servo system in this article. Within the control details, AJC is proposed to realize asymptotic stability without requiring <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of disturbances, which can release the strong restriction and eliminate the design conservation. Furthermore, a novel adaptive law is designed for feedback gain to further enhance the robustness when the system meets the challenge of high-gain feedback and measurement noise. Meanwhile, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</i> -modification parameter estimation is employed to approximate and compensate for the parameter variation, which can not only achieve fast convergence rate but also guarantee small overshoot. The asymptotic stability of the proposed control method is analyzed and proved by the Lyapunov theorem. Finally, the experimental results are presented to validate the efficiency with better disturbance attenuation ability.

Multi-Kernel Neural Network Sliding Mode Control for Permanent Magnet Linear Synchronous Motors

In this paper, a multi-kernel neural network sliding mode control (MNNSMC) method is proposed to enhance the position tracking performance and disturbance suppression capability of permanent magnet linear synchronous motors (PMLSMs). The designed MNNSMC strategy consists of two parts, a sliding mode control with a dynamic boundary layer and a multi-kernel neural network (MNN). In the former, the dynamic boundary layer is utilized to guarantee that the sliding mode variable converges to the sliding manifold asymptotically. In the latter, disturbances are introduced into the design of kernel functions to further approximate and compensate for the internal and external disturbances, such as parameter variations, positioning force, friction, and un-modeled nonlinearities of PMLSMs. The stability of the proposed MNNSMC strategy is analyzed and proved based on Lyapunov Theorem. Experiments are conducted on a PMLSM servo drive system to validate the effectiveness of the presented MNNSMC method, and results show that the proposed scheme has significant improvement on the position tracking performance, disturbance suppression capability, and robustness of the PMLSM system compared with an existing method.

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

Servo systems driven by a permanent magnet linear synchronous motor (PMLSM) are often affected by uncertain disturbances, such as magnetic resistance, friction, and external disturbances, which increase tracking errors and reduce motion accuracy. Traditional control methods may have difficulty to achieve satisfactory control performance in terms of tracking accuracy and disturbance rejection. In this paper, we propose an internal model control PID method based on a model linear extended state observer, which is termed as IMC-PID-MLESO method. With this method, the nominal model parameters of the PMLSM servo system are obtained via system identification. The model linear extended state observer (MLESO), with known parameter information, is designed to improve the estimation accuracy for the system states and total unknown uncertainties. As the uncertainties are compensated in the feedback control law, the PMLSM servo system model is transformed into a known nominal model. Based on the nominal model, the IMC-PID feedback controller is designed to ensure satisfactory performance on disturbance rejection and tracking error reduction. Simulation and experimental results show that the IMC-PID-MLESO method can effectively improve the position tracking accuracy of the PMLSM servo system and rapidly suppress the system disturbance.

Nonlinear Robust Optimal Control via Adaptive Dynamic Programming of Permanent-Magnet Linear Synchronous Motor Drive for Uncertain Two-Axis Motion Control System

In this article, a nonlinear robust optimal control (NROC) scheme for uncertain two-axis motion control system via adaptive dynamic programming (ADP) and neural networks (NNs) is proposed. The two-axis motion control system is an X-Y table actuated by permanent-magnet linear synchronous motor servo drives. First, the motions of the tracking contour in X-axis and Y-axis of the X-Y table are stabilized through feedback linearization control (FLC) laws. However, the control performance may be destroyed due to parameter uncertainties and compounded disturbances. Therefore, to improve the robustness of the control system, an NROC is designed to achieve this purpose. The tracking control problem of the X-Y table with uncertainties is transformed to a regulation problem. Then, it is solved by an infinite horizon optimal control using a critic NN. Consequently, the NN is developed via ADP learning algorithm to facilitate the online solution of the Hamilton-Jacobi-Bellman equation corresponding to the nominal system for approximating the optimal control law. The uniform ultimate boundedness of the closed-loop system is proved utilizing the Lyapunov approach and the tracking error asymptotically converges to a residual set. The validation of the proposed control schemes are carried out through experimental analysis. The control algorithms have been implemented using a DSP control board. A comparison of control performances using FLC, adaptive FLC, and FLC-based NROC is investigated. From the experimental results, the dynamic behaviors of the two-axis control system using the proposed FLC-based NROC can achieve robust optimal control performance against parameter uncertainties and compounded disturbances.

Intelligent Adaptive Jerk Control With Dynamic Compensation Gain for Permanent Magnet Linear Synchronous Motor Servo System

In this paper, an intelligent adaptive jerk control (IAJC) with dynamic compensation gain for the permanent magnet linear synchronous motor (PMLSM) servo system was proposed to improve robustness and tracking performance against nonlinear and time-varying uncertainties. First, the dynamic model of the PMLSM servo system was investigated. Subsequently, the model-based feedforward control was designed for parametric uncertainties. Then, an adaptive jerk control (AJC) was adopted to restrain external load disturbance, nonlinear friction and unmodeled dynamics of the servo system. The adaptive feedback gain of jerk was updated by an exponential function. However, the uncertainties of the PMLSM servo system were unavailable in advance, it was difficult to design the adaptive feedback gain in practice. Thus, in the following part, the IAJC was further developed in which a dynamic compensation gain was designed using a double-loop recurrent feature selection fuzzy neural network (RFSFNN) to compensate for approximation deviation and suppress the chattering phenomenon. The learning algorithms of the double-loop RFSFNN were derived and the stability of the closed-loop system was proved by the Lyapunov approach. Finally, the experimental results demonstrate that the proposed IAC scheme can achieve robust precise tracking performance.

Extended Kalman filter-based disturbance feed-forward compensation considering varying mass in high-speed permanent magnet linear synchronous motor

A disturbance feed-forward compensation method based on 7th-order extended Kalman filter (EKF) is proposed for permanent magnet linear synchronous motor (PMLSM) servo system which is vulnerable to the influence of external disturbances, such as friction force, force ripple and so on. Firstly, a disturbance model which consists of position-dependent functions was developed and utilized as a disturbance feed-forward compensator. Then, the initial position and the varying mass information of plant were estimated by the 7th-order EKF and reflected to the disturbance feed-forward compensator. In addition, the coefficients of the disturbance model were adjusted adaptively to create synchronization between the model and real disturbance and achieve the purpose of varying mass estimation and disturbance compensation. Finally, the system simulation and experiment results confirm that the proposed method is effective and feasible. The PMLSM servo system based on 7th-order EKF has superior performance in disturbance compensation, and it is not affected by the varying mass of PMLSM.

Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor

For the problem that the position tracking accuracy of permanent magnet linear synchronous motor (PMLSM) servo system is easily affected by uncertain factors such as parameters change, load disturbance and friction and so on, an adaptive neural network nonsingular fast terminal sliding mode control (ANNNFTSMC) method is proposed. Firstly, the PMLSM dynamic mathematical model with uncertainty is established. Then, the nonsingular fast terminal sliding mode control (NFTSMC) can avoid the singularity problem and make the state of the system converge to the equilibrium point quickly, so as to improve the response speed of the system. Secondly, in order to minimize the influence of disturbance and dynamic uncertainty, the dynamic model of PMLSM servo system is estimated by RBF neural network, and the uncertain upper bound of PMLSM servo system is estimated in real time combined with adaptive control, which weakens the chattering phenomenon and enhances the robustness of the system. It is proved theoretically that the control scheme can make the system achieve fast convergence and good tracking. Finally, the system experiments show that the proposed control scheme has the advantages of high tracking accuracy, good robustness, fast response speed and small position error.

Adaptive Nonlinear Disturbance Observer Using a Double-Loop Self-Organizing Recurrent Wavelet Neural Network for a Two-Axis Motion Control System

This paper proposes an adaptive nonlinear disturbance observer (ANDO) for identification and control of a two-axis motion control system driven by two permanent-magnet linear synchronous motors servo drives. The proposed control scheme incorporates a feedback linearization controller (FLC), a new double-loop self-organizing recurrent wavelet neural network (DLSORWNN) controller, a robust controller, and an 1-1 controller. First, an FLC is designed to stabilize the XY table system. Then, a nonlinear disturbance observer (NDO) is designed to estimate the nonlinear lumped parameter uncertainties that include the external disturbances, cross-coupled interference, and frictional force. However, the XY table performance is degraded by the NDO error due to parameter uncertainties. To improve the robustness, the ANDO is designed to attain this purpose. In addition, the robust controller is designed to recover the approximation error of the DLSORWNN, while the 1-1 controller is specified such that the quadratic cost function is minimized and the worst-case effect of the NDO error must be attenuated below a desired attenuation level. The online adaptive control laws are derived using the Lyapunov stability analysis and 1-1 control theory, so that the stability of the ANDO can be guaranteed. The experimental results show the improvements in disturbance suppression and parameter uncertainties, which illustrate the superiority of the ANDO control scheme.

Intelligent tracking control of a PMLSM using self‐evolving probabilistic fuzzy neural network

This study presents a self-evolving probabilistic fuzzy (PF) neural network with asymmetric membership function (SPFNN-AMF) controller for the position servo control of a permanent magnet linear synchronous motor (PMLSM) servo drive system. In the beginning, the dynamic model for the PMLSM is analysed on the basis of field-oriented control. Subsequently, an SPFNN-AMF control system, which integrates the advantages of self-evolving NN, PF logic system, and AMF, is proposed to handle vagueness, randomness, and time-varying uncertainties of the PMLSM servo drive system during the control process. For the SPFNN-AMF, the proposed learning algorithm consists of the structure learning and parameter learning in which the former is used to grow and prune the fuzzy rules automatically, whereas the latter is utilised to train the network parameters dynamically. Finally, detailed experimental results of two position commands tracking at different operation conditions demonstrate the validity and robustness of the proposed SPFNN-AMF for controlling the PMLSM servo drive system.

Application of CMAC-PID Compound Control in PMLSM Servo System

Permanent Magnet Linear Synchronous Motor (PMLSM) servo system was widely used in high precision application and advanced control strategy was necessary for its control system. Cerebellar Model Articulation Controller (CMAC) neural network was introduced into control system with conceptual mapping and physical mapping process analyzed, also the mathematical models of PMLSM was constructed. The compound control system with PID controller as feedback control and CMAC neural network as feed- forward control was constructed. The simulation shows the controller can realize inverse dynamic model and robust control.

Nonlinear Decoupling Sliding Mode Control of Permanent Magnet Linear Synchronous Motor Based on α-th Order Inverse System Method

In this paper, a nonlinear dynamic decoupling controller is proposed for the permanent magnet linear synchronous motor (PMLSM) servo system to improve dynamic operating performance. Firstly, the reversibility of the PMLSM mathematical model is analyzed, and it is proved that the system is reversible. Then an inverse system method is applied to the PMLSM servo system, and it is decoupled into a linear velocity subsystem and a linear current subsystem based on the α-th order inverse system method. Considering the both ideal linear subsystems are sensitive to parameter disturbances and various disturbances, a variable rate reaching law approach based subsystem sliding mode controller for higher system stability and robustness is proposed. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control method.

FPGA-Based Intelligent-Complementary Sliding-Mode Control for PMLSM Servo-Drive System

A field-programmable gate array (FPGA)-based intelligent-complementary sliding-mode control (ICSMC) is proposed in this paper to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo-drive system to track periodic-reference trajectories. First, the dynamics of the field-oriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbances, and nonlinear-friction force, is derived. Then, to achieve the required high-control performance, the ICSMC is developed. In this approach, a radial-basis function-network (RBFN) estimator with accurate approximation capability is employed to estimate the lumped uncertainty directly. Moreover, the adaptive-learning algorithms for the online training of the RBFN are derived using the Lyapunov theorem to guarantee the closed-loop stability. Furthermore, the FPGA chip is adopted to implement the developed control and online learning algorithms for possible low-cost and high-performance industrial applications using PMLSM. Finally, some experimental results are illustrated to show the validity of the proposed control approach.

Field-programmable gate array-based functional link radial basis function network control for permanent magnet linear synchronous motor servo drive system

A field-programmable gate array (FPGA)-based functional link radial basis function network (FLRBFN) control is proposed in this study to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive system to track periodic reference trajectories. First, the dynamics of the field-oriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbances and non-linear friction force, is derived. Then, to achieve accurate trajectory tracking performance with robustness, an intelligent control approach using FLRBFN is proposed for the field-oriented control PMLSM servo drive system. The proposed FLRBFN is a radial basis function network (RBFN) embedded with a functional link neural network (FLNN). Moreover, the on-line learning algorithm of the FLRBFN, including the connective weights, the centres and the centres' width of the receptive field functions, are derived using back-propagation (BP) method. Furthermore, an FPGA chip is adopted to implement the developed control and on-line learning algorithms for possible low-cost and high-performance industrial applications using PMLSM. Finally, the effectiveness of the proposed control scheme and the robustness to parameter variations, external disturbances and friction force of the PMLSM servo drive system are verified by some experimental results.

TSK-type recurrent fuzzy network for DSP-based permanent-magnet linear synchronous motor servo drive

A TSK-type recurrent fuzzy network (TSKRFN) control system is proposed to control the position of the mover of a field-oriented control permanent-magnet linear synchronous motor (PMLSM) servo drive system to track periodic reference trajectories in this study. The proposed TSKRFN combines the merits of self-constructing fuzzy neural network (SCFNN), TSK-type fuzzy inference mechanism, and recurrent neural network (RNN). Moreover, the structure and the parameter learning phases are preformed concurrently and online in the TSKRFN. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient-descent method using a delta adaptation law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviour of the proposed TSKRFN control system is robust with regard to uncertainties.

Incremental motion control of linear synchronous motor

In this study a particular incremental motion control problem, which is specified by the trapezoidal velocity profile using multisegment sliding mode control (MSSMC), is proposed to control a permanent magnet linear synchronous motor (PMLSM) servo drive system. First, the structure and operating principle of the PMLSM are described in detail. Second, a field-oriented control PMLSM servo drive is introduced. Then, each segment of the multisegment switching surfaces is designed to match the corresponding part of the trapezoidal velocity profile, thus the motor dynamics on the specified-segment switching surface have the desired velocity or acceleration corresponding part of the trapezoidal velocity profile. In addition, the proposed control system is implemented in a PC-based computer control system. Finally, the effectiveness of the proposed PMLSM servo drive system is demonstrated by some simulated and experimental results.

On-line gain-tuning IP controller using RFNN

In this study an integral-proportional (IP) controller with on-line gain-tuning using a recurrent fuzzy neural network (RFNN) is proposed to control the mover position of a permanent magnet linear synchronous motor (PMLSM) servo drive system. The structure and operating principle of the PMLSM are first described in detail. A field-oriented control PMLSM servo drive is then introduced. After that, an IP controller with on-line gain tuning using an RFNN is proposed to control the mover of the PMLSM for achieving high-precision position control with robustness. The backpropagation algorithm is used to train the RFNN on line. Moreover to guarantee the convergence of tracking error for the periodic step-command tracking, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Furthermore, the proposed control system is implemented in a PC-based computer control system, Finally, the effectiveness of the proposed PMLSM servo drive system is demonstrated by some simulated and experimental results. Accurate tracking response and superior dynamic performance can be obtained due to the powerful on-line learning capability of the RFNN. In addition, the proposed on-line gain-tuning servo drive system is robust with regard to parameter variations and external disturbances.