Motion Control Of Linear Motor Research Articles

Extended state observer design with fractional order Bode’s ideal cut-off filter in a linear motor motion control system

A desired output tracking motion control scheme of a new type of self-made linear motor is proposed in the presence of parameter uncertainties and external disturbance. An extended state observer is designed with a fractional order Bode’s ideal cut-off filter to suppress the high-frequency measurement noise. Characteristics of the linear motor are analyzed, including its working principle, mathematical model, and mechanical structure. A fractional order Bode’s ideal cut-off filter, a second-order extended state observer, a reference acceleration feedforward control scheme, and a normal proportional–derivative algorithm constitute the linear-motion control system in this article. The effectiveness of the control scheme has been verified by comparative simulations and experimental results. The proposed method is an effective way to obtain desired output tracking control performance in actual motion control engineering.

Neural-network-based adaptive robust precision motion control of linear motors with asymptotic tracking performance

Neural-network-based adaptive robust precision motion control of linear motors with asymptotic tracking performance

<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>-Synthesis-Based Adaptive Robust Control of Linear Motor Driven Stages With High-Frequency Dynamics: A Case Study

Existing control approaches for the precision motion control of linear motor driven systems are mostly based on rigid-body dynamics of the system. Since all drive systems are subjected to the effect of structural flexible modes of their mechanical parts, the neglected high-frequency dynamics resulting from these structural modes have become the main limiting factor when pushing for better tracking performance and higher closed-loop control bandwidth. In this paper, physical modeling and dynamic analysis that take into account the flexibility of the ball bearings between the stage and the linear guideways are presented with experimental verification. With the gained knowledge of these high-frequency dynamics, a novel $\mu$ -synthesis-based adaptive robust control strategy is subsequently developed. The proposed control algorithm uses adaptive model compensation having accurate online parameter estimation to effectively deal with various nonlinearity effects and to transform the difficult trajectory tracking control problem into a robust stabilization problem. The well-developed $\mu$ -synthesis-based linear robust control technique is then employed in the fast feedback control loop design to explicitly deal with the robust control issue associated with the high-frequency dynamics to achieve higher closed-loop bandwidth for better disturbance rejection. Comparative experiments have been performed and the results show the better tracking performance of the proposed algorithm over existing ones.

Many High Performance Motion Control of Linear Motor Drive System Research

To realize the movement of high-speed, high-precision positioning, satisfy the requirement of the numerical control processing equipment and high precision, based on high performance motion control platform driven by linear motor, an optimized for high performance XY table structure design, the broadband modal coupling modeling and simulation movement, dynamics analysis and controller design, such as content, high performance motion platform organization structure optimization, the global optimization of mechanical system and electrical system. Eventually for high performance sports mechanism design, modeling method and system control method provides the key techniques such as solution, and to develop more high performance motion control of linear motor driven platform prototype machines.

Accurate Motion Control of Linear Motors With Adaptive Robust Compensation of Nonlinear Electromagnetic Field Effect

Many control methodologies have been applied to the motion control of linear motor drive systems. Compensations of nonlinearities such as frictions and cogging forces have also been carried out to obtain better tracking performance. However, the relationship between the driving current and the resulting motor force has been assumed to be linear, which is invalid for high driving coil currents due to the saturating electromagnetic field effect. This paper focuses on the effective compensation of nonlinear electromagnetic field effect so that the system can be operated at even higher acceleration or heavier load without losing achievable control performance. Specifically, cubic polynomials with unknown weights are used for an effective approximation of the unknown nonlinearity between the electromagnetic force and the driving current. The effectiveness of such an approximation is verified by offline identification experiments. An adaptive robust control (ARC) algorithm with online tuning of the unknown weights and other system parameters is then developed to account for various uncertainties. Theoretically, the proposed ARC algorithm achieves a guaranteed transient and steady-state performance for position tracking, as well as zero steady-state tracking error when subjected to parametric uncertainties only. Comparative experiments of ARC with and without compensation of electromagnetic nonlinearity done on both axes of a linear-motor-driven industrial gantry are shown. The results show that the proposed ARC algorithm achieves better tracking performance than existing ones, validating the effectiveness of the proposed approach in practical applications.

Adaptive Robust Precision Motion Control of Linear Motors With Integrated Compensation of Nonlinearities and Bearing Flexible Modes

To realize the high performance potential of linear motor drive systems, various nonlinearities inherited to the system and their compensations have been extensively studied during the past decade. However, existing research tends to focus on one or several types of nonlinearities at a time and thus do not offer a complete overall solution. This paper studies precision motion control of linear motors in the presence of parameter variations and disturbances. An adaptive robust control (ARC) algorithm with simultaneous compensation of all significant nonlinearities is developed. Those nonlinearities include Coulomb friction, cogging force, and nonlinear electromagnetic field effect. The proposed ARC with and without nonlinearity compensation have also been implemented on the Y-axis of a linear-motor-driven industrial gantry. Comparative experimental results show that the proposed ARC algorithm with simultaneous compensation of all significant nonlinearities achieves better motion tracking performance than existing ones. In addition, high-frequency structural flexible modes due to bearing, which are neglected in the previous researches, are explicitly identified experimentally, and their effects are carefully examined. Theoretical analysis is then conducted to generate a set of practically useful guidelines on the tuning of controller gains to optimize the achievable performance in practice.

High accuracy motion control of linear motor drive wire-EDM machines

This paper aims to develop and investigate a permanent magnet linear-motor-driven table system for a wire-EDM machine. A dynamic model and system identification of `the linear motor system have been derived and analyzed. The linear motor drive system has nonlinear and time-varying behaviors because of the effect of irregular friction of the sliding surface and cogging force. Therefore, a conventional digital controller may not suffice to provide a high contouring accuracy as well as adequate disturbance rejection and parameter variation robustness. An indirect adaptive controller (IAC), combined with a neural network-based feedforward controller (NNBFC) is proposed to improve the contouring performance of the linear motor system. Experimental results not only indicate that the proposed control scheme can achieve a high contouring accuracy of ± 0.3 μm, they also demonstrate that the developed linear motor drive wire-EDM machine can meet the requirement for micro machining. The maximum contour error of circular trajectory at a feed-rate of 0.1 mm/min was significantly reduced from 8 μm to 0.5 μm in comparison with proportional-integral-derivative (PID) controller.

A Globally Stable High-Performance Adaptive Robust Control Algorithm With Input Saturation for Precision Motion Control of Linear Motor Drive Systems

This paper focuses on the synthesis of nonlinear adaptive robust controller with saturated actuator authority for a linear motor drive system, which is subject to parametric uncertainties and uncertain nonlinearities such as input disturbances as well. Global stability with limited control efforts is achieved by breaking down the overall uncertainties to state-linearly-dependent uncertainties (such as viscous friction) and bounded nonlinearities (such as Coulomb friction, cogging force, etc.), and dealing with them via different strategies. Furthermore, a guaranteed transient performance and final tracking accuracy can be obtained by incorporating the well-developed adaptive robust control strategy and effective parameter identifier. Asymptotic output tracking is also achieved in the presence of parametric uncertainties only. Meanwhile, in contrast to the existing saturated control structures that are designed based on a set of transformed coordinates, the proposed saturated controller is carried out in the actual system states, which have clear physical meanings. This makes it much easier and less conservative to select the design parameters to meet the dual objective of achieving global stability with limited control efforts for rare emergency cases and the local high-bandwidth control for high performance under normal running conditions. Real-time experimental results are obtained to illustrate the effectiveness of the proposed saturated adaptive robust control strategy

Robust position control of a transportation carriage directly driven by linear motor using wavelet neural

Design, simulation and experimental implementation of a wavelet basis function network learning controller for a transportation carriage directly driven by linear brushless dc motor are considered. Stability robustness with excellent positional precision is the primary concern. The proposed controller deals mainly with external disturbances, e.g. nonlinear friction force and payload variations in motion control of linear motors. It consists of two parts, one is a state feedback component, and the other one is a learning feedback component. The state feedback controller is designed on the basis of a simple linear model, and the learning feedback component is a wavelet neural controller. The effectiveness of the proposed control schemes is demonstrated by some simulated and experimental results.

Robust wavelet neuro control for linear brushless motors

Design, simulation and experimental implementation of a wavelet basis function network learning controller for linear brushless dc motors (LBDCM) are considered. Stability robustness with position tracking is the primary concern. The proposed controller deals mainly with external disturbances, e.g. nonlinear friction force and payload variation in motion control of linear motors. It consists of two parts, one is a state feedback component, and the other one is a learning feedback component. The state feedback controller is designed on the basis of a simple linear model, and the learning feedback component is a wavelet neural controller. The attenuation effect of wavelet neural networks on friction force is first verified by the numerical method. The learning effect of wavelet neural networks on friction force is also shown in the numerical results. Then, a wavelet neural network is applied on a real LBDCM to on-line suppress the friction force, which may be variable due to the different lubrication. The effectiveness of the proposed control schemes is demonstrated by simulated and experimental results.

Adaptive robust motion control of linear motors for precision manufacturing

Linear motors offer several advantages over their rotary counterparts in many precision manufacturing applications requiring linear motion; linear motors can achieve a much higher speed and have the potential of gaining a higher load positioning accuracy due to the elimination of mechanical transmission mechanisms. However, these advantages are obtained at the expense of added difficulties in controlling such a system. Specifically, linear motors are more sensitive to disturbances and parameter variations. Furthermore, certain types of linear motors such as the iron core are subject to significant nonlinear effects due to periodic cogging force and force ripple. To address all these issues, the recently proposed adaptive robust control (ARC) strategy is applied and a discontinuous projection-based ARC controller is constructed. In particular, based on the special structures of various periodic nonlinear forces, design models consisting of known basis functions with unknown weights are used to approximate those unknown nonlinear forces. On-line parameter adaptation is then utilized to reduce the effect of various parametric uncertainties such as unknown weights, inertia, and motor parameters while certain robust control laws are used to handle the uncompensated uncertain nonlinearities effectively for high performance. The resulting ARC controller achieves a guaranteed transient performance and a guaranteed final tracking accuracy in the presence of both parametric uncertainties and uncertain nonlinearities. In addition, in the presence of parametric uncertainties, the controller achieves asymptotic output tracking. Extensive simulation results are shown to illustrate the effectiveness of the proposed algorithm.

Output feedback adaptive robust precision motion control of linear motors

This paper studies high performance robust motion control of linear motors that have a negligible electrical dynamics. A discontinuous projection based adaptive robust controller (ARC) is constructed. Since only output signal is available for measurement, an observer is first designed to provide exponentially convergent estimates of the unmeasurable states. This observer has an extended filter structure so that on-line parameter adaptation can be utilized to reduce the effect of the possible large nominal disturbances. Estimation errors that come from initial state estimates and uncompensated disturbances are effectively dealt with via certain robust feedback at each step of the ARC backstepping design. The resulting controller achieves a guaranteed output tracking transient performance and a prescribed final tracking accuracy. In the presence of parametric uncertainties only, asymptotic output tracking is also achieved. The scheme is implemented on a precision epoxy core linear motor. Experimental results are presented to illustrate the effectiveness and the achievable control performance of the proposed scheme.

Neural network adaptive robust control with application to precision motion control of linear motors

AbstractIn this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance‐oriented control laws for a class of single‐input–single‐output (SISO) nth‐order non‐ linear systems. Both repeatable (or state dependent) unknown non‐linearities and non‐repeatable unknown non‐linearities such as external disturbances are considered. In addition, unknown non‐linearities can exist in the control input channel as well. All unknown but repeatable non‐linear functions are approximated by outputs of multi‐layer neural networks to achieve a better model compensation for an improved performance. All NN weights are tuned on‐line with no prior training needed. In order to avoid the possible divergence of the on‐line tuning of neural network, discontinuous projection method with fictitious bounds is used in the NN weight adjusting laws to make sure that all NN weights are tuned within a prescribed range. By doing so, even in the presence of approximation error and non‐repeatable non‐linearities such as disturbances, a controlled learning is achieved and the possible destabilizing effect of on‐line tuning of NN weights is avoided. Certain robust control terms are constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. In addition, if the unknown repeatable model uncertainties are in the functional range of the neural networks and the ideal weights fall within the prescribed range, asymptotic output tracking is also achieved to retain the perfect learning capability of neural networks in the ideal situation. The proposed neural network adaptive control (NNARC) strategy is then applied to the precision motion control of a linear motor drive system to help to realize the high‐performance potential of such a drive technology. NN is employed to compensate for the effects of the lumped unknown non‐linearities due to the position dependent friction and electro‐magnetic ripple forces. Comparative experiments verify the high‐performance nature of the proposed NNARC. With an encoder resolution of 1 µm, for a low‐speed back‐and‐forth movement, the position tracking error is kept within ±2 µm during the most execution time while the maximum tracking error during the entire run is kept within ±5.6 µm. Copyright © 2001 John Wiley & Sons, Ltd.

Adaptive robust precision motion control of linear motors with negligible electrical dynamics: theory and experiments

This paper studies the high performance robust motion control of an epoxy core linear motor, which has negligible electrical dynamics due to the fast response of the electrical subsystem. A discontinuous projection based adaptive robust controller (ARC) is first constructed. The controller theoretically guarantees a prescribed transient performance and final tracking accuracy in general, while achieving asymptotic tracking in the presence of parametric uncertainties. A desired compensation ARC scheme is then presented, in which the regressor is calculated using the reference trajectory information only. The resulting controller has several implementation advantages such as less online computation time, reduced effect of measurement noise, a separation of robust control design from parameter adaptation, and a faster adaptation rate. Both schemes are implemented and compared on an epoxy core linear motor. Extensive comparative experimental results are presented to illustrate the effectiveness and the achievable control performance of the two ARC designs.

Linear motor motion control using a learning feedforward controller

The design and realization of an online learning motion controller for a linear motor is presented, and its usefulness is evaluated. The controller consists of two components: (1) a model-based feedback component, and (2) a learning feedforward component. The feedback component is designed on the basis of a simple second-order linear model, which is known to have structural errors. In the design, an emphasis is placed on robustness. The learning feedforward component is a neural-network-based controller, comprised of a one-hidden-layer structure with second-order B-spline basis functions. Simulations and experimental evaluations show that, with little effort, a high-performance motion system can be obtained with this approach.