Model Compensation Term Research Articles

Uniform robust exact differentiator-based output feedback adaptive robust control for DC motor drive systems

This paper studies a high-accuracy motion control method named output feedback adaptive robust control for a dc motor with uncertain nonlinearities and parametric uncertainties, which always exist in physical servo systems and deteriorate their tracking performance. As only position signal is measurable, a uniform robust exact differentiator (URED) for the unmeasurable states and adaptive control for the parametric uncertainties are integrated in the model compensation term; and the robust control is applied to handle uncertain nonlinearities and stabilize the system. Then, the stability of the closed-loop system is proved theoretically. Finally, simulation and experimental results are studied for a dc motor system to prove the control performance of the proposed control method.

Direct Optimization Based Compensation Adaptive Robust Control of Nonlinear Systems With State and Input Constraints

Motion control of mechatronic system with state and input constraints while achieving excellent integrated performance, such as robustness, high tracking accuracy, fast response, and slow overshoot, has always been a challenging issue. However, most existing relating studies merely focus on how to ensure stability under constraints, and few take integrated performance into account. In this article, we proposed a direct optimization based compensation adaptive robust control (ARC) approach, which is under a two-loop feedback structure, where the outer loop directly online replans both the model compensation term and the reference that conform to the constraints; and the conventional ARC control law is synthesized in inner loop to ensure guaranteed tracking accuracy when facing nonlinearity, parametric uncertainties, and external disturbances. Motion control of a linear motor was considered through this article as an introductory example. Comparative experiments are carried out and the results further verify the superiority and effectiveness of the proposed scheme.

Robust integral of neural network and precision motion control of electrical–optical gyro-stabilized platform with unknown input dead-zones

Parametric uncertainty associated with unmodeled disturbance always exist in physical electrical–optical gyro-stabilized platform systems, and poses great challenges to the controller design. Moreover, the existence of actuator deadzone nonlinearity makes the situation more complicated. By constructing a smooth dead-zone inverse, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback is proposed, in which adaptive law is synthesized to handle parametric uncertainty and RISE robust term to attenuate unmodeled disturbance. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded (GUUB) tracking stability of servo systems recently. An asymptotic tracking performance in the presence of unknown dead-zone, parametric uncertainties and various disturbances, which is vital for high accuracy tracking, is achieved by the proposed robust adaptive backstepping controller. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy.

Model-Data Driven Learning Adaptive Robust Control of Precision Mechatronic Motion Systems With Comparative Experiments

In order to meet the rigorous motion accuracy requirement and efficiently utilize the repetitive-task characteristics in modern precision industry, this paper concentrates on the comprehensive research of model-based data-driven learning adaptive robust control (LARC) strategy for precision mechatronic motion systems. The proposed LARC can achieve not only excellent transient/steady-state tracking performance but also adaptation ability and disturbance robustness. Specifically, the LARC strategy contains robust feedback term, adaptive model compensation term, and iterative learning term. Herein, the former two terms are designed based on the system dynamic model under parametric uncertainty and uncertain nonlinearity, and the data-driven iterative learning term is synthesized to generate optimal input to adjust the optimal reference. The whole controller design procedure and stability is presented, while the reason for the practically achievable performance of LARC is analyzed. Comparative experiments, among proportional—integral—differential, adaptive robust control, iterative learning control, and the proposed LARC, are conducted on a developed linear motor stage. The experimental results consistently validate that the proposed LARC scheme simultaneously achieves excellent transient/steady-state tracking performance, parametric adaptation ability, and disturbance robustness. The LARC strategy essentially provides an effective control technology with good potential in industrial applications.

Direct adaptive robust tracking control for 6 DOF industrial robot with enhanced accuracy

A direct adaptive robust tracking control is proposed for trajectory tracking of 6 DOF industrial robot in the presence of parametric uncertainties, external disturbances and uncertain nonlinearities. The controller is designed based on the dynamic characteristics in the working space of the end-effector of the 6 DOF robot. The controller includes robust control term and model compensation term that is developed directly based on the input reference or desired motion trajectory. A projection-type parametric adaptation law is also designed to compensate for parametric estimation errors for the adaptive robust control. The feasibility and effectiveness of the proposed direct adaptive robust control law and the associated projection-type parametric adaptation law have been comparatively evaluated based on two 6 DOF industrial robots. The test results demonstrate that the proposed control can be employed to better maintain the desired trajectory tracking even in the presence of large parametric uncertainties and external disturbances as compared with PD controller and nonlinear controller. The parametric estimates also eventually converge to the real values along with the convergence of tracking errors, which further validate the effectiveness of the proposed parametric adaption law.

Advanced GTCF-LARC Contouring Motion Controller Design for an Industrial X–Y Linear Motor Stage With Experimental Investigation

In this paper, a global task coordinate frame (GTCF)-based learning adaptive robust contouring controller is proposed for an industrial X–Y linear-motor-driven stage to achieve not only good parametric adaptation ability and disturbance robustness, but also excellent contouring accuracy even under high-speed large-curvature contouring tasks. Specifically, the contouring controller employs GTCF to guarantee the multiaxes motion coordination. After transforming the system dynamics of the X–Y linear-motor-driven stage into the GTCF, a learning adaptive robust control (LARC) scheme is developed to deal with the strongly coupled dynamics under parametric uncertainty and uncertain disturbances. During the LARC, adaptive model compensation term, robust feedback term, and iterative learning term are organically integrated in a serial structure. The controller design process with the stability analysis is presented, while the essence of the practical achievable performance is also introduced for the nature of the GTCF-LARC. Comparative experiments are carried out on an industrial linear-motor-driven stage with different cases. The results consistently verify that the proposed GTCF-LARC contouring controller can simultaneously meet the industrial requirements of excellent transient/steady-state contouring accuracy, parametric adaptation ability, external disturbance robustness, and large-curvature high-speed contouring tasks. The proposed GTCF-LARC scheme actually provides a practical high-performance-oriented contouring control framework, and could be extended to other multiaxes applications.

RISE-Based Precision Motion Control of DC Motors With Continuous Friction Compensation

Continuous friction compensation along with other modeling uncertainties is concerned in this paper, to result in a continuous control input, which is more suitable for controller implementation. To accomplish this control task, a practical method, named as robust integral of the sign of the error controller, is synthesized with a continuous differentiable friction model for high-accuracy motion control of a dc motor. To reduce the noise sensitivity and further improve the tracking accuracy, a desired compensation technique is employed in the proposed controller, in which the model compensation term depends on the reference trajectory only, and its global stability is guaranteed by a proper robust feedback law. Furthermore, the proposed controller theoretically guarantees an asymptotic output tracking performance even in the presence of modeling uncertainties, which is very important for high-accuracy control of motion systems. Comparative experimental results are obtained for the motion control of a dc motor drive system to verify the high-performance nature of the proposed control strategy.