This article addresses the synchronization control problem for dual-linear-motors-driven systems with model uncertainties and disturbances. An approximation-free robust synchronization control scheme based on cross-coupled control (CCC) frame is proposed to achieve high-precision tracking and synchronization performance, along with excellent uncertainties and disturbances rejection ability. More specifically, the CCC frame is designed to handle the asynchronous motion of two parallel motors. The main advantage is that the proposed method does not require the explicit system model, and any approximations utilized to handle the model uncertainties, such as estimation, identification, and online learning, are not required. Therefore, the computational burden and complexity of the controller are significantly reduced. Considering the importance of the transient and steady-state response, the concept and technology of prescribed performance are adopted to guarantee the control effect and state constraints. In addition, none of the high-order derivatives of desired trajectory, difficult to obtain directly in many applications, are used in the proposed controller. Furthermore, the stability and convergence performance of the closed-loop system are rigorously demonstrated. Finally, comparative experiments show the effectiveness of this study via a dual-driven H-type gantry.

This article proposes a gradient descent (GD) algorithm-based B-spline wavelet neural network (GDBSWNN) learning adaptive controller for linear motor (LM) systems under system uncertainties and actuator saturation constraints. A recursive-least-squares-algorithm-based indirect adaptive strategy is used to effectively estimate model parameters, which can guarantee that they converge to true values. A novel GDBSWNN compensator is proposed to estimate the remaining complex uncertainties, where weights are updated by online GD training. An auxiliary system is integrated into the control scheme to address the saturation problem, which guarantees stability and satisfactory control performance when saturation occurs. In addition, a stability analysis is presented to prove that all the signals of the closed-loop system are bounded using the Lyapunov theory. Experiments have been conducted on an LM-driven motion platform, where different controllers have been tested, demonstrating the effectiveness and advantages of the proposed approach.

A barrier function-based adaptive sliding mode control strategy is proposed for the vehicle active suspension system in this paper. The barrier-functions-based adaptive law is applied to design the sliding mode controller, which can ensure the finite time convergence of the suspension system. Meanwhile, the proposed adaptive law can achieve greatly chattering reduction without requiring the prior information of the upper bound of system uncertainties. Furthermore, the time-delay estimation technique is applied to design the simple and efficient model-free controller. Therefore, the proposed controller has the strong robustness against the nonlinearities and uncertainties of the active suspension system, and it is very practical in actual scenarios. In addition, considering the transient and steady-state performance of the active suspension system, a prescribed performance function is adopted to constrain the vehicle displacement with its convergence rate, maximum overshoot and steady-state value. Finally, compared with the conventional sliding mode control and PD control, comparative experiments of a quarter-car active suspension platform are performed to verify the effectiveness of the proposed strategy.

Model predictive current controller is a popular and effective technique to provide fast dynamic response in the field of motor control. However, conventional predictive controllers are susceptible to deteriorating control performance when model mismatch exists, such as changes in motor parameters due to the temperature variations. Therefore, this article proposes a precise model-aid extended state observer (MAESO) compensation-based real-time model predictive current controller with enhanced parameter robustness performance and high bandwidth. The predictive controller is converted into the form of multiparameter quadratic programming for online solution using numerical computational method and the constraints are linearized. In addition, the disturbances estimated by MAESO are fed back to the controller in the form of parameters for cycle-by-cycle compensation without extra controller design. Comparative simulations and experiments under different operating conditions are carried out to verify the effectiveness and superiority of the proposed method.

To enhance tracking performance and accuracy of linear motor (LM) systems, it is essential to consider the main system uncertainties, such as parameter variations, unmodeled dynamics, and external disturbances, in the control algorithm design. This article proposes a novel motion control scheme based on the utilization of an integral sliding mode observer (SMO). Initially, an indirect adaptive strategy that depends on system states rather than tracking error is designed for parameter estimation, which facilitates the convergence of parameters to their true values. Subsequently, an integral SMO is proposed to estimate the remaining uncertainties, eliminating the requirement of disturbance derivability typical of many existing observers. The finite-time stability of the observer is proven via Lyapunov theory. By combining the two aspects, a composite adaptive controller is designed for LMs. Comparative experiments demonstrate the effectiveness of the proposed controller.

Repetitive motion is one of the most common motion tasks in linear motor (LM) driven system. The LM performs repetitive motion based on periodic target trajectory under control, thus leading to periodic characteristics in certain system uncertainties. For this type of tasks, this article proposes an iterative learning observer-based high-precision motion control scheme, which comprehensively considers high-accuracy model compensation and periodic uncertainties estimation. A recursive least squares (RLS) algorithm-based indirect adaptation strategy is used to achieve high-accuracy parameter estimation and model compensation. A saturated constrained-type iterative learning observer is designed to effectively estimate and compensate for periodic uncertainties. The closed-loop stability of the system is guaranteed in the presence of both periodic and non-periodic uncertainties due to the composite adaptive robust control design. Comparative experiments are conducted on an LM driven motion platform to verify the effectiveness and advantages of the proposed control scheme. Furthermore, the experimental results confirm the enhancement of both transient and steady-state performance of the system.