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In addition to the difficulty of system modeling, every physical system faces actuator saturation, making the real control different from the controller output (desired control input). When this occurs, the controller output does not regulate the system properly and errors occur as a result of incorrect updating. This is known as the windup phenomenon. In the event that the controller is configured to override actuator saturation, it can lead to a failure in the controller's performance, such as large overshoots, high response time, and even system instability in the worst case. Therefore, in this paper, a new robust model-free controller method is proposed. It is a combination of four nonlinear control techniques, namely model-free controller, fractional-order sliding mode controller, fractional integral–proportional (PI) controller, and anti-windup compensator, which gives rise to the new model-free controller algorithm called hybrid fractional-order intelligent PI fractional sliding mode controller with an anti-windup compensator termed (MF-FOiPI-FOSMC-AW). However, to illustrate the effectiveness of the new proposed MF-FOiPI-FOSMC-AW method, simulation and experimental results compared to classical PI and iPI controllers (with and without an anti-windup compensator) on a hydraulic system are presented in the presence or absence of external disturbances for different types of references. Finally, simulation is conducted through an experimental comparison of the proposed approach with other strategies such as the classical anti-windup PI controller and the anti-windup intelligent PI controller, which is carried out for this purpose. [ABSTRACT FROM AUTHOR]

Starting from Fliess's model-free control (MFC) technique developed 15 years ago, this article aims to provide a systematic framework for characterizing, benchmarking, and generalizing this emerging control technique, with a particular focus on power electronics (PE). It examines the performance of MFC in terms of dynamic response, stability, and robustness, using the classical control theory as a basic tool. A theoretical comparison is conducted with the conventional linear control techniques on dynamic response and performance robustness. A generalized MFC theory and means to enhance its robustness performance are also highlighted. This article suggests that MFC, in contrast to the conventional understanding based on model-independent error dynamics, is practically a model-based control technique. Such model dependence characteristics under MFC become more severe for PE systems. However, following a new design principle, MFC is found possible to possess extraordinarily robust performance against model variations as compared to most existing model-based control methods. On top of that, stability margin is found to be the key bottleneck hindering the performance robustness of the existing MFC techniques. A new MFC with greater stability margin and performance robustness is proposed in this article. Comprehensive Pareto fronts analysis, simulations, and experiments are conducted on a buck converter system to verify the new understandings and conclusions drawn from the framework. With the new design principle and the new MFC, the system is able to demonstrate an almost constant dynamic response despite 25-fold circuit parameter (R, C, and L) variations and 1.85-fold input voltage variations. [ABSTRACT FROM AUTHOR]

Lower-body exoskeleton control that adapts to users and provides assistance-as-needed can increase user participation and motor learning and allow for more effective gait rehabilitation. Adaptive model-based control methods have previously been developed to consider a user's interaction with an exoskeleton; however, the predefined dynamics models required are challenging to define accurately, due to the complex dynamics and nonlinearities of the human-exoskeleton interaction. Model-free deep reinforcement learning (DRL) approaches can provide accurate and robust control in robotics applications and have shown potential for lower-body exoskeletons. In this paper, we present a new model-free DRL method for end-to-end learning of desired gait patterns for over-ground gait rehabilitation with an exoskeleton. This control technique is the first to accurately track any gait pattern desired in physiotherapy without requiring a predefined dynamics model and is robust to varying post-stroke individuals' baseline gait patterns and their interactions and perturbations. Simulated experiments of an exoskeleton paired to a musculoskeletal model show that the DRL method is robust to different post-stroke users and is able to accurately track desired gait pattern trajectories both seen and unseen in training. [ABSTRACT FROM AUTHOR]

This paper is devoted to the problem of prescribed performance trajectory tracking control for symmetrical underactuated unmanned surface vessels (USVs) in the presence of model uncertainties and input quantization. By combining backstepping filter mechanisms and adaptive algorithms, two robust control architectures are investigated for surge motion and yaw motion. To guarantee the prespecified performance requirements for position tracking control, the constrained error dynamics are transformed to unconstrained ones by virtue of a tangent-type nonlinear mapping function. On the other hand, the inaccurate model can be identified through radial basis neural networks (RBFNNs), where the minimum learning parameter (MLP) algorithm is employed with a low computational complexity. Furthermore, quantization errors can be effectively reduced even when the parameters of the quantizer remain unavailable to designers. Finally, the effectiveness of the proposed controllers is verified via theoretical analyses and numerical simulations. [ABSTRACT FROM AUTHOR]