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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]

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]

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]

The purpose of this research is to construct a simple and practical controller design method, considering the actuator's parameter uncertainty, without using a model of controlled objects. In this method, a controller is designed with an actuator model including a single-degree-of-freedom virtual structure inserted between actuator and controlled object, resulting in a model-free controller design. Furthermore, an H ∞ control problem is defined so that the actuator's parameter uncertainty is compensated by satisfying a robust stability condition. Because the actuator model including the virtual controlled object is a simple low-order system, and the actuator's parameter uncertainty is considered, a controller with high robustness to the actuator's parameter uncertainty can be designed based on traditional model-based control theory. The effectiveness of the proposed method is verified by both simulation and experiment. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a simple and practical controller design method without modeling controlled objects. In this technique, modeling of the controlled object is not necessary and a controller is designed with an actuator model, which includes a single-degree-of-freedom virtual structure inserted between the actuator and the controlled object. The parameters of the virtual structure are determined so that indirect active vibration suppression is effectively achieved by considering the frequency transfer function from the vibration response of the controlled object to that of the virtual structure. Since the actuator model, which includes a virtually controlled object, is a simple low-order system, a controller with high control performance can be designed by traditional model-based optimal control theory. In this research, a mixed H 2 / H ∞ controller is designed considering both control performance and robust stability. The effectiveness of the proposed method is validated experimentally. The robustness of the controller is demonstrated by applying the same controller to various structures. [ABSTRACT FROM AUTHOR]

A new model-free approach was investigated to control two moving aps and a T-foil of a fast ferry traveling in the head sea. Considered as a highly perturbed system, fast ferries in sea waves suffer from the undesirable effects of pitch and heave motions causing severe vertical accelerations that lead to the discomfort of passengers who then experience seasickness. To appropriately control this multivariable system, a model-free control strategy was adopted. An augmented PD controller with compensating terms resulting from an online identification of an ultralocal model of the system was designed. Relying only on input{output data and using online numerical differentiation based on a fast estimation technique, a nonphysical model was designed and continuously updated to capture all the unknown system dynamics, uncertainties, and perturbations. The resulting controller, known as an intelligent PD (iPD) controller, has been proven to be of a reduced design complexity but able to cope with the system's changing characteristics under high perturbations. The robustness to parameter variations was analyzed and compared to a classic PD controller's performance. The results showed that good reductions in the system's vertical motions and seasickness were obtained with a low computational cost. Moreover, the iPD successfully exhibited very robust behavior based on its model-free property when changing the system parameters and the operating velocity. [ABSTRACT FROM AUTHOR]

The event-driven model-free control is proposed in this article, which deals with the 'trade-off' between computational cost and system performance. Model-free controllers demand low computational resources and have high robustness, which is especially suitable for embedded systems with complex dynamics and/or affected by disturbances. The proposed method is implemented in two distinct nonlinear Multiple Input, Multiple Output (MIMO) motion models: a quadrotor model hovering in different weather circumstances and a vehicular friction control model operating in variable road surface conditions. Under the time- and event-driven schemes, the model-free control is compared with standard control strategies in various realistic scenarios. [ABSTRACT FROM AUTHOR]

Most control algorithms for rigid-link electrically driven robots are given in joint coordinates. However, since the task to be accomplished is expressed in Cartesian coordinates, inverse kinematics has to be computed in order to implement the control law. Alternatively, one can develop the necessary theory directly in workspace coordinates. This has the disadvantage of a more complex robot model. In this paper, a robust control scheme is given to achieve exact Cartesian tracking without the knowledge of the manipulator kinematics and dynamics, actuator dynamics and nor computing inverse kinematics. The control design procedure is based on a new form of universal approximation theory and using Stone–Weierstrass theorem, to mitigate structured and unstructured uncertainties associated with external disturbances and actuated manipulator dynamics. It has been assumed that the lumped uncertainty can be modeled by linear differential equations. As the method is Model-Free, a broad range of manipulators can be controlled. Numerical case studies are developed for an industrial robot manipulator. [ABSTRACT FROM PUBLISHER]

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]

Recent studies show that emotion is a mechanism for fast decision-making in human and other animals. Mathematical models have been developed for describing emotion in mammals. These models, similar to other bioinspired models, must be implemented in embedded platforms for industrial and real applications. In this paper, brain emotional learning based intelligent controller, which is based on mammalian middle brain, is designed and implemented on field-programmable gate arrays, and this emotional controller is applied for controlling of laboratorial overhead traveling crane in model-free and embedded manner. The main features of this controller are leaning capability, providing a model-free control algorithm, robustness and the ability to respond swiftly. By designing appropriate stress signals, a designer can implement a proper trade among control objectives. [ABSTRACT FROM AUTHOR]

This paper deals with the robust control of robot manipulators by presenting a novel voltage-based control approach. For this purpose, an Extended State Observer (ESO) is utilized to estimate the dynamical behaver of the system without any need to use the equations of motion including robot dynamics, actuator dynamics and their parameters in the proposed method. Convergence analysis proves that the estimated parameters converge to actual parameters asymptotically. The boundedness of tracking error is guaranteed through the Lyapunov theorem. Simulation results on an articulated robot driven by permanent magnet DC motors, and experimental implementation on a SCARA robot show that the proposed model-free controller has a satisfactory performance as compared to an adaptive uncertainty estimation-based controller. [ABSTRACT FROM AUTHOR]