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In this paper, an online adaptive super twisting sliding mode controller is proposed for a non-linear system. The adaptive controller has been designed in order to deal with the unknown dynamic uncertainties and give the best trajectory tracking. The adaptation is based on an optimal Particle Swarm Optimization (PSO) algorithm whose goal is online tuning the parameters through focusing on decreasing the objective function. The novelty of this study is online handling parameters setting in the conventional super twisting algorithm, bypass heavy offline calculation, and also avoid the instability and abrupt changing of the controller's parameters for better actuators lifetime. This novel approach has been applied on an upper limb exoskeleton robot for arm rehabilitation. Despite the changes of the dynamic model of the system which defers from one patient to another due to the direct interactions between the wearer and the exoskeleton, this control technique preserves its robustness with respect to bounded external disturbances. The effectiveness of the proposed adaptive controller has been proved in simulation and then in real-time experiment with two human subjects. A comparison between the proposed approach and classic super twisting algorithm has been conducted. The obtained results show the performance and efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop an active steering assistance system to avoid the rollover of heavy vehicles (HV). The proposed approach is applied on a single body model of HV presented in this paper. An estimator based on the high-order sliding mode observer is developed to estimate the vehicle dynamics, such as lateral acceleration limit and center height of gravity. Lateral position and lateral speed are controlled using a twisting algorithm to ensure the stability of the vehicle and avoid accidents. At the same time, the identification of unsprung masses and suspension stiffness parameters of the model have been computed to increase the robustness of the method. Some simulation and experimental results are given to show the quality of the proposed concept. [ABSTRACT FROM PUBLISHER]

This paper presents an adaptive control strategy for an upper-limb exoskeleton based on an on-line dynamic parameter estimator. The objective is to improve the control performance of this system that plays a critical role in assisting patients for shoulder, elbow and wrist joint movements. In general, the dynamic parameters of the human limb are unknown and differ from a person to another, which degrade the performances of the exoskeleton-human control system. For this reason, the proposed control scheme contains a supplementary loop based on a new efficient on-line estimator of the dynamic parameters. Indeed, the latter is acting upon the parameter adaptation of the controller to ensure the performances of the system in the presence of parameter uncertainties and perturbations. The exoskeleton used in this work is presented and a physical model of the exoskeleton interacting with a 7 Degree of Freedom (DoF) upper limb model is generated using the SimMechanics library of MatLab/Simulink. To illustrate the effectiveness of the proposed approach, an example of passive rehabilitation movements is performed using multi-body dynamic simulation. The aims is to maneuver the exoskeleton that drive the upper limb to track desired trajectories in the case of the passive arm movements.