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In recent technological advancements, designing of exoskeleton have gained attraction from various scientists and researchers across the world. Exoskeletons are assistive devices which are used by the subjects with limb impairments. Various trajectory tracking control schemes have been developed in the past, however there are several challenges like human machine interaction, external disturbances and model uncertainties present that should be handled in the efficient designing of a controller. This paper deals with the development of 2-link lower limb extremity manipulator for rehabilitation purposes in bio-medical engineering. A novel control strategy is proposed to track the desired trajectory for assistive devices using the robust adaptive sliding mode controller coupled with Radial Basis Function (RBF) neural network and fuzzy techniques for function approximation. The simulations are carried out on a musculoskeletal model Gait2354 which can be scaled according to the physical dimensions of the experimental subject developed in OpenSim software. The controller designed is robust in terms of uncertainties and disturbances present in the system and reduces chattering problems. In addition, Minimum Learning Parameter is also incorporated in the system which reduces the number of parameters to only one, thereby reducing the computational cost and enhancing the real time performance of the system. For validation of the proposed model, Matlab Simulink environment is used. The different performance index parameters used in comparative analysis are Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE). The simulation results show that SMC-RBF network has better performance and tracking rate as compared to other controllers implemented. The results obtained were also compared with the existing method to prove their effectiveness. The simulation results obtained ensures to provide effective and comfortable rehabilitation services for lower limb extremity in the future. [ABSTRACT FROM AUTHOR]

This paper introduces a novel neural network adaptive fractional-order sliding mode control strategy based on the minimal learning parameter method (MLPNN-FOSMC). This method aims to solve the problem of relative position control in satellite formation flying (SFF), especially in the presence of model uncertainties, external disturbances, input saturation, and unknown actuator gains. First, the mathematical model for satellite relative positioning is derived. Then, a fractional-order sliding mode controller is introduced and integrated with a radial basis function (RBF) neural network and the minimal learning parameter (MLP) strategy to compensate for errors caused by model uncertainties and external disturbances. At the same time, adaptive control is employed to mitigate the impact of unknown actuator gains on the system. To address the non-smooth input saturation nonlinearity problem, a new saturation function with the smoothing properties of the hyperbolic tangent function is introduced. The system’s stability is ensured by employing the Lyapunov theorem. Finally, a comparative analysis with traditional sliding mode control (SMC) and neural network sliding mode control based on minimal learning parameter (MLPNN-SMC) highlights the superiority of the proposed method.

In this article, an adaptive asymptotic tracking control approach is first proposed for the autonomous underwater vehicle (AUV) on the horizontal plane by using the Bernstein polynomial approximation. Bernstein polynomials aim to compensate for the synthetical uncertainties including unknown model dynamics and environmental disturbances. However, they cannot be directly used due to the unknown polynomial coefficients. To avoid this trouble, the adaptive backstepping technique is applied to estimation. Considering that the adaptive backstepping can only procure the bounded steady tracking errors and suffer from the redundancy of adaptive parameters, we further integrate the σ -modification and the minimum learning parameter (MLP) technique with the Bernstein polynomial approximation, so as to guarantee the high-precision tracking performance of AUVs with the asymptotic convergent tracking errors and less computation burden. The direct Lyapunov's method and the Barbalat's lemma are introduced for proving the stability of the control system. Finally, simulation reveals the advantage of the proposed scheme. • The asymptotic stability ensures the high-precision tracking performance. • Bernstein polynomial approximation has the nice mathematical interpretability. • The MLP design reduces the computational complexity of the controller. [ABSTRACT FROM AUTHOR]