In this article, two types of actuators are applied for a lower limb exoskeleton. They are DC motors with the harmonic drive and the pneumatic artificial muscles. This combination takes advantages of both the harmonic drive and the pneumatic artificial muscle. It provides both high accuracy position control and high ratio of strength and weight. The shortcomings of the two actuators are overcome by the hybrid actuation, for example, low control accuracy and modeling difficult of pneumatic artificial muscle, compactness, and structural flexibility of DC motors. The design and modeling processes are discussed to show the proposed exoskeleton can increase the strength of human lower limbs. Experiments and analysis of the exoskeleton are given to evaluate the effectiveness of the design and modeling.

In this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neural Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic Manipulators (PKMs) are highly nonlinear systems, so the design of a suitable control scheme represents a significant challenge given that these kinds of systems are continually dealing with parametric and non-parametric uncertainties and external disturbances. The main contribution of this work is the design of an adaptive feedforward compensation term using B-Spline Neural Networks (BSNNs). They make an on-line approximation of the DPR dynamics and integrates it into the control loop. The BSNNs' functions are bounded according to the extreme values of the desired joint space trajectories that are the BSNNs' inputs, and their weights are on-line adjusted by gradient descend rules. In order to evaluate the effectiveness of the proposed control scheme with respect to the standard RISE controller, numerical simulations for different case studies under different scenarios were performed.

In this paper, a novel 5-Degree of Freedom (DOF) Redundantly Actuated (RA) Parallel Kinematic Manipulator (PKM) called SPIDER4 is presented. The main purpose of this manipulator is to perform machining tasks such as drilling and milling. All the mathematical models including the forward and inverse kinematic models, as well as the inverse dynamic model were developed. Owing to machining tasks require high precision, a RISE Feedforward controller is proposed for desired trajectory tracking. To show the performance and effectiveness of the proposed control scheme, real-time experiments were performed. The obtained results of the proposed controller compared to the standard RISE controller are presented and discussed. They confirm that the proposed controller outperforms the standard one.

In this paper a PD controller with intelligent compensation is used to solve the problem of tracking trajectories for a Delta Parallel Robot with three degrees of freedom. This controller uses an artificial B-Spline neural network as a feedforward compensation term. To evaluate the proposed controller performance some numerical simulations under two different scenarios have been carried out in order to know its effectiveness respect to a simple PD controller.

his work addresses the problem of fault detection and diagnosis (FDD) for a quad-rotor mini air vehicle (MAV). Actuator faults are considered on this paper. The basic idea behind the proposed method is to estimate the faults signals using the extended state observers theory. To estimate the faults, a polynomial observer is presented by using the available measurements and know inputs of the system. In order to investigate the observability and diagnosability properties of the system, a differential algebra approach is proposed. The effectiveness of the methodology is illustrated by means of numerical simulations.