In this paper, a novel approach for adaptive control of flexible multi-link robots in the joint space is presented. The approach is valid for a class of highly uncertain systems with arbitrary but bounded dimension. The problem of trajectory tracking is solved through developing a stable inversion for robot dynamics using only joint angles measurement; then a linear dynamic compensator is utilised to stabilise the tracking error for the nominal system. Furthermore, a high gain observer is designed to provide an estimate for error dynamics. A linear in parameter neural network based adaptive signal is used to approximate and eliminate the effect of uncertainties due to link flexibilities and vibration modes on tracking performance, where the adaptation rule for the neural network weights is derived based on Lyapunov function. The stability and the ultimate boundedness of the error signals and closed-loop system is demonstrated through the Lyapunov stability theory. Computer simulations of the proposed robust controller are carried to validate on a two-link flexible planar manipulator.

This study proposes a real‐time sliding mode field oriented control for a doubly‐fed induction generator (DFIG)‐based wind turbine prototype connected to the grid. The proposed controller is used to track the desired direct current (DC) voltage reference at the output of the DC link, to maintain constant the grid power factor at the step‐up transformer terminals controlled by the grid side converter, and to force independently the stator active and reactive power to track desired values through the rotor currents controlled by the rotor side converter. This control scheme is based on a recurrent high‐order neural network (RHONN) identifier trained on‐line by an extended Kalman filter. The RHONN is used to approximate the DC link and the DFIG mathematical models. The adequate approximation helps to calculate the exact equivalent control part of the sliding mode controller and to eliminate the effects of disturbances and unknown dynamics appearing in the grid, which improves the robustness of the control scheme. This controller is experimentally validated on a 1/4 HP DFIG prototype and tested for variable wind speed to track a time‐varying power reference and to extract the maximum power from the wind, under both balanced and unbalanced grid conditions.

In this paper, we propose different linearization control algorithms to solve the stabilization problem of the quadrotor. First we introduce the nonlinear model of the quadrotor. Then using tangent linearization method, a linear model is generated of the system where decentralized and centralized LQR control methods are applied. The second strategy is based on exact feedback linearization of the nonlinear model of the quadrotor. The comparison between these methods is highlighted by simulations to show effectiveness of the proposed methods.

Recently, parallel inverters have been investigated to provide multilevel characteristics besides their advantage to increase the power system capacity, reliability, and efficiency. However, the issue of differential currents imbalance remains a challenge in parallel inverter operation. The distribution of switching vectors of the resulting multilevel topology has a certain degree of self-differential current balancing properties. Nevertheless, the method alone is not sufficient to maintain balanced differential current in practical applications. This paper proposes a closed-loop differential current control method by introducing a control variable adjusting the dwell time of the selected switching vectors and thus maintaining the differential currents balanced without affecting the overall system performance. The control strategy, including distribution of switching sequence, selection of adjusted switching vectors, and calculation of adjusting times are described and analyzed in detail in this paper. Finally, experimental results confirm the performance and effectiveness of the proposed method.