A novel nonlinear Controller is proposed to track active and reactive power for a Brushless Doubly-Fed Induction Generator (BDFIG) wind turbine. Due to nonlinear Dynamics and the presence of parametric uncertainties and perturbations in this system, Sliding Mode Control is employed. To generate a smooth Control signal, Dynamic Sliding Mode method is used. Uncertainties bound is not required in the suggested algorithm, since the adaptive gain in the Controller relation is used in this study. Convergence of the Sliding variable to zero and adaptive gain to the uncertainty bound are verified using Lyapunov stability theorem. The proposed Controller is evaluated in a comprehensive simulation on the BDFIG Model. Moreover, output performance of the proposed Control algorithm is compared to the conventional and second-order Sliding Mode and proportional-integral-derivative (PID) Controllers.

Two phenomena can produce chattering: switching of input Control signal and the large amplitude of this switching (switching gain). To remove the switching of input Control signal, Dynamic Sliding Mode Control (DSMC) is used. In DSMC switching is removed due to the integrator which is placed before the plant. However, in DSMC the augmented system (system plus the integrator) is one dimension bigger than the actual system and then, the plant Model should be completely known. To overcome this difficulty, a fuzzy system is employed to identify the unknown nonlinear function of the plant Model and then, a robust adaptive law is developed to train the parameters of this fuzzy system. The other problem is that the switching gain may be chosen unnecessary large to cope on the unknown uncertainty. To solve this problem, another fuzzy system is proposed which does not need the upper bound of the uncertainty. Moreover, to have a suitable small enough switching gain an adaptive procedure is applied to increase and decrease the switching gain according to the system circumstances. Then, chattering is removed using the DSMC with a small adaptive switching gain (ASG). As a case study, nonlinear chaotic Duffing-Holmes system is selected.

In this paper, Dynamic Sliding Mode Control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC, the chattering is removed due to the integrator placed before the input Control signal of the plant. However, in DSMC, the augmented system has higher order than the actual system, i. e. the states number of the augmented system is higher than the actual system and then to Control of such a system, we must know and identify the new states, or the plant Model should be completely known. To solve this problem, we suggest two online neural networks to identify and to obtain a Model for the unknown nonlinear system. In the first approach, the neural network training law is based on the available system states and the bound of the observer error is not proved to converge to zero. The advantage of the second training law is only using the system’ s output and the observer error converges to zero based on the Lyapunov stability theorem. To verify these approaches, Duffing-Holmes chaotic systems (DHC) are used.

In this paper, a novel Model-free Control scheme is developed to enhance the tracking performance of robotic systems based on an adaptive Dynamic Sliding Mode Control and voltage Control strategy. In the voltage Control strategy, actuator Dynamics have not been excluded. In other words, instead of the applied torques to the robot joints, motor voltages are computed by the Control law. First, a Dynamic Sliding Mode Control is designed for the robotic system. Then, to enhance the tracking performance of the system, an adaptive mechanism is developed and integrated with the Dynamic Sliding Mode Control. Since the lumped uncertainty is unknown in practical applications, the uncertainty upper bound is necessary in the design of the Dynamic Sliding Mode Controller. Hence, the lumped uncertainty is estimated by an adaptive law. The stability of the closed-loop system is proved based on the Lyapunov stability theorem. The simulation results demonstrate the superior performance of the proposed adaptive Dynamic Sliding Mode Control strategy.

This paper proposes a Control strategy for a cable-suspended robot based on Sliding Mode approach (SMC) which is faced to external disturbances and parametric uncertainties. This Control algorithm is based on Lyapunov technique which is able to provide the stability of the end-effecter during tracking a desired path with an acceptable precision. The main contribution of the paper is to calculate the Dynamic Load Carrying Capacity (DLCC) of a spatial cable robot while tracking a desired trajectory based on SMC algorithm. In finale, the efficiency of the proposed method is illustrated by performing some simulation studies on the ICaSbot (IUST Cable Suspended Robot) which supports 6 DOFs using six cables. Simulation and experimental results confirm the validity of the authors’ claim corresponding to the accurate tracking capability of the proposed Control, its robustness and its capability toward DLCC calculation.

In this paper, the problem of Control a single-stage boost inverter is studied. The goal is to achieve a system with robustness against variations in parameters, fast response, high-quality AC voltage, and smooth DC current. To this end, a new type of Dynamic Sliding Mode Control is proposed to apply to various scenarios such as parameter uncertainties and DC input voltages. In comparison with the conventional double-loop Controllers, the proposed Sliding Mode Controller utilizes only a single loop in its design, while having attractive features such as robustness against parametric uncertainties. In addition, a methodology is proposed for the decoupling of double-frequency power ripples based on proportional-resonant (PR) Control to remove the low-frequency current ripples without using additional power components. Compared to conventional Controllers, the proposed Controller provides several features such as fast and chattering-free response, robustness against uncertainty in the parameters, smooth Control, proper steady-state error, decoupled power and good total harmonic distortion (THD) over the output voltage and input currents, and simple implementation. In a fair comparison with classical Sliding Mode Control, simulation results demonstrate more satisfactory performance and effectiveness of the proposed Control method.