Search

The adoption of electrical vehicles (EVs) is nowadays a center of attraction, which reduces the carbon footprint and adds to the economic growth of a country. However, providing a proper charging facility to the customers is still a challenging task. This paper presents the mix (AC‐DC) charging station (CS) integrated with the PV array and the smart distribution grid. The acceleration of the EV causes the power quality burden on the grid and this can be taken care of by using advanced adaptive controls. In this paper, the control strategy used for the CS is the immune feedback‐based adaptive (IFA) control technique. It reduces the ripples in the fundamental load current extraction under steady‐state and produces fast response under dynamic conditions and helps to synchronize CS with the grid. The multi‐functional capabilities of the CS are presented with different voltage levels DC fast off‐board chargers and AC onboard slow chargers. Moreover, the Rao optimization technique is utilized to select the gains for the cascaded proportional‐integral (PI) controllers which are used to regulate the DC bus voltage and battery charging/discharging currents. The optimized fitness values of integral time square error (ITSE) by using Rao1, Rao2, and Rao3 techniques are 297.341, 285.023, and 282.924 respectively. The simulated and test results depict the efficacy of the CS. Moreover, the mixed charging facilities with multi DC fast chargers avoid the rush and burden on the grid, which reduces the power outage across the load and rapidly rising energy cost. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the radial basis function neural network‐based model reference adaptive speed control for vector controlled induction motor drive system is presented. The speed control of induction motors is challenging because of their complex mathematical model, non‐linear structure, and time varying dynamics. The radial basis function neural network is used to compensate the non‐linearity which comes from the non‐linear state equations of induction motor model. Neural network parameters are online updated via gradient descent algorithm to minimize the error. The drive system has been tested under various operating conditions. This paper demonstrated benefits of the proposed control approach by comparing the algorithm to conventional PI controllers. The results show that the proposed controller ensures good robustness and stable operation of the system under variable speed and variable loads than the PI controller. [ABSTRACT FROM AUTHOR]

This paper presents a state‐dependent self‐tuning fractional control strategy for a DC‐DC buck converter in order to enhance its output voltage regulation and disturbance attenuation capability. The proposed control scheme primarily employs a ubiquitous proportional‐integral‐derivative (PID) controller, where gains are optimally selected using a linear‐quadratic state‐space tuning approach. The optimal PID controller is then augmented with fractional‐order integral and derivative operators in order to improve the controller's degrees‐of‐freedom as well as the system's overall time‐domain performance. The fractional controller's robustness against bounded exogenous disturbances, contributed by the input fluctuations and load‐step transients, is further enhanced by adaptively modulating the fractional‐orders of the integro‐differential operators as a smooth nonlinear function of controlled‐variable's error‐dynamics. An online dynamic adjustment law, comprising of a zero‐mean Gaussian function of error and its derivative, is used to individually update the two fractional orders after every sampling interval. The error derivative is evaluated by measuring the output capacitor's current in order to compensate the noise injected by parasitic impedance. The other controller parameters are tuned via particle‐swarm‐optimization algorithm. The proposed self‐adaptive control strategy renders rapid transits, minimum transient recovery time, and minimal fluctuations around steady state in the response. Its efficacy is validated through hardware in‐the‐loop experiments conducted on a buck converter prototype. [ABSTRACT FROM AUTHOR]

Abstract: This work presents an accurate algorithm for solving the chaotic control (optimal and adaptive control) problems of hyperchaotic finance system. A modification of the spectral homotopy analysis method technique is proposed for solving the hyperchaotic finance system and the extreme conditions obtained from the Pontryagin minimum principle. The proposed method is based on implementing the spectral homotopy analysis method on a sequence of multiple intervals thereby increasing its radius of convergence to yield highly accurate method that is referred to as the piecewise spectral homotopy analysis method. Finally, piecewise spectral homotopy analysis method is used to solve the problem of Lyapunov stabilization of the hyperchaotic finance system. Numerical simulations are given to illustrate and verify effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

A switched reluctance motor (SRM) circuit drive system caused many nonlinear effects due to convex construction. The linear control methods were hard to achieve good performance for the SRM circuit drive. The adaptive nonlinear backstepping control system using switching function is proposed for controlling the SRM drive system to obtain good performance. To reduce chattering of control effort, the adaptive nonlinear backstepping control system using adaptive law is proposed to estimate the required lumped uncertainty. When the inertia of the counterweight is varying, this proposed method cannot get a satisfactory performance. The adaptive nonlinear backstepping control system using mended recurrent Romanovski polynomials neural network with adaptive law and error‐estimated law is proposed for controlling the SRM drive system to raise robustness of the SRM drive system. Furthermore, two variable learning rates in the mended recurrent Romanovski polynomials neural network are adopted by using mended particle swarm optimization (PSO) algorithm to speed up parameter's convergence. Finally, comparative performances through some experimental results are verified that the proposed control system has better control performances than those of the proposed methods for the SRM drive system. [ABSTRACT FROM AUTHOR]