Tousif Khan Nizami

Adaptive backstepping control for DC-DC buck converters using Chebyshev neural network

This paper proposes a novel control technique for the Buck type DC-DC converters using adaptive backstepping control and Chebyshev neural network. To enhance the transient performance of both the capacitor voltage and the inductor current under nominal conditions, input voltage fluctuations and load variations, this control algorithm has been proposed. The systematic design of backstepping controller has been improvised by incorporating the approximation of unknown load resistance parameter by a single layer Chebyshev neural network. Results have been compared with a recently developed adaptive terminal sliding mode control technique. The proposed method significantly improves voltage and current transient performances.

An intelligent adaptive control of DC–DC buck converters

Abstract Buck DC–DC converter is used in many applications to supply a fixed amount of DC voltage. They are highly sensitive to the frequently changing loading conditions. Such a situation demands a robust control mechanism which can guarantee satisfactory performance of the buck converter over a widely changing load. This can be made possible by developing an adaptive control scheme which can estimate the true values of the uncertain load parameters in the least possible time. This paper proposes an adaptive Chebyshev neural network (CNN) based backstepping control technique for the output voltage regulation of a DC–DC buck converter. The proposed control strategy utilizes neural networks in approximating the unknown non-linear nature of load resistance by using orthogonal basis Chebyshev polynomials. CNN approximation tool in conjunction with the conventional backstepping procedure yields a robust control mechanism. The weights of neural network are tuned online using adaptive laws satisfying the overall closed loop stability criterion in the Lyapunov sense. The performance of the proposed control is demonstrated for wide range perturbations by subjecting the buck converter to changes in load resistance, input voltage and reference output voltage. Simulation studies are conducted to evaluate the performance of the proposed controller against radial basis function neural network based adaptive backstepping control and conventional adaptive backstepping. The results obtained are further verified from experimentation on a hardware setup using DSP based TM320F240 processor. Thus, the investigation confirms effectiveness of the proposed control scheme as the output voltage shows a fast and accurate response besides successfully rejecting the disturbances acting upon it.

Hybrid Backstepping Control for DC–DC Buck Converters

This paper presents a backstepping control technique in combination with the sliding-mode mechanism for simultaneous control of the capacitor voltage and inductor current in a DC–DC buck converter. The proposed hybrid controller is capable of tackling both the matched and mismatched types of uncertainties like input voltage change and load current variation. The backstepping control can reject both matched and mismatched types of uncertainties, whereas the sliding-mode control is robust against matched uncertainties only. The systematic controller design procedure of backstepping and invariance property of SMC for matched uncertainty have been utilized for robust tracking of both the capacitor voltage and inductor current simultaneously. It is found that by switching between these two different control structures, one exclusively for the matched and the other for the mismatched uncertainties, excellent transient and steady-state performances can be ensured. In the case of backstepping control, performance of the buck converter is largely dependent on design parameters. Hence, these design parameters are judiciously selected to assure optimum performance. Simulation studies have been carried out to verify the effectiveness of proposed hybrid control structure. Transient performances like peak overshoot, peak undershoot, settling time, and also steady-state error have been measured under widely varying changes in input voltage and load current. Simulation results demonstrate that as compared to existing controllers, the proposed hybrid control strategy offers superior transient and steady-state performances.

Finite time current observer based adaptive backstepping control of buck converters

A finite time current observer based adaptive backstepping control strategy is proposed for the output voltage regulation of buck type DC-DC converters. The proposed current observer is designed by utilizing only the output voltage information to reconstruct the inductor current profile while achieving an adaptive control by means of backstepping procedure. This method eliminates the usage of extra sensor involved in sensing the inductor current, thereby reducing the cost of control besides overcoming the problems of measurement noise encountered while sensing. Simulations have been performed on a buck converter using Matlab software under both continuous and discontinuous conduction modes. Further, the usefulness of proposed scheme is also examined by subjecting the buck converter system to sudden changes in load resistance. The results obtained reveal that the proposed observer is successful in not only estimating the nominal inductor current but also estimates the perturbed level of inductor current under load disturbances in finite time. Satisfactorily transient and steady state response in the output voltage are ensured by using the proposed method.

Real time implementation of an adaptive backstepping control of buck converter PMDC-motor combinations

This article presents an experimental realization of adaptive backstepping control methodology on a cascaded buck converter permanent magnet dc (PMDC)-motor combination for angular velocity control. The experiment aims at illustrating the practical applicability of adaptive control to power converters fed with a DC motor load. The systematic design procedure of conventional backstepping control design is enhanced by incorporating an online adaptive control mechanism to estimate the unknown non-linear load torque. Asymptotic stability of the closed loop system under the action of proposed control law is ensured and update law is derived satisfying Lyapunov stability criterion. The experimental investigation is conducted using dSPACE, Control Desk DS1103 setup with an embedded TM320F240 Digital Signal Processor. The buck dc-dc converter fed PMDC motor system is subjected to a wide variation in load torque and set point angular velocity tracking. The results obtained through adaptive backstepping control scheme have been evaluated against the conventional backstepping control mechanism. Results highlight a superior performance using adaptive backstepping control by producing an accurate and time bound estimation of unknown load torque, under both nominal and perturbed conditions, thereby improving the transient and steady state response of desired angular velocity.

Single layer type II Chebyshev neural network based adaptive backstepping control of DC-DC buck converter

This article presents a novel adaptive control method based on neural networks for robust output voltage tracking in buck converters over a wide operating range. Buck converters are significantly sensitive to input, parametric and load perturbations. The intrusion of mismatched uncertainties due to load changes make the controller design task a challenging issue. Hence, a feedback control law based on the adaptive backstepping control technique integrated with a single layer type II Chebyshev neural network (CNN) is proposed. The distinctive feature of the type II CNN is its quick and accurate estimation of time varying load disturbance which is thereafter utilized for subsequent compensation in the control law. The neural networks are trained online using a Lyapunov based learning algorithm. The efficacy of the proposed control is studied for wide variations in load resistance, input voltage and reference voltage and compared against control using conventional adaptive backstepping method. Simulations are performed in MATLAB tool and experimentation is conducted using dSPACE DS1103 setup with TM320F240 DSP. The results demonstrate a good agreement between the simulation and experimental findings. Further, the proposed control achieves a remarkable reduction in settling time and peak overshoot/undershoot in the event of occurrence of unanticipated disturbances.

A feedback control design of buck converter: An artificial immune system based approach

This article presents design of a Proportional-Integral-Derivative (PID) control for a buck converter by incorporating the principles derived from the processes of Artificial Immune System present in vertebrates in the control design algorithm. The buck converter represents a class of variable structure systems and its controller design by conventional means yields a near-satisfactory performance, however not the best transient and steady state dynamics at wider range of operating points. Therefore a feedback control design problem of buck converter is rearranged as an optimization goal and the concern parameters of the PID controller are found through an intelligent Artificial Immune System (AIS) based technique. Computations have been done by using Matlab software. The output voltage response of buck converter is tested for 1.) reference voltage change, 2.) load resistance change and 3.) input voltage change. To verify the findings obtained in simulations, a prototype of buck converter is build and controlled in the laboratory with the proposed methodology. The results found are then evaluated against the performance of conventional controller design and genetic algorithm (GA) based approach, followed by tabulation of performance measures, which clearly indicates that the AIS method of PID controller design provides better static and dynamic response in the output voltage besides achieving a faster convergence of parameters, thereby confirming the validity of new approach.

Experimental investigations into a hybrid control algorithm for DC-DC buck converters

An experimental investigation into a hybrid backstepping control proposed for the output voltage regulation of DC-DC buck converter is carried out in this paper. The proposed hybrid control algorithm utilizes a backstepping procedure in conjunction with a robust sliding mode control mechanism to counter both matched and mismatched uncertainties. An experimental prototype of buck converter is fabricated and controlled with the hybrid control scheme in the laboratory. The dynamic response under wide variation of operating points is investigated. The control scheme is further extended to a discontinuous conduction mode buck converter which is even more nonlinear in nature. A DSP based DS1103 platform is used to conduct experimentation. Experimental results demonstrate a satisfactory transient and steady state performance of both output voltage and inductor current.

Analysis and experimental investigation into a finite time current observer based adaptive backstepping control of buck converters

A finite time current observer based adaptive backstepping control strategy is proposed for the output voltage regulation of buck type DC-DC converters. The proposed current observer is designed by utilizing only the output voltage information to reconstruct the inductor current profile while achieving an adaptive control by means of backstepping procedure. This method eliminates the usage of extra sensor involved in sensing the inductor current, thereby reducing the cost of control besides overcoming the problems of measurement noise encountered while sensing. Simulations have been performed on a buck converter using Matlab software under both continuous and discontinuous conduction modes. Further, the usefulness of proposed scheme is also examined by subjecting the buck converter system to sudden changes in load resistance. The results obtained reveal that the proposed observer is successful in not only estimating the nominal inductor current but also estimates the perturbed level of inductor current under load disturbances in finite time. Satisfactorily transient and steady state response in the output voltage are ensured by using the proposed method.

Fast neuro-adaptive control of DC-DC buck converters: Design and implementation

A fast neuro-adaptive compensation based control scheme is proposed for dc-dc buck converters. The compensation of uncertainties is obtained by design and development of a single functional layer Hermite neural network. The estimates are further utilized for subsequent compensation in the online adaptation process using backstepping control. The stability of over all closed loop converter equipped with the proposed control is proved to be asymptotically stable under Lyapunov stability criterion. Extensive simulation and experiments are conducted to evaluate the response of buck converter under the action of proposed control at wide operating points. Further, the results are compared with recently published relevant control method. Finally, the performances indices suggests a significant improvement in the transient performance by yielding lesser settling time and lower peak overshoot/undershoot under proposed control, thereby confirming the validity proposed scheme.