This paper presents an AC-DC converter system tailored for grid-to-vehicle (G2V) applications, aimed at facilitating efficient power flow while achieving a Unity power factor (UPF). The system employs a rectifier for AC-DC conversion, which effectively steps up a 230V AC input to a 380V DC output. This DC output can be further regulated using a buck converter to meet specific load requirements. A Proportional-Integral (PI) controller is implemented to oversee the voltage and current regulation, thereby minimizing harmonic distortion and enhancing the overall power factor. By actively managing the input voltage and current, the controller ensures that the system operates within desired parameters, thus optimizing power quality. Comprehensive simulation results validate the system's performance, demonstrating its capability to maintain a UPF in G2V mode. The findings indicate significant reductions in total harmonic distortion (THD), reinforcing the system's effectiveness in managing power quality. This AC-DC converter design not only enhances the efficiency of power flow in electric vehicle charging systems but also contributes to the stability of the grid by minimizing reactive power and harmonics. Overall, this work represents a significant advancement in converter technology for sustainable transportation and energy management.

This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter-driven DC motor system. The proposed technique integrates an adaptive polynomial-neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network-based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady-state performance. The stability of the entire closed-loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start-up, step-reference tracking, and external step-load torque disturbances. The real-time experimentation is conducted on a laboratory prototype of power converter-driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to (Formula presented.) and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.

In this work, energy management in Battery Supercapacitor (SC) based Electric Vehicle (EV) is investigated from a control theory perspective. An Energy Management System (EMS) based on Sliding Mode Control (SMC) is proposed to distribute power suitably among the energy sources. The energy management problem is formulated in terms of power and energy of the two energy sources. The Rate of Change (RoC) of reference battery power is directly controlled by the SMCto maintain a smooth trajectory of reference battery power. The highly fluctuating load power components are managed by the auxiliary power source driven by the SC while keeping its energy within desired limits. Thus the battery is protected from sudden load surge and thereby improving its performance and enhancing its life. The control parameters of the SMC are tuned by using Cuckoo Search optimization technique. Robustness of the proposed SMC based EMS under different driving conditions is assessed by testing its performance on various drive cycles like UDDS, HWFET, SC03, Manhattan and NYCC. Real-time simulator OPAL-RT is used to implement the proposed control strategy. A performance metric based on battery State of Health (SOH) is used to study the impact of battery current change on capacity degradation of the battery. The improved performance quality indices confirm robust and satisfactory performance of the proposed SMC based EMS across a variety of driving conditions. Simulation results ensure that the proposed SMC based EMS has promising potential to reduce ageing stress on the battery and enhance efficiency of EVs.

This chapter addresses a proper step-by-step mathematical procedure for implementing and designing the Type-2/3 compensators using the K-factor method that has not been previously described in any literature. This method offers a methodical and effective means of adjusting the K-factor and getting suitable values for compensator design. Synthesis of feedback compensators with any desired crossover frequency and phase margin is feasible using a few algebraic equations and the presented methodology. Type-2 compensators boost system damping to enhance closed-loop response, whereas Type-3 compensators insert a pole at the origin to eliminate steady-state error. This chapter examines the origin, use, advantages, disadvantages, and applications of the K-factor technique in the context of compensator design.

This article presents a novel control architecture for an enhanced closed-loop speed tracking of a DC–DC buck power converter fed Permanent Magnet DC motor (PMDC) motor in face of large exogenous load torque uncertainty. The proposed architecture combines a new self learning Zernike radial polynomial neural network (ZRNN) estimator with the backstepping controller. The design involves a computationally simple online learning based ZRNN to rapidly and accurately estimate the unknown large load torque uncertainties. The proposed control solution concurrently guarantees stability and excellent dynamic performance through an effective neural network based estimation and subsequent compensation of unanticipated load torque perturbations over a wide range. The closed loop stability of the DC–DC buck power converter driven PMDC motor and asymptotic speed tracking with the proposed neuro-adaptive controller is proved using the stability theory for non-autonomous systems. The effectiveness of the proposed controller has been investigated through experimentation on an indigenously developed laboratory prototype of 200 W under closed loop operation using digital signal processors. The tests conducted around different operating conditions include the motor start-up response, step variations in the load torque, and step changes in the reference speed. Experimental results demonstrate a significant improvement in the speed tracking performance achieving 48.13% reduction in the settling time and no-change in speed during start-up and load torque perturbations upto 600%, respectively. Experimental validations and extensive tests spanning over a large operating region, substantiate the theoretical claims and real-time suitability of the proposed controller for sensitive applications demanding high performance.

Concerning the power quality issues in the power distribution network due to load uncertainties and improper impedance matching of the inductances, deep reinforced learning (DRL)-based inductively coupled DSTATCOM (IC-DSTATCOM) is proposed. First, by analyzing the impedance matching principle, the expression of source, load and filter current is derived with the help of inductive filtering transformer. And second, an individual DRL subnet structure is accumulated for each phase using mathematical equations to perform the better dynamic response. A 10-kVA, 230-V, 50-Hz prototype direct coupled distributed static compensator (DC-DSTATCOM) and IC-DSTATCOM experimental setup is buit to verify the experimental performance under uncertainties of loading. The IC-DSTATCOM is augmented better dynamic performance in terms of harmonics curtailment, improvement in power factor, load balancing, potential regulation, etc. The benchmark IEEE-519-2017, IEC-61727 and IEC-61000-1 grid code are used to evaluate the effectiveness of the simulation and experimental study.

This work investigates the application of Artificial Bee Colony (ABC) optimization for the design of Type compensators utilizing the dual-loop control scheme. The proposed Type compensators integrate the ABC optimization for regulating the closed-loop operation of a DC-DC buck converter. Such an integration of ABC optimization, aids in effectively regulating the output voltage and inductor current, besides ensuring enhanced time domain criteria. The proposed dual-loop control scheme consists of a current loop and a voltage loop. The current loop regulates the inductor current and the voltage loop regulates the output voltage. The efficacy of the proposed method is revealed through extensive simulation and experimental investigation under start-up response, step perturbations in external load. The experimentation is conducted on a laboratory prototype using dspace DS1104 control board.

Since the capacitor current can reflect load variations faster than the peak inductor current, incorporating the capacitor current into control loops helps to improve the transient response of the dc-dc converter. In this paper, a coupled inductor single input dual output (CI-SIDO) buck converter is investigated under capacitor current ripple (CCR) control. A precise small-signal model for a CCR-controlled CI-SIDO buck converter operating in continuous conduction mode (CCM) is developed. The accurate small-signal model is obtained by substituting the derived CCR controller expressions in the CI-SIDO buck converter state-space model. The CCR controller equations specify the duty ratios as functions of the circuit variables namely the capacitor currents and the output voltages. It is observed that the CI-SIDO buck converter with the CCR controller exhibits instability when both or either of the duty ratios are greater than 0.7 or their sum is greater than 1. The results of the PLECS STANDALONE simulation validate the theoretical propositions as regards the switching instability of the controlled converter.

As the capacitor current can respond to load fluctuations more rapidly than the peak inductor current, incorporating the capacitor current into the current control loop enhances the transient response in DC-DC converters as well as ensures over-current protection and noise immunity. This paper presents a comprehensive small-signal model (SSM) for a capacitor current ripple controlled (CCR) coupled inductor single-input dual-output (CI-SIDO) buck converter. The complete SSM is derived by unifying the developed SSM of the CCR controller with the SSM of the considered power converter using state-space averaging technique. In CCR control, the amalgamation of the comparator and SR flip-flop is accountable for producing the duty cycle. The proposed SSM is of immense usefulness in designing the outer loop controller, deriving switching instability conditions, and analyzing the dynamic characteristics of the capacitor current controlled CI-SIDO buck converter. To evaluate the advantages of this current controller to CI-SIDO buck converter, a load regulation analysis using the SSM of CCR controller is provided and thereafter verified through simulations in MATLAB/Simulink. It is observed that the low frequency gain of the cross and self regulation transfer functions is substantially less signifying promising dynamic and load disturbance rejection capability of the CCR control driven CI-SIDO buck converter.

This article presents a promising self-learning-based robust control for output voltage tracking in DC–DC buck power converters, particularly for applications demanding high precision performance in face of large load uncertainties. The design involves a computationally simple online single hidden layer neural network, to rapidly estimate the unanticipated load changes and exogenous disturbances over a wide range. The controller is designed within a backstepping framework and utilizes the learnt uncertainty from the neural network for subsequent compensation, to eventually ensure an asymptotic stability of the tracking error dynamics. The results obtained feature a significant improvement of dynamic and steady-state performance concurrently for both output voltage and inductor current in contrast to other competent control strategies lately proposed in the literature for similar applications. Extensive numerical simulations and experimentation on a developed laboratory prototype are carried out to justify the practical applicability and feasibility of the proposed controller. Experimental results substantiate the claims of fast dynamic performance in terms of 94% reduction in the settling time, besides an accurate steady-state tracking for both output voltage and inductor current. Moreover, the close resemblance between computational and experimental results is noteworthy and unveils the immense potential of the proposed control system for technology transfer.

This article proposes a novel Zernike radial neural network based adaptive control architecture for closed-loop control of output DC voltage in DC–DC buck power converter. The proposed combination of novel Zernike radial neural network estimator and the adaptive backstepping controller effectively compensates for wide range of perturbations affecting the converter system, in an online manner. The closed loop stability of the DC–DC buck power converter with the proposed neuro-adaptive backstepping controller is shown using Lyapunov stability criterion. Numerical simulations are conducted to examine the effectiveness of the proposed controller under start-up response and step changes in the load, source voltage and reference output voltage. Furthermore, the simulation findings are validated by conducting extensive real-time investigation on a laboratory prototype, under a wide range of operating points. The results obtained show a significant improvement in the transient response of both output voltage and inductor current of the converter, relative to the relevant control methods proposed in the recent past.

This study investigates the problem of attitude control design for spacecraft operating in a bandwidth-restricted environment. The proposed control solution should ensure robustness toward unknown inertia and disturbance-induced uncertainties in tandem with an economically efficient utilization of network resources. The study is conducted is to develop an attitude controller that stabilizes the states of the spacecraft while avoiding unwinding problem. It also needs to provide robustness toward unknown uncertainties and disturbances with minimum use of communication resources. A comparative numerical analysis of the proposed strategy with a newly developed event triggered (ET) sliding mode control (SMC) scheme is also presented.

A novel actuator failure compensation scheme is proposed for affine nonlinear uncertain systems (not necessarily Lipschitz) subject to persistent/intermittent actuator faults/failures unknown in time, magnitude and pattern. The proposed control methodology satisfies the nonlinear separation principle through a modular backstepping control. The controller is then augmented with multiple estimation models to estimate failure induced parametric uncertainties and unknown system parameters. The output transient performance at start up and post-failure instances, is improved on account of a two layer adaptation which enhances the convergence speed and accuracy of parameter estimates. The proposed fault tolerant control (FTC) method yields a faithful accommodation of uncertain finite as well as infinite/intermittent/persistent actuator failures while ensuring satisfactory output transient and steady state performances. Further, compared to existing multiple model based adaptive fault tolerant control design for nonlinear systems, the proposed methodology circumvents the issues of stability due to switching between different models and utilizes a minimum number of estimation models for parameter estimation without compromising on the output performance. Consequently, the computational burden is also reduced. Compared to multiple model adaptive control based FTC strategies proposed earlier which assume finite actuator failures and Lipschitz nonlinear system, the proposed method is applicable to both Lipschitz and non-Lipschitz nonlinear systems affected by intermittent actuator failures. Using the concepts from stability analysis in random nonlinear impulsive systems, the L∞ and L2 bounds on tracking error for all future time are derived in the case of intermittent/persistent actuator failures obtained using the proposed fault-tolerant controller. The improvement of output transient performance in the proposed control scheme in comparison with controller with single identifier, is theoretically proved and quantified.

A novel, robust attitude controller for rigid bodies in the presence of time-varying orientation constraints is presented in this paper. Using an error transformation, the dynamic attitude constraints are converted into time-varying quaternion constraints. Subsequently, a robust attitude control law is synthesized using the backstepping philosophy in which barrier Lyapunov functions (BLFs) are used to achieve asymptotic tracking and simultaneously avoid attitude constraint violation. This is accomplished by ensuring the boundedness of BLFs in the closed-loop Lyapunov stability analysis. Besides the nominal scenario, an adaptive control law is also formulated to tackle moment of inertial uncertainties and an unknown, time-varying, bounded disturbance. In this case, the attitude tracking errors are uniformly ultimately bounded, whose bounds can be adjusted by user-defined constants. Note that in both scenarios, the dynamic attitude constraints are not transgressed. Finally, the effectiveness of the proposed controller is demonstrated by carrying out extensive numerical simulations in the presence of parametric uncertainties and disturbance torques.

A novel neuro-adaptive control scheme is proposed in the context of angular velocity tracking in DC–DC buck converter driven permanent magnet DC motor system. The controller builds upon the idea of backstepping and consists of a fast single hidden layer Hermite neural network (HNN) module equipped with on-board (adaptive) learning to counteract the unknown non-linear time-varying load torque and to ensure nominal tracking performance. The HNN has a simple structure and exhibits promising speed and accuracy in estimating dynamic variations in the unknown load torque apart from being computationally efficient. The proposed method guarantees a rapid recovery of nominal angular velocity tracking under parametric and non-parametric uncertainties. In order to verify the performance of the proposed neuro-adaptive speed controller, extensive experimentation has been conducted in the laboratory under various real-time scenarios. Results are obtained for start-up, time-varying angular velocity tracking and under the influence of highly non-linear unknown load torque. The performance metrics such as peak undershoot/overshoot and settling time are computed to quantify the transient response behaviour. The results clearly substantiate theoretical propositions and demonstrate an enhanced dynamic speed tracking under a wide operating regime, thus confirming the suitability of proposed method for fast industrial applications.

In this paper, an adaptive controller is proposed for DC–DC buck converters featuring prescribed time bound estimation of unknown system uncertainties and exogenous disturbances followed by nominal output performance recovery. The objective of the proposed control is to attain a robust output voltage tracking in buck converter in presence of parametric, non-parametric, matched and mismatched perturbations across wide operating range. Different from neural network estimators and characterizing substantially low computational complexity, an online estimator is presented to reconstruct the incurred uncertainty. The estimated additive uncertainty is thereafter fed to the nominal backstepping controller for subsequent compensation in finite time. Exact recovery of nominal output voltage tracking is claimed in a piecewise sense owing to the accuracy and precise estimation of the unknown unparametrized lumped uncertainty manifested in the form of large sudden variations in load and input voltage. Rigorous performance and stability analysis of the online estimator, along with similar analysis of the overall tracking control system are undertaken. Extensive numerical study is carried out to investigate the performance of the proposed control scheme. Further, experimentation of the proposed controller on a dc–dc buck converter using control desk DS1103 with an embedded TMS320F240 processor has been performed. The obtained experimental results demonstrate a good agreement with the simulation findings.

DC-DC step-down/buck converters are prominent part of DC power supply system. The dynamics of DC-DC step down converter are nonlinear in nature and are largely influenced from both parametric and external load perturbations. Under its closed loop operation, obtaining a precise output voltage tracking besides satisfactorily inductor current response is a challenging control objective. In this regard, this article proposes a novel Laguerre neural network estimation technique for the approximation of unknown and uncertain load function, followed by its subsequent compensation in the adaptive backstepping controller. A detailed design of the proposed estimator and adaptive backstepping controller along with closed loop asymptotic stability have been presented. Further, the proposed control mechanism is evaluated through extensive numerical simulations while subjecting the converter to input voltage, reference voltage and load resistance perturbations. Furthermore, the results are verified by testing the proposed controller on a laboratory prototype with DSP based TM320F240 controller board. The transient performance metrics such as settling time and peak overshoot/undershoot are evaluated and compared against adaptive backstepping control and PID control methods. Finally, the analysis of results reveals that the proposed control methodology for DC-DC step down converter offers a faster transient output voltage tracking with smooth and satisfactory inductor current response over a wide operating range.

This paper deals with the output voltage regulation problem of dc-dc boost converter feeding a resistive load. A new control mechanism based on Chebyshev neural network embedded in an adaptive backstepping framework is proposed for the boost converter control. Since the converter is complex, time varying and non-linear in nature, it exhibits high sensitivity to unanticipated disturbances in the load current. Hence, designing a robust control mechanism to attain a satisfactory transient and steady state performance over a wide range of operating points is a challenging task. In this work, a control law is derived based on the systematic and recursive design strategy of adaptive backstepping method. A single layer functional link Chebyshev neural network is employed for a fast estimation of uncertain and time varying load profile of the boost converter. The stability of overall converter equipped with the proposed controller is proved using Lyapunov stability criterion. Further, in order to validate the proposed methodology, the boost converter is simulated in MATLAB/Simulink software and is subjected to different load perturbations. The efficacy of the proposed control is highlighted by evaluating it against the conventional adaptive backstepping control under identical conditions. The results obtained reveals that the proposed control is much faster in estimating the unknown load parameter and offers satisfactory output voltage tracking, yielding fast response and low peak overshoot/undershoot in the event of unknown load perturbations. Experimental investigation using dspace DS1103 controller is further carried out to validate the efficacy of proposed control scheme.

An affiliation for Dr. Tousif Khan Nizami is missing from the original article. In addition to the Department of Electronics and Electrical Engineering at Indian Institute of Technology, Dr. Tousif Khan Nizami is affiliated with the Department of Electrical and Electronics Engineering at SRM University-AP, Amaravati 522 502, India.

Over the past few years, networked control systems (NCSs) have shown rapid progress and have indeed been very popular in terms of research as well as industrial applications. The issue of limited resources has been a fundamental problem in the translation of modern control techniques to NCS design. In order to address the aforesaid design challenge, an improved event-triggered adaptive backstepping control scheme is presented in this study for a class of uncertain NCSs with non-Lipschitz non-linearities under limited resources. Rather than a preselected constant threshold assumption, a well-designed and systematic triggering rule is derived based on the Lyapunov approach in order to satisfy bandwidth limitation and ensure system stability with acceptable transient performance. Relative to existing strategies in the literature, the proposed method leads to a substantially reduced number of transmissions with longer inter-event time. Thereby, the proposed algorithm exhibits more efficiency in resource utilisation. Simulation results on a networked control based robotic manipulator system illustrate the efficacy of the proposed adaptive scheme compared to benchmark control algorithms intended for a similar application.

A nonholonomic mobile robot, affected by parameter uncertainties and controlled over a communication network under limited bandwidth, is considered in this paper. Adaptive backstepping controller is designed to compensate the uncertainties in the model parameters. Thereafter, an event-triggered scheme is proposed based on a Lyapunov-based triggering condition in order to reduce the unnecessary utilization of network resources. Compared to traditional time-triggered implementation, simulation results show that the proposed control scheme successfully ensures faithful trajectory tracking with substantial saving of input resources characterized by number of required control signal transmissions.

In this paper, the issue of output voltage regulation in buck type dc-dc converters is addressed using a current sensorless control technique. The proposed strategy integrates a finite time current observer with an adaptive backstepping control scheme to yield a cost-effective and robust control mechanism. The overall controller stability in the sense of Lyapunov is proved. Applicability of the proposed control is verified experimentally on a buck converter in the laboratory. The control scheme is implemented on dSPACE DS1103 platform based on DSP TM320F240 processor. To examine the efficacy of the proposed method, the buck converter is subjected to a wide change in input voltage, load resistance and reference voltage. For comparison purpose, a conventional adaptive backstepping control scheme is evaluated under identical conditions of experimental study to examine the merit of the proposed control. The results obtained reveal that the proposed control is prompt in rejecting perturbations and achieves a smooth, reliable and satisfactory output voltage regulation with faithful and time bound estimation of inductor current. Thereby, this investigation demonstrates the validity of the proposed control in maintaining a stringent output voltage regulation in buck converters.

A neuro-adaptive backstepping control (NABSC) method using single-layer Chebyshev polynomial based neural network is proposed for the angular velocity tracking in buck converter fed permanent magnet dc (PMDC)-motor. Owing to their universal approximation property, neural networks have been utilized for approximating the unknown nonlinear profile of instantaneous load torque. The inherent computational complexity of the neural network based adaptive scheme has been circumvented through the use of orthogonal Chebyshev polynomials as basis functions. A detailed stability and transient performance analysis has been conducted using Lyapunov stability criteria. The proposed control scheme is shown to yield a superior output performance with enhanced robustness for wide variations in load torque and set-point changes, compared to existing conventional approaches based on adaptive backstepping. The theoretical propositions are verified on an experimental prototype using dSPACE, Control Desk DS1103 setup with an embedded TM320F240 Digital Signal Processor proving its applicability to real-time electrical systems. The efficiency of the proposed strategy is quantified using performance measures and are evaluated against the conventional adaptive backstepping control (ABSC) methodology. Ultimately, this investigation confirms the effectiveness of the proposed control scheme in achieving an enhanced output transient performance while faithfully realizing its control objective in the event of abrupt and uncertain load variations.

This paper proposes a novel integrated robust system identification and state of charge estimation of Li-ion battery. A finite time extended state observer is used for state of charge (SOC) estimation of Li-ion battery. A relay feedback test has been adopted to yield accurate battery parameters in noisy environment. New identification method using state space based relay feedback approach is employed with a disturbance nullifier to extract the parameters of battery model. Thereafter, the objective of SOC estimation is carried out by designing a novel finite time extended state observer based on higher order sliding modes. The unknown battery model parameters are determined first, followed by the estimation of SOC next, under both charging and discharging conditions. This investigation finds a high amount of accuracy in the proposed battery model identification method, besides an accurate and time bound estimation of SOC using the proposed observer scheme.

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.

In this paper, a novel actuator failure compensation scheme is proposed for affine nonlinear uncertain systems subject to actuator faults/failures unknown in time, magnitude and pattern. The proposed control methodology utilizes a backstepping procedure integrated with multiple estimation models to estimate failure induced parametric uncertainties and unknown system parameters. Relative to existing direct adaptive backstepping based fault compensation strategies, the proposed fault tolerant control (FTC) method yields a faithful accommodation of uncertain actuator failures while ensuring satisfactory output transient and steady state performances. Further, compared to multiple model based adaptive FTC design, the proposed methodology circumvents the issues of stability due to switching between different models and utilizes a minimum number of estimation models for parameter estimation without sacrificing the output performance and thereby reducing the computational burden. Simulation results illustrate the effectiveness and applicability of the proposed method to FTC design problem for longitudinal pitch control of Boeing 747-100/200 aircraft.

This paper presents a novel fast learning neural network for the estimation of load torque in PMDC motor. The control objective of angular velocity trajectory tracking is achieved by designing a controller for cascaded Buck converter PMDC motor system by utilizing an adaptive backstepping methodology augmented with a new Type-II Chebyshev neural network (CNN). The online learning laws for the neural network are developed, satisfying overall closed loop system stability using Lyapunov stability criterion. A rigorous stability analysis has been provided. Performance of the proposed control method is validated on a digital platform using dSPACE Control Desk DS1103 set-up with TM320F240 Digital Signal Processor. The dynamic response of Buck converter driven PMDC motor is examined for settling time, peak undershoot and overshoot guaranteeing the transient performance under conventional adaptive backstepping control and Type-I CNN based adaptive backstepping control techniques. Further, such results are compared with those obtained using the proposed method under start-up, wide range variations in load torque and reference trajectory.

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.

In this paper, a novel adaptive failure compensation scheme is designed for near space vehicles (NSVs) which belong to a class of affine nonlinear MIMO systems, prone to actuator float free, stuck and oscillatory failures, unknown in time, pattern and magnitude. The proposed control methodology utilizes a backstepping procedure based on an adaptive finite time disturbance observer to estimate failure induced uncertainties and external disturbances. The proposed failure compensation scheme ensures effective failure accommodation in finite time while providing excellent transient and steady state performances even when three input failures occur simultaneously. Stability and asymptotic tracking in presence of unknown actuator failures at unknown time instants using the proposed control scheme as well as finite time stability of the adaptive finite time disturbance observer is proved using Lyapunov criterion. Simulation results illustrate the effectiveness of the proposed methodology in application to NSVs.

A robust fault-tolerant control scheme is proposed for uncertain nonlinear systems with zero dynamics, affected by actuator faults and lock-in-place and float failures. The proposed controller utilizes an adaptive second-order sliding mode strategy integrated with the backstepping procedure, retaining the benefits of both the methodologies. A Lyapunov stability analysis has been conducted, which unfolds the advantages offered by the proposed methodology in the presence of inherent modeling errors and strong eventualities of faults and failures. Two modified adaptive laws have been formulated, to approximate the bounds of uncertainties due to modeling and to estimate the fault-induced parametric uncertainties. The proposed scheme ensures robustness towards linearly parameterized mismatched uncertainties, in addition to parametric and nonparametric matched perturbations. The proposed controller has been shown to yield an improved post-fault transient performance without any additional expense in the control energy spent. The proposed scheme is applied to the pitch control problem of a nonlinear longitudinal model of Boeing 747-100/200 aircraft. Simulation results support theoretical propositions and confirm that the proposed controller produces superior post-fault transient performance compared with already existing approaches designed for similar applications. Besides, the asymptotic stability of the overall controlled system is also established in the event of such faults and failures.

In this paper, a failure compensation scheme is designed for affine nonlinear systems prone to actuator stuck failures unknown in time, magnitude and pattern. The design scheme utilizes an adaptive second order sliding mode control developed in a backstepping framework exploiting the advantages of both the methodologies. An adaptive law is used to estimate the unknown upper bounds of uncertainties introduced due to the occurrence of actuator failures and guarantees a globally bounded estimation. The proposed failure compensation scheme ensures effective failure accommodation while providing excellent transient and steady state performances compared to the basic scheme based on backstepping, with chattering free control inputs. Stability and asymptotic tracking in presence of unknown actuator failures at unknown time instants is proved for the proposed control scheme using Lyapunov criterion. Simulation results illustrate the effectiveness of the proposed methodology.

A fault tolerant control (FTC) scheme for tracking control is proposed for nonlinear uncertain systems affected by actuator faults and matched uncertainties with unknown upper bound. The proposed controller utilizes backstepping control which is integrated with integral sliding mode control method. For designing the integral sliding mode controller, an adaptive law is used to estimate the unknown upper bounds of faults and uncertainties. The proposed FTC ensures satisfactory transient and steady state performances with a chattering free control input. Asymptotic stability in presence of matched uncertainties and partial loss of actuator effectiveness is guaranteed in the proposed control system. The efficiency of the proposed fault tolerant control scheme is demonstrated by simulation results. © 2013 IEEE.