Total Sliding-mode Control Research Articles

Adaptive Fuzzy-Neural-Network Design for Voltage Tracking Control of a DC–DC Boost Converter

In this study, an adaptive fuzzy-neural-network control (AFNNC) scheme is designed for the voltage tracking control of a conventional dc–dc boost converter. First, the description of the circuit framework of a conventional boost converter and system modeling is introduced. Then, a total sliding-mode control (TSMC) strategy without the reaching phase in the conventional SMC is developed for enhancing system robustness during the transient response of the voltage control. In order to alleviate the control chattering phenomena caused by the sign function in the TSMC design and relax the requirement of detailed system dynamics, an AFNNC scheme is further investigated to imitate the TSMC law for the boost converter. In the AFNNC scheme, online learning algorithms are derived in the sense of Lyapunov stability theorem and projection algorithm to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The output of the AFNNC scheme can be easily supplied to the duty cycle of the power switch in the boost converter without strict constraints on control parameters selection in conventional control strategies. In addition, the effectiveness of the proposed AFNNC scheme is verified by realistic experimentations, and its advantages are indicated in comparison with the TSMC strategy.

Design of Voltage Tracking Control for DC–DC Boost Converter Via Total Sliding-Mode Technique

In this paper, a total sliding-mode control (TSMC) scheme is designed for the voltage tracking control of a conventional dc-dc boost converter. This control strategy is derived in the sense of Lyapunov stability theorem such that the stable tracking performance can be ensured under the occurrence of system uncertainties. The salient feature of this control scheme is that the controlled system has a total sliding motion without a reaching phase as in conventional sliding-mode control (CSMC). Moreover, the effectiveness of the proposed TSMC scheme is verified by numerical simulations and realistic experimentations, and the advantages of good transient response and robustness to uncertainties are indicated in comparison with a conventional proportional-integral control system and a CSMC scheme.

Sliding Mode Predictive Control Method for Attitude Control of Networked Launch Vehicle

A novel adaptive fuzzy sliding mode predictive control (AFSMPC) method for solving the networked launch vehicle attitude control system with network-induced delay, uncertain parameters and outer disturbances is proposed in this paper for the attitude stabilization of the networked launch vehicle control system. The proposed sliding mode surface includes a predictor to compensate for the network-induced delay of the NCS. Then, for the networked Launch vehicle attitude control system with uncertain parameters and outer disturbances, a total sliding-mode control system is proposed, which is designed without the reaching phase of a conventional sliding-mode control. In order to attenuate the chattering phenomena brought by the proposed control, a fuzzy logic system is designed to mimic the good behavior of a total sliding-mode predictive control system. Finally, Simulation results show that the proposed control scheme is effective.

Experimental verification of total sliding-mode control for Chua's chaotic circuit

This study mainly focuses on the development of a total sliding-mode control (TSMC) strategy for a Chua's chaotic circuit. The TSMC scheme, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, comprises the baseline model design and the curbing controller design. In the baseline model design, a computed torque controller is designed to cancel the non-linearity of the nominal plant. In the curbing controller design, an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore the controlled system has a total sliding motion without a reaching phase in the TSMC system. The effectiveness of the proposed TSMC scheme is verified by experimental results, and the advantages of good transient response and robustness to uncertainties are indicated in comparison with a conventional sliding-mode control system.

Intelligent transportation control system design using wavelet neural network and PID-type learning algorithms

In this paper, an intelligent transportation control system (ITCS) using wavelet neural network (WNN) and proportional-integral-derivative-type (PID-type) learning algorithms is developed to increase the safety and efficiency in transportation process. The proposed control system is composed of a neural controller and an auxiliary compensation controller. The neural controller acts as the main tracking controller, which is designed via a WNN to mimic the merits of an ideal total sliding-mode control (TSMC) law. The PID-type learning algorithms are derived from the Lyapunov stability theorem, which are utilized to adjust the parameters of WNN on-line for further assuring system stability and obtaining a fast convergence. Moreover, based on H∞ control technique, the auxiliary compensation controller is developed to attenuate the effect of the approximation error between WNN and ideal TSMC law, so that the desired attenuation level can be achieved. Finally, to investigate the effectiveness of the proposed control strategy, it is applied to control a marine transportation system and a land transportation system. The simulation results demonstrate that the proposed WNN-based ITCS with PID-type learning algorithms can achieve favorable control performance than other control methods.

Active Low-Frequency Ripple Control for Clean-Energy Power-Conditioning Mechanism

This paper focuses on the design of an active low-frequency ripple control for a clean-energy power-conditioning mechanism with an aim to protect clean-energy sources (e.g., solar photovoltaics, fuel cells, etc.) from the severe damage of current-ripple propagation to expand their life span. First, a simplified circuit for representing the current-ripple phenomenon inside a general power conditioner including a dc/dc converter and a dc/ac inverter is derived, and the dynamic model of the active low-frequency ripple-control circuit is analyzed. Moreover, an adaptive linear neural network is taken as a neural filter to generate the compensation current command, and a total sliding-mode controller is designed to manipulate the ripple-control circuit for injecting a suitable compensation current into the high-voltage bus of the conditioner. In addition, the effectiveness of the proposed active low-frequency ripple-control scheme is verified by numerical simulations and experimental results. Its superiority is indicated in comparison with a conventional high-pass filter and a proportional-integral controller.

On-Line Supervisory Control Design for Maglev Transportation System via Total Sliding-Mode Approach and Particle Swarm Optimization

This study focuses on the design of an on-line levitation and propulsion control for a magnetic-levitation (maglev) transportation system. First, the dynamic model of a maglev transportation system including levitated electromagnets driven by linear servo amplifiers and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed. Then, a total sliding-mode (TS) control strategy is introduced, and the concept of TS control is incorporated into particle swarm optimization (PSO) to form an on-line TSPSO control framework with varied inertial weights for preserving the robust control characteristics and reducing the chattering control phenomena of TS control. In this TSPSO control scheme, a PSO control system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TS control without strict constraint and detailed system knowledge. In order to further directly stabilize the system states around a predefined bound region and effectively accelerate the searching speed of the PSO control, a supervisory mechanism is embedded into the TSPSO control to constitute a supervisory TSPSO (STSPSO) control strategy. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations and experimental results, and the superiority of the STSPSO control scheme is indicated in comparison with the adaptive fuzzy neural network, PSO-based proportional-integral-differential, TS and TSPSO control strategies.

Design of backstepping particle-swarm-optimisation control for maglev transportation system

This study focuses on the design of a backstepping particle-swarm-optimisation control (BSPSOC) for the on-line levitated balancing and propulsive positioning of a magnetic levitation (maglev) transportation system. The dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is first constructed. The aim is to design an on-line particle-swarm-optimisation (PSO) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BSPSOC scheme, a PSO control is utilised to be the major control role, and adaptation laws derived from Lyapunov stability analyses are manipulated to adjust appropriate evolutionary coefficients. The effectiveness of the proposed control strategy for the maglev transportation system is verified by experimental results, and the superiority of the BSPSOC scheme is indicated in comparison with the total sliding-mode control (TSMC) and BSC strategies.

Speed Synchronization of Multiple Induction Motors with Total Sliding Mode Control

Abstract A speed synchronization control strategy for multiple induction motors, based on adjacent cross-coupling control structure, is developed by employing total sliding mode control method. The proposed control strategy is to stabilize speed tracking of each induction motor while synchronizing its speed with the speed of the other motors so as to make speed synchronization error amongst induction motors converge to zero. The global stability and the convergence of the designed controller are proved by using Lyapunov method. Simulation results demonstrate the effectiveness of the proposed method.

Grid-Connected Photovoltaic Generation System

This study addresses a grid-connected photovoltaic (PV) generation system. In order to make the PV generation system more flexible and expandable, the backstage power circuit is composed of a high step-up converter and a pulsewidth-modulation (PWM) inverter. In the dc-dc power conversion, the high step-up converter is introduced to improve the conversion efficiency of conventional boost converters and to allow the parallel operation of low-voltage PV modules. Moreover, an adaptive total sliding-mode control system is designed for the current control of the PWM inverter to maintain the output current with a higher power factor and less variation under load changes. In addition, an adaptive step-perturbation method is proposed to achieve the objective of maximum power point tracking, and an active sun tracking scheme without any light sensors is investigated to make PV plates face the sun directly in order to capture maximum irradiation and enhance system efficiency. Experimental results are given to verify the validity of the high step-up converter, the PWM inverter control, the ASP method, and the active sun tracker for a grid-connected PV generation system.

High-Performance Stand-Alone Photovoltaic Generation System

This study develops a high-performance stand-alone photovoltaic (PV) generation system. To make the PV generation system more flexible and expandable, the backstage power circuit is composed of a high step-up converter and a pulsewidth-modulation (PWM) inverter. In the dc-dc power conversion, the high step-up converter is introduced to improve the conversion efficiency in conventional boost converters to allow the parallel operation of low-voltage PV arrays, and to decouple and simplify the control design of the PWM inverter. Moreover, an adaptive total sliding-mode control system is designed for the voltage control of the PWM inverter to maintain a sinusoidal output voltage with lower total harmonic distortion and less variation under various output loads. In addition, an active sun tracking scheme without any light sensors is investigated to make the PV modules face the sun directly for capturing the maximum irradiation and promoting system efficiency. Experimental results are given to verify the validity and reliability of the high step-up converter, the PWM inverter control, and the active sun tracker for the high-performance stand-alone PV generation system.

Design of Total Sliding-Mode-Based Genetic Algorithm Control for Hybrid Resonant-Driven Linear Piezoelectric Ceramic Motor

This study presents a total sliding-mode-based genetic algorithm control (TSGAC) system for a linear piezoelectric ceramic motor (LPCM) driven by a newly designed hybrid resonant inverter. First, the motor configuration and driving circuit of an LPCM are introduced, and its hypothetical dynamic model is represented by a nonlinear function with unknown system parameters. In the hybrid resonant drive system, it has the merits of the high voltage gain from a parallel-resonant current source, and the invariant output characteristic from a two-inductance two-capacitance resonant driving circuit. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, a TSGAC system is therefore investigated based on direction-based genetic algorithm with the spirit of total sliding-mode control (TSC) and fuzzy-based evolutionary procedure to achieve high-precision position control under a wide operation range. In this control scheme, a GAC system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TSC without strict constraints and detailed system knowledge. In addition, the effectiveness of the proposed drive and control system is verified by numerical simulations and experimental results in the presence of uncertainties

Robust Decoupled Control of Direct Field-Oriented Induction Motor Drive

This paper focuses on the development of a decoupling mechanism and a speed control scheme based on total sliding-mode control (TSMC) theory for a direct rotor field-oriented (DRFO) induction motor (IM). First, a robust decoupling mechanism including an adaptive flux observer and a sliding-mode current estimator is investigated to decouple the complicated flux and torque dynamics of an IM. The acquired flux angle is utilized for the DRFO object such that the dynamic behavior of the IM is like that of a separately excited dc motor. However, the control performance of the IM is still influenced seriously by the system uncertainties including electrical and mechanical parameter variation, external load disturbance, nonideal field-oriented transient responses, and unmodeled dynamics in practical applications. In order to enhance the robustness of the DRFO IM drive for high-performance applications, a TSMC scheme is constructed without the reaching phase in conventional sliding-mode control (CSMC). The control strategy is derived in the sense of Lyapunov stability theorem such that the stable tracking performance can be ensured under the occurrence of system uncertainties. In addition, numerical simulations as well as experimental results are provided to validate the effectiveness of the developed methodologies in comparison with a model reference adaptive system flux observer and a CSMC system.

Robust position control of a perturbed electrical servo drive system via the adaptive fuzzy sliding-mode technique

A novel adaptive fuzzy sliding-mode control (AFSMC) system for high-precision position control of a perturbed electrical servo drive is developed in this paper. The proposed AFSMC system is designed via the approximation ability of a fuzzy system to mimic the good behaviour of a total sliding-mode control (TSMC) system, which is designed without the reaching phase of a conventional sliding-mode control (SMC). In the developed system, a priori knowledge of the system information is not required. Moreover, the gradually increasing estimate upper bound, which may induce the control effort into saturation and excite unstable system dynamics in some conditions, would not exist. In the proposed controller, the adaptive tuning algorithms are developed in the sense of the Lyapunov stability theorem, so that system-tracking stability can be guaranteed. Finally, the effectiveness of the proposed control scheme is verified via the experimental results of a field-oriented control permanent magnet (PM) synchronous motor.

Adaptive fuzzy position control for electrical servodrive via total-sliding-mode technique

An adaptive fuzzy total-sliding-mode controller (AFTSMC) with a total switching surface is proposed to control the position of an electrical servodrive. The proposed controller comprises a special fuzzy total-sliding-mode controller (FTSMC) and an adaptive tuner. The FTSMC acts as the main tracking controller, which is designed via a fuzzy system to mimic the merits of a total-sliding-mode controller (TSMC). In addition, a translation width is embedded into fuzzy rules to reduce the chattering phenomena. The adaptive tuner, which is derived in the sense of Lyapunov stability theorem, is utilised to adjust the translation width on-line for further assuring robust and optimal performance. In the AFTSMC, the fuzzy-control-rules base is compact and only one parameter needs to be adjusted. An electrical servodrive with the proposed position-control system possesses the advantages of a simple control framework, reduced chattering, stable performance and robustness to uncertainties. The effectiveness is demonstrated by simulated and experimental results, and its advantages are indicated in comparison with conventional TSMC and adaptive TSMC for a field-oriented control induction-motor (IM) drive.

Implementation of robust wavelet-neural-network sliding-mode control for induction servo motor drive

This paper presents the design and properties of a robust wavelet-neural-network sliding-mode control (RWSC) system for an indirect field-oriented induction servo motor drive to track periodic commands. First, a total sliding-mode control (TSC) system with an integral-operation-switching surface, which is insensitive to uncertainties in the whole control process, is introduced. In the TSC system the controlled system has a total sliding motion without a reaching phase. Moreover, to relax the requirement for the bound of uncertainties, a wavelet-neural-network sliding-mode control (WSC) system is investigated to control the induction servo motor. In the WSC system, a wavelet neural network (WNN) is utilized to estimate the bound of uncertainties on line. In addition, an RWSC system is proposed to alleviate the chattering phenomena in the control effort. In the RWSC system, a boundary layer is introduced into the TSC system, and a WNN is used to estimate the width of the boundary layer and the uncertainty bound. Numerical simulation and experimental results due to periodic commands show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties. Furthermore, the advantages of the proposed RWSC system are indicated in comparison with the TSC and WSC systems.

Total sliding-mode controller for PM synchronous servo motor drive using recurrent fuzzy neural network

In this paper, the dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode-controlled permanent-magnet (PM) synchronous servo motor are described. First, a newly designed total sliding-mode control system, which is insensitive to uncertainties, including parameter variations and external disturbance in the whole control process, is introduced. The total sliding-mode control comprises the baseline model design and the curbing controller design. In the baseline model design, a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design, an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system, the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, i.e., the assumption of known uncertainty bounds and the chattering phenomena in the control effort, an RFNN sliding-mode control system is investigated to control the PM synchronous servo motor. In the RFNN sliding-mode control system, an RFNN bound observer is utilized to adjust the uncertainty bounds in real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.

Recurrent-fuzzy-neural-network sliding-mode controlled motor-toggle servomechanism

The dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode controlled motor-toggle servomechanism are described. The servomechanism is a toggle mechanism actuated by a permanent magnet (PM) synchronous servo motor. First, a total sliding-mode control system, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, is introduced. In the baseline model design a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, a RFNN sliding-mode control system is investigated to control the motor-toggle servomechanism. In the RFNN sliding-mode control system a RFNN bound observer is utilized to adjust the uncertainty bounds in real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic sinusoidal command show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.

Intimal Thickness and Layering, and Smooth Muscle Cell Phenotypes in Aorta of Youth

Proneness to the lesions of atherosclerosis varies along the length and circumferential topography of the aorta. Smooth muscle cells, in particular those of the ‘modulated’ synthetic phenotype which are able to proliferate and synthesize matrix proteins, are considered to play an important role in lesion progression. We report on a study of the aortic intima at a lesion-prone site from abdominal aorta and a lesion-resistant site from thoracic aorta in young humans to determine (1) whether the histologic structure and the smooth muscle cell composition show quantitative differences between lesion-prone and lesion-resistant aortic sites; (2) whether there are gender differences, and (3) whether any differences increase in degree with increasing age in this young population. Material for this study was obtained as part of the NIH-funded multicenter study on Pathobiological Determinants of Atherosclerosis in Youth (PDAY) from autopsies of male and female subjects between the ages of 15 and 34, victims of unexpected sudden death, usually from trauma. The samples consisted of strips of abdominal and thoracic aorta, all derived from the same anatomical sites standardized in the PDAY studies. The thickness of total intima (TI) and its elastic hyperplastic (EH) layer was measured. Smooth muscle cells of all types (SMC) and separately those of the synthetic phenotype (SynSMC) were quantified in each site using immunohistochemical procedures in replicate sections of uniform thickness. The intima of the atherosclerotic lesion-prone dorsal half of the abdominal aorta (AD) shows significant differences from the lesion-resistant ventral half of thoracic aorta (TV) in that (1) its EH layer is significantly thicker; (2) its EH layer has a comparatively higher number of both total SMC and SynSMC, even when adjusted for intimal thickness, and (3) the age-related increase in thickness of both TI and EH layer of AD is much greater than that of TV.

Adaptive sliding-mode control for induction servomotor drive

An adaptive sliding-mode control system is proposed to control the position of an induction servomotor drive. First, a newly designed total sliding-mode control system, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, is introduced. The total sliding-mode control comprises the baseline model design and the curbing controller design. In the baseline model design a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system the controlled system has a total sliding motion without a reaching phase. Then, to relax the requirement for the bound of uncertainties, an adaptive sliding-mode control system is investigated to control the induction servomotor. In the adaptive sliding-mode control system a simple adaptive algorithm is utilised to estimate the bound of uncertainties. Simulated and experimental results due to periodic sinusoidal command show that the dynamic behaviours of the proposed control systems are robust with regard to uncertainties.