Total Sliding-mode Control Research Articles

Adaptive Fuzzy-Neural-Network Power Decoupling Strategy for Virtual Synchronous Generator in Micro-Grid

This article proposes an online-trained adaptive fuzzy-neural-network power decoupling (AFNNPD) strategy for a virtual synchronous generator (VSG) control in a micro-grid (MG). First, the mechanism of the power coupling for the VSG control in an MG is analyzed, and the system dynamic model for the proposed power decoupling method is derived. Then, a total sliding-mode control (TSMC) is designed for the power decoupling with the characteristics of the strong robustness and fast dynamic response. Moreover, an adaptive fuzzy-neural-network (AFNN) control is designed to mimic the TSMC law for relaxing the requirement of the detail system information in the TSMC. In addition, adaptive tuning laws for network parameters are derived according to the projection algorithm and the Lyapunov stability theorem for guaranteeing the network convergence as well as the totally power decoupling performance. Furthermore, experimental results are provided to verify the superiority of the proposed AFNNPD method in comparison with conventional methods.

Distributed secondary control of islanded micro-grid based on adaptive fuzzy-neural-network-inherited total-sliding-mode control technique

In this study, an adaptive fuzzy-neural-network (FNN) control scheme is proposed for an islanded micro-grid (MG) as a distributed secondary controller (DSC) to achieve the aims of voltage and frequency restoration and the optimal power sharing. Firstly, the dynamic model of an islanded MG is built, which consists of an inverter-interfaced distributed generation (DG) model and a MG architecture model. The DG model can be represented by considering the dynamics of a primary controller with an optimal active power sharing scheme. The MG architecture model is composed of power flow dynamics and loads. Then, a consensus-algorithm-based error function is defined, and a model-dependent total sliding-mode control (TSMC) technique is presented for dealing with synchronization and tracking problems. Moreover, an adaptive FNN (AFNN) scheme is designed to mimic the TSMC law to inherit its fast dynamic response with robust properties. Meanwhile, the requirement of precise information of the MG dynamic model in the TSMC law can be relaxed by the AFNN scheme. Adaptive tuning algorithms for FNN network parameters of the AFNN-based DSC (AFNN-DSC) strategy are derived by using the projection algorithm and the Lyapunov stability theorem, which can guarantee the stability of the AFNN-DSC-controlled system. The effectiveness of the proposed control method is verified by numerical simulations for real scenarios.© 2017 Elsevier Inc. All rights reserved.

Robust Power Sharing and Voltage Stabilization Control Structure via Sliding-Mode Technique in Islanded Micro-Grid

With a focus on the problems of active power sharing and voltage deviation of parallel-connected inverters in an islanded micro-grid (MG), in this study, the power-voltage droop controller and the inner voltage regulator are redesigned based on a total sliding-mode control (TSMC) technique. As for the power-voltage droop control loop, a droop control relation error between the active power and the point-of-common-coupling (PCC) voltage amplitude is defined. Then, the TSMC scheme is adopted to reach the new droop control relation, where the active power sharing and voltage amplitude recovery can be achieved simultaneously. Owing to the faster dynamic response and strong robustness provided by the TSMC framework, high-precision active power sharing during transient state also can be ensured without the influence of line impedances. The power allocation error can be improved by more than 81.2% and 50% than the conventional and proportional-integral (PI)-based droop control methods, respectively, and the voltage deviation rate can be reduced to 0.16%. Moreover, a small-signal model of the TSMC-based droop-controlled system is established, and the influences of control parameters on the system stability and the dynamic response are also investigated. The effectiveness of the proposed control method is verified by numerical simulations and experimental results.

Design of Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Islanded Micro-Grid

In this study, an adaptive fuzzy-neural-network-imitating sliding-mode control (AFNNISMC) is developed for a parallel-inverter system in an islanded micro-grid (MG) via a master-slave current sharing strategy. For ensuring the system-level stability, an entire dynamic model is constructed by viewing the parallel-inverter system as a whole. First, a total sliding-mode control (TSMC) scheme, and the TSMC plus an adaptive observer to form an adaptive TSMC (ATSMC) framework are designed for the parallel-inverter system. Then, a four-layer fuzzy neural network (FNN) is investigated to imitate the TSMC law to improve the system robustness, overcome the drawback of the dependence on detailed system dynamics, and deal with the chattering phenomena caused by the TSMC. According to the Lyapunov stability theorem and the projection algorithm, network parameters in the FNN are regulated online by employing the approximation error between the FNN and the TSMC law to ensure the convergence of the network and the stability of the control system. Thereby, the performance of high power quality and high-precision current sharing between inverters can be guaranteed even if system uncertainties exist. Moreover, the proposed AFNNISMC system can achieve the seamless disconnection and re-connection of slave inverters from and into an energized parallel-inverter system, which improves the redundancy and operation flexibility. In addition, numerical simulations and experimental results are given to demonstrate the feasibility and effectiveness of the proposed AFNNISMC scheme. Furthermore, performance comparisons with the ATSMC strategy and a conventional proportional-integral control (PIC) framework are provided to verify the superiority of the proposed scheme.

Adaptive Power Decoupling Strategy for Single-Phase Grid-Connected Converter

This study analyzes the power coupling in the single-phase grid-connected system and designs a novel power decoupling method for restraining the ripple voltage on the dc bus. First, the phenomena of power coupling under a non-ideal power grid, and the circuits with an active power decoupling method are derived. By introducing a ratio factor between dc bus capacitors, the decoupling ability in the proposed method can be improved. In order to achieve accurate power decoupling, a neural filter is adopted for clearly identifying the dc bus voltage level and extracting the ripple voltage on the dc bus. In addition, a total sliding-mode controller is further designed in the control strategy to enhance the system robustness with respect to varied power coupling conditions, e.g., under ideal or distorted power grids. The effectiveness of the proposed method is verified by numerical simulations and experimental results in comparison with a traditional capacitor decoupling method and an active power decoupling method with multi-proportional-resonant controllers in previous researches.

DSPACE DS1104 Based Real Time Implementation of Sliding Mode Control of Induction Motor

<span>In this paper, the design of a speed control scheme based on a total sliding mode control for Indirect Field Oriented of a three phase induction motor (IM) is proposed. Firstly, the indirect field oriented control is derived. Then, sliding mode control design is investigated to achieve a speed tracking objective under different load torque disturbance. Finally a dSPACE DS1104 R&D board is used to implement the proposed scheme. The experimental results released on 0.25 kW slip-ring IM show a high dynamic performance, fast transient response without overshot as well as a good load disturbances rejection response.</span>

Adaptive total sliding mode control for the current of power factor correction circuit

Adaptive total sliding mode control for the current of power factor correction circuit

Implementation of an estimator-based adaptive sliding mode control strategy for a boost converter based battery/supercapacitor hybrid energy storage system in electric vehicles

For the energy management of the boost converter based battery/supercapacitor (SC) hybrid energy storage system (HESS) in electric vehicles, a robust current control should be achieved for the battery such that the battery safety can be guaranteed. In this paper, an estimator-based adaptive sliding-mode control (estimator-based ASMC) strategy is proposed for the current tracking control of the boost converter based battery/SC HESS. By considering the unmodeled dynamics of the SC and the unknown disturbances, the equivalent circuit and the state-space average model are established for the boost converter based battery/SC HESS. To achieve the current tracking control, a sliding surface is defined based on the estimated tracking current error. The average control factor is designed according to the sliding-mode control (SMC) strategy. Furthermore, the adaptation laws are designed based on the state observers and the Lyapunov function, which can be used to estimate the load variations and unknown external input voltage. Finally, the adaptive control factor is recalculated based on the estimator-based ASMC strategy. In practical application, a hysteresis control strategy and an average filter method are developed to guarantee the smooth SMC for the boost converter based battery/SC HESS. Simulations and experiments are established to verify the estimator-based ASMC strategy. Compared to the conventional total SMC (TSMC) and PI strategies, the estimator-based ASMC strategy has over 37% and 50% settling-time improvement of current adjustment to deal with the load variation, respectively. The estimator-based ASMC strategy can improve the operating stability of the boost converter based battery/SC HESS under different operating modes. The battery can provide a constant or optimal current to the load. Therefore, the boost converter based battery/SC HESS with the proposed estimator-based ASMC strategy can effectively ensure the system stability and extend the battery life, simultaneously.

Robust Direct Power Control Schemes of DFIGs Driven by Variable-speed Wind Turbines

Despite its several advantages, a classic direct power control (DPC) technique of doubly fed induction generators (DFIGs) driven by variable-speed wind turbines has some drawbacks such as high power ripples and variable switching frequency. In this paper, two robust controllers are designed to improve the classical DPC performance without complicating the overall scheme. First, an integral sliding mode controller (ISMC) is designed to regulate the stator active and reactive powers. Two integral switching functions are selected for controlling stator active and reactive powers. The idea of total sliding mode controller is selected to avoid reaching phase stability problem. Second, a diagonal recurrent neural network (DRNN) controller is designed and trained based on DPC. The DRNN has several advantages compared to the classical static neural networks such as recurrence and simple construction. Simple off-line back-propagation algorithm is proposed to train the proposed DRNN. The stability of the proposed ISMC and DRNN controller is proved using the Lyapunov stability theorem. The grid side converter is controlled based on the DPC principle to ensure both constant DC-link voltage and grid side reactive power. The feasibility of the proposed DPC schemes is validated by simulation studies on a 1.5-MW wind power generation system. The performance of the proposed schemes is compared with a conventional DPC scheme under different operating conditions.

Adaptive Sliding-Mode With Hysteresis Control Strategy for Simple Multimode Hybrid Energy Storage System in Electric Vehicles

In this paper, a simple multimode hybrid energy storage system (HESS) is proposed for electric vehicles (EVs). Compared to the improved semiactive HESS, only two switches are added in the main circuit topology of the multimode HESS, thereby achieving the operating modes can be actively switched. The mode switch strategy is designed according to the driving modes of the EV and the status of the power sources. To improve the overall system efficiency of the multimode HESS, the boost converter will operate at the peak efficiency to convey the energy from the battery to the supercapacitor (SC). An adaptive sliding-mode control (ASMC) with hysteresis control (HC) strategy is also developed by combining practical application of the multimode HESS. Simulations and experiments are presented to verify the effectiveness of the proposed multimode HESS and its ASMC strategy. Simulated results show that the multimode HESS can select suitable operating modes in corresponding conditions. Compared to the total sliding-mode control strategy, experimental results demonstrate that the ASMC with HC strategy can improve the operating stability of the multimode HESS under different operating modes. The multimode HESS can not only switch to suitable operating modes, but also avoid the excessive output power of the battery to meet different power demands of the load. In addition, the SC absorbs all the braking energy such that the battery safety can be effectively ensured.

Design of Adaptive Fuzzy-Neural-Network Control for a Single-Stage Boost Inverter

This study mainly focuses on the development of an adaptive fuzzy-neural-network control (AFNNC) system for a single-stage boost inverter. First, the dynamic model of a single-stage boost inverter is analyzed and is built for the later control manipulation. Then, a total sliding-mode control (TSMC) framework without the reaching phase in conventional SMC is developed for enhancing the system robustness during the transient response of the voltage tracking 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 system is further investigated to imitate the TSMC law for the boost inverter. In the AFNNC system, 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 system can be easily supplied to the duty cycle of the power switch in the boost inverter without strict constraints on control parameters selection in conventional control strategies. In addition, the effectiveness of the proposed AFNNC scheme is verified by realistic experiments, and its advantages are indicated in comparison with a traditional double-loop proportional-integral control scheme and the TSMC framework.

Sliding mode direct power control of RSC for DFIGs driven by variable speed wind turbines

In spite of its several advantages, a classic direct power control (DPC) of doubly fed induction generators (DFIGs) driven by variable speed wind turbines has some drawbacks. In this paper, a simple and robust total sliding mode controller (TSMC) is designed to improve the classical DPC performance without complicating the overall scheme. The TSMC is designed to regulate the DFIG stator active and reactive powers. Two integral switching functions are selected for describing the switching surfaces of the active and reactive powers. Reaching phase stability problem of the classical sliding mode controller is avoided in the proposed TSMC. Neither current control loops nor accurate values of machine parameters are required in the proposed scheme. In addition, axes transformation of the stator voltage and current are eliminated. The grid side converter is controlled based on DPC principle to regulate both DC-link voltage and total reactive power. The feasibility of the proposed DPC scheme is validated through simulation studies on a 1.5MW wind power generation system. The performance of the proposed and conventional DPC schemes is compared under different operating conditions.

Microgrid Stability Controller Based on Adaptive Robust Total SMC

This paper presents a microgrid stability controller (MSC) in order to provide existing distributed generation units (DGs) the additional functionality of working in islanding mode without changing their control strategies in grid-connected mode and to enhance the stability of the microgrid. Microgrid operating characteristics and mathematical models of the MSC indicate that the system is inherently nonlinear and time-variable. Therefore, this paper proposes an adaptive robust total sliding-mode control (ARTSMC) system for the MSC. It is proved that the ARTSMC system is insensitive to parametric uncertainties and external disturbances. The MSC provides fast dynamic response and robustness to the microgrid. When the system is operating in grid-connected mode, it is able to improve the controllability of the exchanged power between the microgrid and the utility grid, while smoothing the DGs’ output power. When the microgrid is operating in islanded mode, it provides voltage and frequency support, while guaranteeing seamless transition between the two operation modes. Simulation and experimental results show the effectiveness of the proposed approach.

Intelligent total sliding‐mode control with dead‐zone parameter modification for a DC motor driver

A functional-linked cerebellar model neural network (FCMNN) equipped with sine–cosine perturbed Gaussian basis functions to online approximate an unknown nonlinear term in the system dynamics of a DC motor driver is proposed in this study. The sine–cosine perturbation in the Gaussian basis functions possessing the ability of handling rule uncertainties is quite useful for real-time applications. Then, an intelligent total sliding-mode control (ITSMC) system that is composed of a computation controller and a robust compensator is proposed. The computation controller including an FCMNN approximator is the main controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the FCMNN approximator upon system stability. The online parameter adaptation laws are derived based on a Lyapunov function so that the L2 tracking performance can be guaranteed. To reduce the parameter overtraining problem, a dead-zone parameter modification scheme is adopted so that the parameter tuning process will be stopped when a tracking index is smaller than a pre-specified threshold. Finally, the proposed ITSMC system is implemented on a 32-bit microcontroller for possible low-cost and high-performance industrial applications. The experimental results show that the ITSMC system can achieve favourable tracking performance and is robust against parameter variations in the plant.

Total sliding-mode-based particle swarm optimization control for linear induction motor

In this study, a total sliding-mode-based particle swarm optimization control (TSPSOC) scheme is designed for the periodic motion control of an indirect field-oriented linear induction motor (LIM) drive. First, an indirect field-oriented mechanism for a LIM drive is introduced to preserve the decoupling control characteristic. Then, the concept of total sliding-mode control (TSC) is incorporated into particle swarm optimization (PSO) to form an on-line TSPSOC framework for preserving the robust control characteristics and reducing the chattering control phenomena of TSC. Moreover, an adaptive inertial weight is devised to accelerate the searching speed effectively. In this control scheme, a PSO control system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TSC without strict constraint and detailed system knowledge. With the proposed TSPSOC system, the mover position of the controlled LIM drive possesses the advantages of favorable robust characteristic, control effort without chattering, and simple control framework. Numerical simulations and experimental results are given to verify the effectiveness of the proposed control scheme for the tracking of periodic reference trajectories. In addition, the superiority of the proposed TSPSOC scheme is indicated in comparison with the TSC, Petri fuzzy-neural-network control (PFNNC) and traditional fuzzy-neural-network control (TFNNC) systems.

Speed Synchronization of web winding System with Sliding Mode Control

A continuous web winding system is a large-scale, complex interconnected dynamic system with numerous tension zones to transport the web while processing it. There are two control schemes for large-scale system control: the centralized scheme and the decentralized scheme. Centralized control is the traditional control method, which considers all the information about the system to be a single dynamic model and design a control system for this model. 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. DOI: http://dx.doi.org/10.11591/ijpeds.v3i2.2432

SIMULATION OF SPEED CONTROL OF INDUCTION MOTOR WHEN SUBJECTED TO NON-LINEAR VARIATIONS IN MOTOR PARAMETERS

Today induction motor requires a variable speed control to serve wide range of applications. The objective of this work is to control the position of a field oriented induction motor for a given reference input signal. This work addresses the design of a speed control scheme based on total sliding-mode control (TSMC) theory for a field-orientated induction motor (IM). The total sliding mode control comprises an equivalent control design and a robust control design. In this work 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. The salient feature of this control scheme is that the controlled system has a total sliding motion without a reaching phase. The work is been accomplished using Matlab/Simulink. In this work a comparison is been done for a fixed torque & change in torque (Change in motor parameter).

Design of High-Performance Stand-Alone and Grid-Connected Inverter for Distributed Generation Applications

In this study, a high-performance inverter, including the functions of stand-alone and grid-connected power supplies, is developed so that distributed generation units can operate individually or in a microgrid mode. In the stand-alone power-supply mode, the output ac voltage can supply to ac loads. In the grid-connected power-supply mode, the goal of power management can be achieved by controlling the amplitude and direction of the output current in the inverter. An adaptive total sliding-mode control (ATSMC) scheme is designed for the proposed high-performance inverter with a full-bridge framework. As a result, the proposed high-performance inverter with the ATSMC scheme has the output voltage with a low total harmonic distortion in the stand-alone power-supply mode and the output current with a high power factor in the grid-connected power-supply mode to provide an ac output with high-performance power quality. The effectiveness of the proposed high-performance inverter with the ATSMC is verified by experimental results of a 5-kW prototype, and the merit of the proposed ATSMC scheme is indicated in comparison with conventional proportional-integral and proportional-resonant control strategies.

Design of backstepping control for high‐performance inverter with stand‐alone and grid‐connected power‐supply modes

In this study, the backstepping control (BSC) design for a high-performance inverter with the functions of stand-alone and grid-connected power supply is developed so that distributed generation units can operate individually or in a micro-grid mode. In the stand-alone power-supply mode, the output ac voltage can supply to ac loads. In the grid-connected power-supply mode, the goal of power management can be achieved by controlling the amplitude and direction of the output current in the inverter. As a result, the proposed high-performance inverter with the BSC scheme has the output voltage with a low total harmonic distortion in the stand-alone power-supply mode and the output current with a high-power factor in the grid-connected power-supply mode to provide an ac output with high-performance power quality. The effectiveness of the proposed high-performance inverter with the BSC is verified by experimental results of a 3 kW prototype, and the merit of the proposed BSC scheme is indicated in comparison with previous proportional-integral control, proportional-resonant control and adaptive total sliding-mode control strategies.

High performance direct torque control schemes for an IPMSM drive

Classical direct torque control (DTC) has several advantages, however, the high torque and flux ripples and variable switching frequency are considered its main drawbacks. In this paper, two techniques are proposed to improve the classical DTC performance of the interior permanent magnet synchronous motor (IPMSM) drive. Firstly, a torque/flux sliding mode controller (SMC) is used to replace the hysteresis comparators and lookup table of the classical DTC. The proposed torque/flux SMC has two integral switching functions for torque and flux control. The idea of total SMC is selected to avoid the reaching phase problem; however, the chattering problem is considered its main drawback. Secondly, to overcome the chattering problem, a torque/flux diagonal recurrent neural network (DRNN) controller is designed to replace the SMC. DRNN has several advantages such as recurrence and simple construction. In addition, a full motor dynamic model and the band of parameters variation are not required. Dynamic off line back-propagation algorithm is used to train the torque/flux DRNN controller. In addition, a space vector modulation (SVM) is combined with the two proposed techniques to provide constant switching frequency and high voltage resolution.The feasibility and effectiveness of the proposed systems have been demonstrated through computer simulations. A comparison between the classical DTC, torque/flux SMC and torque/flux DRNN controller have been made in order to confirm the validity of the proposed schemes. The superiority of the torque/flux DRNN controller has been proved through comparative simulation results.