Total Sliding-mode Control System Research Articles

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.

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.

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.

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.