Equivalent Sliding Mode Control Research Articles

Current Sensor Fault Reconstruction for PMSM Drives.

This paper deals with a current sensor fault reconstruction algorithm for the torque closed-loop drive system of an interior PMSM. First, sensor faults are equated to actuator ones by a new introduced state variable. Then, in αβ coordinates, based on the motor model with active flux linkage, a current observer is constructed with a specific sliding mode equivalent control methodology to eliminate the effects of unknown disturbances, and the phase current sensor faults are reconstructed by means of an adaptive method. Finally, an αβ axis current fault processing module is designed based on the reconstructed value. The feasibility and effectiveness of the proposed method are verified by simulation and experimental tests on the RT-LAB platform.

Synchronous Control of Hysteretic Creep Chaotic Neural Network

A chaotic neuron with hysteretic and creep characteristics is proposed based on the conventional chaotic neuron model, with which a neural network is constructed, and the synchronous control between the hysteretic creep chaotic neuron or neural network and the deterministic chaotic neuron or neural network is investigated by the sliding mode control. The hysteretic activation function of the neuron is constructed by shifting the sigmoid function. The hysteretic parameters have creep properties, which lead to the uncertain responses of the neurons. The equivalent sliding mode control law can be designed according to the error equation of the synchronous system, and the Lyapunov method is used to prove the stability of the system. Furthermore, fuzzy sliding mode control law is designed to restrain the chattering, to reduce the synchronous error, as well as to shorten the transition time. The validity of this method has been proved by simulation experiments.

Modeling and Analysis of a Fast and Robust Module-Integrated Analog Photovoltaic MPP Tracker

Analog circuitry-based photovoltaic (PV) maximum power point (MPP) tracking (MPPT) technique is attractive due to its low cost and capability of easy integration with normal dc–dc switching converters. However, realization of classical digital MPPT algorithms using analog circuitries is a challenging task. It necessarily requires to store the information of module voltage/current and power in order to find the desired MPP. While at the same time, improper design of digital MPPT controllers may cause poor tracking performances or limit cycle oscillations to manifest, which are generally seen as being undesirable. This paper proposes a fast and robust analog PV MPP tracker without imposing any external control or perturbation. The fast dynamic performances with absolute robustness are ensured here by integrating the concepts of Utkin's equivalent sliding mode control law and fast-scale stability analysis of actual switched converter systems. Moreover, the superiority of the proposed MPP tracker (in terms of high-tracking performances) over classical ones, and its impact in series-connected converters configuration are analytically demonstrated through the procedure developed in this paper. Finally, the analytical results have been validated by means of simulations and experiments.

Study on Sliding Mode Control with RBF Network for DSTATCOM

For the problems of parameters disturbance, nonlinear and uncertainty of distribution static compensator (DSTATCOM), this paper studies on sliding mode control based on radial basis function (RBF) network. The fast tracking of DSTATCOM reactive current is achieved by using of RBF neural networks and equivalent sliding mode control. The method has a strong adaptability and robustness for load disturbances and system parameters change, and integrates the advantages of neural network and sliding mode control, so it is an ideal intelligent control strategy .The MATLAB simulation results show that the controller has good dynamic and static quality, and provides an effective way for improving the performance of DSTATCOM.

Friction and uncertainty compensation of robot manipulator using optimal recurrent cerebellar model articulation controller and elasto-plastic friction observer

A model-free control scheme with the elasto-plastic friction observer is presented for robust and high-precision positioning of a robot manipulator. The traditional model-based adaptive controller requires information on the robotic dynamics in advance and thus undergoes robustness problem because of complex dynamics and non-linear frictions of a robot system. This problem is overcome by an employed model-free recurrent cerebellar model articulation controller (RCMAC) system and friction estimator for friction and uncertainty compensation of a robot manipulator. The adaptive laws of the RCMAC networks to approximate an ideal equivalent sliding mode control law and adaptive friction estimation laws based on the elasto-plastic friction model are derived based on the Lyapunov stability analysis. To guarantee stability and increase convergence speed of the RCMAC network, the optimal learning rates are obtained by the fully informed particle swarm (FIPS) algorithm. The robust positioning performance of the proposed control scheme is verified by simulation and experiment for the Scorbot robot in the presence of the joint dynamic friction and uncertainty.

Sliding Mode Control with Fixed Switching Frequency for Four-wire Shunt Active Filter

The present paper proposes a sliding mode control with fixed switching frequency for three-phase three-leg voltage source inverter based four-wire shunt active power filter. The aim is to improve phase current waveform, neutral current mitigation, and reactive power compensation in electric power distribution system. The performed sliding mode for active filter current control is formulated using elementary differential geometry. The discrete control vector is deduced from the sliding surface accessibility using the Lyapunov stability. The problem of the switching frequency is addressed by considering hysteresis comparators for the switched signals generation. Through this method, a variable hysteresis band has been established as a function of the sliding mode equivalent control and a predefined switching frequency in order to keep this band constant. The proposed control has been verified with computer simulation which showed satisfactory results.

Adaptive proportional–integral–derivative tuning sliding mode control for a shape memoryalloy actuator

In this paper, a novel adaptive sliding mode control with a proportional–integral–derivative(PID) tuning method is proposed to control a shape memory alloy (SMA) actuator. Thegoal of the controller is to achieve system robustness against the SMA hysteresisphenomenon, system uncertainties and external disturbances. In the controller, the PIDcontroller is employed to approximate the sliding mode equivalent control along thedirection that makes the sliding mode asymptotically stable. Due to the systemnonlinearity, the PID control gain parameters are systematically computed online accordingto the adaptive law. To improve the transient performance, the initial PID gain parametersare optimized by the particle swarm optimization (PSO) method. Simulationand experimental results demonstrate that the controller performs well for thedesired trajectory tracking, and the hysteresis phenomenon is compensated forcompletely. The control results are also compared with the optimized PID controller.

Voltage-lift split-inductor-type boost converters

A series of voltage-lift (VL) split-inductor-type boost converters are proposed in this study. They can get much higher voltage transfer gains owing to the implementation of the VL technique. They have symmetrical structures that make them easily understood. Meanwhile, they retain the advantages of the elementary split-inductor-type boost converter, which consumes less energy and has lower-voltage stresses on active switches and diodes at the same circuit complexity level. Although there are four active switches in the multiple-lift circuits, they operate synchronously making the control algorithm much easier. An equivalent sliding mode controller is designed to evaluate the characteristics of the series of converters based on reduced-order state space modelling. Theoretical analysis is supported by simulation and experimental results.

A RBF neural network sliding mode controller for SMA actuator

A radial basis function neural network sliding-mode controller (RBFSMC) is proposed to control a shape memory alloy (SMA) actuator. This approach, which combines a RBF neural network with sliding-mode control (SMC), is presented for the tracking control of a class of nonlinear systems having parameter uncertainties. The centers and output weights of the RBF neural network are updated through on-line learning, which causes the output of the neural network control to approximate the sliding-mode equivalent control along the direction that makes the sliding-mode asymptotically stable. Using Lyapunov theory, the asymptotic stability of the overall system is proven. Then, the controller is applied to compensate for the hysteresis phenomenon seen in SMA. The results show that the controller was applied successfully. The control results are also compared to those of a conventional SMC.

Real-time transient stabilization and voltage regulation of power generators with unknown mechanical power input

A nonlinear adaptive excitation controller is proposed to enhance the transient stability and voltage regulation of synchronous generators with unknown power angle and mechanical power input. The proposed method is based on a standard third-order model of a synchronous generator which requires only information about the physical available measurements of relative angular speed, active electric power, infinite bus and generator terminal voltages. The operating conditions are computed online using the above physical available measurements, the terminal voltage reference value and the estimate of the mechanical power input. The proposed design is therefore capable of providing satisfactory voltage in the presence of unknown variations of the power system operating conditions. Using the concept of sliding mode equivalent control techniques, a robust decentralized adaptive controller which insures the exponential convergence of the outputs to the desired ones, is obtained. Real-time experimental results are reported, comparing the performance of the proposed adaptive nonlinear control scheme to one of the conventional AVR/PSS controller. The high simplicity of the overall adaptive control scheme and its robustness with respect to line impedance variation including critical unbalanced operating condition and temporary turbine fault, constitute the main positive features of the proposed approach.

High-Performance Tracking Controller for Discrete Plant Using Nonlinear Sliding Surface

A nonlinear sliding surface is proposed to improve the transient response for general discrete multiple-input-multiple-output linear systems with matched perturbations. The tracking case is analyzed, and it is shown that the scheme is able to achieve low overshoot and low settling time simultaneously; this is not possible with a linear sliding surface. The control law is based on the discrete-time sliding mode equivalent control and thus eliminates chattering. The control law is proposed based on two approaches: (1) reaching-law-based approach which needs only disturbance bounds and (2) disturbance-observer-based approach. Multirate output feedback is used to relax the need of the entire state vector for the implementation of the control law. A magnetic tape position control application is used to show the effectiveness of the proposed scheme.

A Fuzzy Sliding Mode Controller and Its Application

An approach of self-learning fuzzy sliding-mode control which combines fuzzy control with the sliding-mode control, is presented for the tracking control of a class of nonlinear systems with the parameter uncertainties. The fuzzy control rules are updated through on-line learning, which make the output of fuzzy control approximate to sliding-mode equivalent control along the direction of making sliding-mode asymptotic stable. Based on Lyapunov theory, the asymptotic stability of the overall systems is proved. The proposed method is applied to some electrohydraulic servo systems, and the results of simulation show that the satisfied control precision and stability can be obtained by using proposed method for the systems.

Backlash phenomenon observation and identification in electromechanical system

In this paper a second order sliding mode observer with finite time convergence is developed for an electromechanical system with backlash. As a consequence of finite time convergence the sliding mode equivalent control is used to apply identification algorithms in order to characterize the backlash phenomenon. The dead zone amplitude and the disturbing torque are identified asymptotically. Simulation and experimental tests applied to an electromechanical actuator are given to support the theoretical development.

Higher Order Sliding Mode Observer-Based Control

The aim of this paper is to present a new high-order sliding mode controller for uncertain SISO minimum-phase nonlinear systems. The main properties of this controller are that the convergence time is finite and the uncertainties robustness is ensured. The controller design combines standard equivalent sliding mode control with linear quadratic (LQ) one over a finite time interval with a fixed final state (Lewis. 1986). The advantages of the algorithm are that no exact differentiation is necessary if the value of sliding mode order is equal to the relative degree ρ and the discontinuous gain is minimized via a preliminary nonlinear feedback. Furthermore, the upper bound of the convergence time is chosen a priori and the control law can be adjusted via two weighting matrices.