Adaptive Sliding-mode Control System Research Articles

An adaptive sliding mode control system and its application to real‐time hybrid simulation

An adaptive sliding mode control system and its application to real‐time hybrid simulation

Design of a Fuzzy Adaptive Sliding Mode Control System for MEMS Tunable Capacitors in Voltage Reference Applications

Micro electro mechanical system (MEMS) tunable capacitors (TC) are major elements in ac voltage reference sources (VRS). Physical parametric uncertainties, external electrostatic disturbance, and measurement noise malfunction their operation and ruin the preciseness of the VRS output voltage. Our objective problem is the design of a controller to cope with the mentioned parametric uncertainties, noise, and disturbance. Our applied method is the design and employment of a proportional integral fuzzy adaptive sliding mode controller (FASMC). Both terms of matched and unmatched uncertainties as well as external disturbance and measurement noise are all addressed in this article to generate a stable VRS output for the first time. Not only does this article contribute to the employment of a FASMC to enhance robustness in the drive of the capacitor, but also it benefits the reduction of the chattering effect due to fuzzy regulation in the switching term of the control law. Moreover, the automatic fuzzy adjustment in the controller, which is used for estimation of the coefficients in the sliding surface error dynamical equation, facilitates the specification of those coefficients, which can be time consuming in simulation affairs. Clarifying the importance of the proposed fuzzy adaptive sliding mode controller, this article reviews some previous controllers for MEMS TC in comparison with the proposed fuzzy controller, demonstrating its enhancement in comparison with previous schemes.

Intuitionistic fuzzy adaptive sliding mode control of nonlinear systems

In this paper, Takagi–Sugeno intuitionistic fuzzy adaptive sliding mode control system (TS-IFASMC) is designed for nonlinear systems. We propose an intuitionistic fuzzy method to determine the parameters of the adaptive sliding mode control method which is used to control nonlinear systems. Intuitionistic fuzzy systems are considered as a skilled tool to model uncertainty in systems so they can transfer expert knowledge to control schemes better than the other classical methods, and real-world problems can be handled more effectively with this control method. In the proposed system, control parameters are defined by the intuitionistic fuzzy membership, non-membership, hesitation degrees and an integral sliding mode surface for a robust control performance. The novelty of this study is the use of Takagi–Sugeno type intuitionistic fuzzy system in adaptive sliding mode control method and comparison of performance of this new system with other classical methods. Thus, adaptive sliding mode controller based on the Takagi–Sugeno intuitionistic fuzzy system is obtained to provide robust control performance. Finally, the results support the effectiveness of the presented control scheme.

Adaptive sliding mode control based on friction compensation for opto-electronic tracking system using neural network approximations

In this paper, an adaptive sliding mode control algorithm based on friction compensation utilizing neural network (NN) is designed for an opto-electronic tracking system under the circumstance of friction and external disturbance. Since neural networks can approximate any nonlinear function, the developed NN controller can approximate the nonlinear friction which is integrated into the adaptive sliding mode control system in the Lyapunov framework. The adaptive sliding mode controller with friction compensation can effectively reduce the effects of nonlinear friction and external disturbance of the opto-electronic tracking system utilizing neural network approximations. The stability of the proposed method is guaranteed according to Lyapunov criterion. Simulation and experimental validation results for a nonlinear LuGre dynamic model of the opto-electronic tracking system are provided to validate the effectiveness of the proposed control method.

Fractional Order Adaptive Sliding Mode Control System of Micro Gyroscope

In this paper, an adaptive sliding mode control method based on fractional order method is proposed for the estimation of unknown parameters in micro gyroscope system. Firstly, compared with the traditional integer order sliding surface, the fractional order sliding surface in this paper increases the order terms which can adjust the fractional order, and improve the control performance and precision. Additionally, the adaptive laws are derived to tune the control parameters online based on the Lyapunov stability theorem to deal with the uncertainty of the system. Moreover, simulation results show that the proposed control system's convergence speed, output signal tracking error and parameter fitting are better than the integral sliding mode control system, proving that the proposed control method is practicable and effective.

Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure.

In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.

Non-linear control of the reactor power in a PWR using online parameter adaptation

In this paper, an adaptive sliding mode control system is presented to control the Pressurised-Water Nuclear Reactor (PWR) core power. Sliding mode controller is a robust non-linear control but chattering in this method is undesirable; large parameter uncertainties accommodation envisaged by designing adaptive mechanisms for the controller and high chattering authority are inhibited. The reactor core is simulated based on the point kinetics equations and one delayed neutrons group. The stability analysis is given by means of the Lyapunov approach, thus the control system is guaranteed to be stable within a large range. The adaptive sliding mode control exhibits the desired dynamic properties during the entire output-tracking process independent of the perturbations.

Speed Sensorless Vector Control of Unbalanced Three-Phase Induction Motor with Adaptive Sliding Mode Control

This paper presents a technique for speed sensorless Rotor Flux Oriented Control (RFOC) of 3-phase Induction Motor (IM) under open-phase fault (unbalanced or faulty IM). The presented RFOC strategy is based on rotational transformation. An adaptive sliding mode control system with an adaptive switching gain is proposed instead of the speed PI controller. Using an adaptive sliding mode control causes the proposed speed sensorless RFOC drive system to become insensitive to uncertainties such as load disturbances and parameter variations. Moreover, with adaptation of the sliding switching gain, calculation of the system uncertainties upper bound is not needed. Finally, simulation results have been presented to confirm the good performance of the proposed method.

An adaptive sliding-mode control technique for three-phase UPS system with auto-tuning of switching gain

This paper presents an adaptive sliding-mode control (ASMC) technique for a three-phase UPS system with an auto-tuning mechanism of the switching gain. First, a sliding-mode control (SMC) scheme for the three-phase UPS inverter is designed to guarantee the robustness against the system uncertainties and external disturbances. Then, through addition of an adaptive control term, the ASMC algorithm is developed to optimize the switching gain without the prior information of the unknown and bounded uncertainties. It is shown that the upper bounds of the uncertainties are not required to be known in the SMC design. Also, the chattering problem in the reaching mode is considerably alleviated and the excessive use of electric power can be reduced by avoiding overestimation of the switching gain. Both the stability and robustness of the proposed ASMC method are proven by using the Lyapunov theory. In addition, a simple sliding-mode observer is used to estimate the load current without any additional current sensors. Therefore, the proposed ASMC system can accomplish the superior control performance (such as faster voltage recovery under a sudden load change, smaller steady-state error under parameter deviations, and lower THD under nonlinear load) compared to the conventional SMC scheme. Finally, the performance verifications of the proposed algorithm are carried out through the comparative simulation and experimental results on a prototype 1-kVA three-phase UPS inverter using TMS320F28335 DSP.

Adaptive Neural Sliding Mode Control of Active Power Filter

A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode control are combined together to achieve the control task; that is, the harmonic current of nonlinear load can be eliminated and the quality of power system can be well improved. Sliding surface coordinate function and sliding mode controller are used as input and output of the RBF neural network, respectively. The neural network control parameters are online adjusted through gradient method and Lyapunov theory. Simulation results demonstrate that the adaptive RBF sliding mode control can compensate harmonic current effectively and has strong robustness to disturbance signals.

Adaptive sliding mode control of tri-layer conjugated polymer actuators

This paper proposes an adaptive sliding mode control methodology to enhance the positioning ability of conducting polymer actuators typified by tri-layer conjugated polymer actuators. This is motivated by the search for an effective control strategy to command such actuators to a desired configuration in the presence of parametric uncertainties and unmodeled disturbances. After analyzing the stability of the adaptive sliding mode control system, experiments were conducted to demonstrate its satisfactory tracking ability, based on a series of experimental results. Implementation of the control law requires a valid model of the conducting polymer actuator and boundaries of the uncertainties and disturbances. Based on the theoretical and experimental results presented, the adaptive sliding mode control methodology is very attractive in the field of smart actuators which contain significant uncertainties and disturbances.

Adaptive sliding mode control of dynamic system using RBF neural network

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network (NN) for a class of time varying system in the presence of model uncertainties and external disturbance. Adaptive RBF neural network controller that can learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control system in the same Lyapunov framework. The proposed adaptive sliding mode controller can on line update the estimates of system dynamics. The asymptotical stability of the closed-loop system, the convergence of the neural network weight-updating process, and the boundedness of the neural network weight estimation errors can be strictly guaranteed. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.

COMPARATIVE STUDY OF SYSTEM IDENTIFICATION APPROACHES FOR ADAPTIVE TRACKING OF MEMS GYROSCOPE

In this paper, a comparative study of adaptive vibration control approaches is presented for the system identification for microelectro-mechanical systems (MEMS) z-axis gyroscope. Two adaptive methodologies such as adaptive controller and adaptive sliding mode controller are developed and compared in the aspect of algorithm derivation and Lyapunov stability analysis. The proposed adaptive control approaches can estimate the angular velocity and the damping and stiffness coefficients including the coupling terms. Numerical simulations are investigated to verify the effectiveness of the proposed control schemes, demonstrating that the robust performance of the adaptive sliding mode control system has been improved in the presence of external disturbance compared with adaptive control.

A robust vector control for induction motor drives with an adaptive sliding-mode control law

A novel adaptive sliding-mode control system is proposed in order to control the speed of an induction motor drive. This design employs the so-called vector (or field oriented) control theory for the induction motor drives. The sliding-mode control is insensitive to uncertainties and presents an adaptive switching gain to relax the requirement for the bound of these uncertainties. The switching gain is adapted using a simple algorithm which does not imply a high computational load. Stability analysis based on Lyapunov theory is also performed in order to guarantee the closed loop stability. Finally, simulation results show not only that the proposed controller provides high-performance dynamic characteristics, but also that this scheme is robust with respect to plant parameter variations and external load disturbances.

견실한 추종 제어를 위한 EHA 시스템의 슬라이딩 모드제어에 관한 연구

The response characteristics of EHA systems are sensitive to the temperature change of working fluid because the temperature of working fluid causes the variation of system parameters such as effective bulk modulus and viscous friction coefficient. In this paper, a precise position control of EHA system using the adaptive sliding mode control system is suggested. The adapted system parameters such as effective bulk modulus and viscous friction coefficient can be used for monitoring failures in the EHA system which has potential applications in the industrial fields. Not only the accuracy of adapted system parameters but also the improved performance and robustness in a given reference position of the cylinder are verified by computer simulation using AMESim software.

Adaptive stabilizing and tracking control for a nonlinear inverted-pendulum system via sliding-mode technique

Since the system behaviors of a dual-axis inverted-pendulum mechanism including actuator dynamics are highly nonlinear, it is difficult to design a suitable control system that realizes real-time stabilization and accurate tracking control at all times. In this paper, an adaptive sliding-mode control system is implemented to control a dual-axis inverted-pendulum mechanism that is driven by permanent magnet synchronous motors. First, the energy conservation principle is adopted to build a mathematical model of the motor-mechanism-coupled system. Moreover, an adaptive sliding-mode control system is developed for stabilizing and tracking control of the dual-axis inverted-pendulum system, where an adaptive algorithm is investigated to relax the requirement of the bound of lumped uncertainty in the traditional sliding-mode control. In addition, numerical simulation and experimental results show that the proposed control scheme provides high-performance dynamic characteristics and is robust with regard to parametric variations, various reference trajectories, and different initial states.

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.