Sliding-mode-controlled slider-crank mechanism with fuzzy neuralnetwork

The dynamic response of a sliding-mode-controlled slider-crank mechanism, which is driven by a permanent-magnet (PM) synchronous servo motor, is studied in this paper. First, a position controller is developed based on the principles of sliding-mode control. Moreover, to relax the requirement of the bound of uncertainties in the design of a sliding-mode controller, a fuzzy neural network (FNN) sliding-mode controller is investigated, in which a FNN is adopted to adjust the control gain in a switching control law on line to satisfy the sliding mode condition. In addition, 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 FNN. Numerical and experimental results show that the dynamic behaviors of the proposed controller-motor-mechanism system are robust with regard to parametric variations and external disturbances. Furthermore, compared with the sliding-mode controller, smaller control effort results and the chattering phenomenon is much reduced by the proposed FNN sliding-mode controller     

Intelligent voltage control strategy for three-phase UPS inverters with output LC filter

This paper presents a supervisory fuzzy neural network control (SFNNC) method for a three-phase inverter of uninterruptible power supplies (UPSs). The proposed voltage controller is comprised of a fuzzy neural network control (FNNC) term and a supervisory control term. The FNNC term is deliberately employed to estimate the uncertain terms, and the supervisory control term is designed based on the sliding mode technique to stabilise the system dynamic errors. To improve the learning capability, the FNNC term incorporates an online parameter training methodology, using the gradient descent method and Lyapunov stability theory. Besides, a linear load current observer that estimates the load currents is used to exclude the load current sensors. The proposed SFNN controller and the observer are robust to the filter inductance variations, and their stability analyses are described in detail. The experimental results obtained on a prototype UPS test bed with a TMS320F28335 DSP are presented to validate the feasibility of the proposed scheme. Verification results demonstrate that the proposed control strategy can achieve smaller steady-state error and lower total harmonic distortion when subjected to nonlinear or unbalanced loads compared to the conventional sliding mode control method.     

Robust fuzzy neural network sliding mode control scheme for IPMSM drives

This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.     

Adaptive fuzzy neural network control for motor-toggle servomechanism

In this study, the dynamic responses of an adaptive fuzzy neural network (FNN) controlled toggle mechanism is described. The toggle mechanism is driven by a permanent magnet (PM) synchronous servo motor. First, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the motor-toggle servomechanism. Since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the motor-toggle servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Moreover, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed adaptive and adaptive FNN controllers are robust with regard to uncertainties.     

A hybrid computed torque controller using fuzzy neural network formotor-quick-return servo mechanism

The dynamic response of a hybrid computed torque controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this paper. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque control, a position controller is designed to control the position of a slider of the motor-quick-return servo mechanism. In addition, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a fuzzy neural network (FNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Moreover, a hybrid control system, which combines the computed torque controller, the FNN uncertainty observer, and a compensated controller, is developed based on Lyapunov stability to control the motor-quick-return servo mechanism. The computed torque controller with FNN uncertainty observer is the main tracking controller, and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rule numbers of the FNN. Finally, simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed hybrid computed torque control system are robust with regard to parametric variations and external disturbances     

Comparison of sliding-mode and fuzzy neural network control formotor-toggle servomechanism

A comparative study of sliding-mode control and fuzzy neural network (FNN) control on the motor-toggle servomechanism is presented. The toggle mechanism is driven by a permanent-magnet synchronous servomotor. The rod and crank of the toggle mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the equation of motion. Then, based on the principles of the sliding-mode control, a robust controller is developed to control the position of a slider of the motor-toggle servomechanism. Furthermore, an FNN controller with adaptive learning rates is implemented to control the motor-toggle servomechanism for the comparison of control characteristics. Simulation and experimental results show that both the sliding-mode and FNN controllers provide high-performance dynamic characteristics and are robust with regard to parametric variations and external disturbances. Moreover, the FNN controller can result in small control effort without chattering     

Lateral stabilization of a four wheel independent drive electric vehicle on slippery roads

In this paper, a new controller is proposed for lateral stabilization of four wheel independent drive electric vehicles without mechanical differential. The proposed controller has three levels including high, medium and low control levels. Desired vehicle dynamics such as reference longitudinal speed and reference yaw rate are determined by higher level of controller. Moreover, using a neural network observer and a fuzzy logic controller, a novel reference longitudinal speed generator system is presented. This system guarantees the vehicle’s stable motion on the slippery roads. In this paper, a new sliding mode controller is proposed and its stability is proved by Lyapunov stability theorem. This sliding mode control structure is faster, more accurate, more robust, and with smaller chattering than classic sliding mode controller. Based on the proposed sliding mode controller, the medium control level is designed to determine the desired traction force and yaw moment. Therefore, suitable wheel forces are calculated. Finally, the effectiveness of the introduced controller is investigated through conducted simulations in CARSIM and MATLAB software environments.     

Fuzzy neural network control of a motor-quick-return servomechanism

A quick-return mechanism, which is driven by a field-oriented control permanent magnet (PM) synchronous servomotor, with fuzzy neural network (FNN) control is proposed in this study. The rod and crank of the quick-return mechanism are assumed to be rigid. First, the machine design of the motor-quick-return mechanism is developed. Next, the kinematic analysis of the quick-return mechanism is introduced. Then, an FNN controller with varied learning rates is implemented to control the motor-quick-return servomechanism without using the complex mathematical model of the motor–mechanism coupled system. Finally, experimental results are provided to demonstrate the effectiveness of the FNN controller.     

Continuous variable structure controller for BLDDSM positioncontrol with prescribed tracking performance

The continuous, accurate, and robust sliding mode tracking controller based on a disturbance observer for a brushless direct drive servo motor (BLDDSM) is presented. Although the conventional sliding mode control (SMC) or variable structure control (VSC) can give the desired tracking performance, there exists an inevitable chattering problem in control which is undesirable for a direct drive system. With the proposed algorithm, not only are the chattering problems removed, but also the prescribed tracking performance can be obtained by using the efficient compensation of the disturbance observer. The design of the sliding mode tracking controller for the prescribed, accurate, and robust tracking performance without the chattering problem is given based on the results of the detailed stability analysis. The usefulness of the proposed algorithm is demonstrated through the computer simulations for a BLDDSM under load variations     

Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.     

Indirect sliding mode control based on gray-box identification method for pneumatic artificial muscle

We present an indirect robust nonlinear controller for position-tracking control of a pneumatic artificial muscle (PAMs) testing system. The system modeling is reviewed, for which the existence of uncertain, unknown, and nonlinear terms in the internal dynamics is presented. From the obtained results, an online identification method is proposed for estimation of the internal functions with learning rules designed via a Lyapunov derivative function. A robust nonlinear controller is then designed based on the approximated functions to satisfy the control objective under the sliding mode technique. Appropriate selection of the smooth robust gain and the sliding surface ensures convergence of the tracking error to a desired level of performance. Stability of the closed-loop system is proven through another Lyapunov function. The proposed approach is verified and compared with a conventional proportional–integral–differential (PID) controller, adaptive recurrent neural network (ARNN) controller, and robust nonlinear controller in a real-time system with three different kinds of trajectories and loading. From the comparative experimental results, the effectiveness of the proposed method is confirmed with respect to transient response, steady-state behavior, and loading effect.     

Inversion-free force tracking control of piezoelectric actuators using fast finite-time integral terminal sliding-mode

The major hurdles to control the force created by piezoelectric actuators (PEAs) are originated from its strong nonlinear behaviors which include hysteresis, creep, and vibration dynamics. To achieve an accurate, fast and robust force tracking performance without using complicated modeling and parameter identification of PEAs, this paper presents a practical direct force control scheme. The proposed controller is based on two core approaches: 1) fast finite-time integral terminal sliding mode (FFI-TSM) which allows fast convergence and high accuracy to the closed-loop system without control chattering; and 2) an inverse-model-free compensation, named force-based time-delayed estimation (FBTDE) which offers significant robustness with minimum use of plant dynamics information. The finite-time stability of the overall closed-loop system is proven through the Lyapunov’s method. The proposed force tracking controller is implemented on the PEA system driving a variable physical damping actuator mechanism. The overall accuracy, convergence speed, and robustness of the proposed controller are validated under various experimental scenarios. Comparative experimental results are particularly presented to verify the effectiveness of the FFI-TSM term and the FBTDE term.     

A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional–integral–derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.     

Congestion control in cognitive radio networks with event-triggered sliding mode

The increasing demand for assorted services from extensive wireline and wireless users place a significant burden on the band-limited radio spectrum. To settle the demand, smart reuse and management of the spectrum are necessary. In this contribution, Cognitive Radio being an emerging technology provides a platform to share the same spectrum between Primary Users (licensed) and Secondary Users (unlicensed) for significant improvement in the spectrum efficiency. The coexistence of users for data communications in a band-limited channel calls for a robust congestion controller to maximize throughput. This work presents the design of a robust nonlinear congestion controller based on event-triggered sliding mode for Cognitive Radio Networks. The goal is to maintain desired Quality of Service of the network with optimum bandwidth and resource utilization. The controller has been designed on the notions of sliding mode, better known for its inherent robustness and disturbance rejection capabilities. An event-triggering scheme has been incorporated with the sliding mode for optimum utilization of the available resources. The signal is sampled and control is updated only when a predefined condition gets violated while ensuring acceptable closed-loop behavior of the system. The efficiency of the proposed controllers has been validated using simulations.     

Recurrent-fuzzy-neural-network sliding-mode controlled motor-toggleservomechanism

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     

Design of M-G modal space sliding mode control for lower limb exoskeleton robot driven by electrical actuators

Lower limb exoskeleton robot (LLER) can help patients with lower limb paralysis to carry out effective rehabilitation training. However, LLER is a kind of nonlinear system with the strong dynamic coupling between joints and the parameter perturbation following different poses of the robot. They will damage the control performance in the process of trajectory tracking. To solve these problems, a novel control strategy, Mass-Gravity modal space sliding mode control (M-GMSSMC), is proposed. The objective for this paper is to develop a novel decoupling control framework for an electrical actuators driven LLER to track a predefined gait trajectory. The controller design aims to improve trajectory tracking accuracy, reduce dynamic coupling between hip joint and knee joint and weaken the chattering phenomenon of the sliding mode controller. The decoupling condition and the robust stability condition are analyzed in this work. Experimental results validate the correctness of the presented conclusions and show the effectiveness of the proposed M-GMSSMC.     

直流微电网双向AC/DC变换器鲁棒前馈控制策略研究

针对双向AC/DC功率变换器在直流微电网母线电压稳定性方面的问题,文中提出了一种结合LESO和滑模理论的前馈鲁棒控制策略。通过建立直流微电网三相AC/DC双向功率变换器的动态数学模型,架构了三阶线性扩张状态观测器,并将三阶LESO的观测值用于滑模控制器的设计。该控制策略能够在不需要额外电流传感器的情况下实现前馈控制,并确保系统具有良好的动态性能。该策略还能够有效降低滑模控制的实现难度,提高系统的鲁棒性。仿真分析验证了文中所提控制策略的有效性。     

Sliding mode controller by use of neural networks

This paper proposes a method to design a robust controller by use of a neural network. The trained neural network functions as a sliding mode controller which is robust against uncertainties. From the analysis of the neural network, it is proved that the switching surface is not the same as the sliding surface like conventional sliding mode control theory. The neural network shows that the switching surface should be a nonlinear surface because of a hard limitation on control inputs, even if the designed sliding surface is linear. From the result of estimating the robustness of neural networks, we propose that generalization of neural networks which are used as controllers should be measured by the robustness. Numerical simulations show that the controller is robust against uncertainties and robustness can be improved by the proposed method.     

PID-type sliding mode-based adaptive motion control of a 2-DOF piezoelectric ultrasonic motor driven stage

This paper presents a new scheme of adaptive sliding mode control (ASMC) for a piezoelectric ultrasonic motor driven X–Y stage to meet the demand of precision motion tracking while addressing the problems of unknown nonlinear friction and model uncertainties. The system model with Coulomb friction and unilateral coupling effect is first investigated. Then the controller is designed with adaptive laws synthesized to obtain the unknown model parameters for handling parametric uncertainties and offsetting friction force. The robust control term acts as a high gain feedback control to make the output track the desired trajectory fast for guaranteed robust performance. Based on a PID-type sliding mode, the control scheme has a simple structure to be implemented and the control parameters can be easily tuned. Theoretical stability analysis of the proposed novel ASMC is accomplished using a Lyapunov framework. Furthermore, the proposed control scheme is applied to an X–Y stage and the results prove that the proposed control method is effective in achieving excellent tracking performance.     

Sliding mode control of quadruple tank process

The process investigated herein is the quadruple tank system that is stable only within a limited zone of operating parameters. The process model has been developed from fundamentals and tuned with experimental data. A controller design based on feedback linearization has been tested on this process model. Coupling feedback linearization with sliding mode algorithm provides robust control of the process and performs far superior to conventional PI control. A PC based controller interfaced to the experimental quadruple tank experimental set up has been used to implement this algorithm and test its performance. Inserting a ‘boundary layer’ around the sliding surface reduced the ‘chattering’ associated with sliding mode control. The implemented controller provides robust control and excellent set point tracking.