Enhanced fuzzy sliding mode controller for active suspension systems

We proposed a fuzzy sliding mode controller (FSMC) to control an active suspension system and evaluated its control performance. The FSMC employed the error of the sprung mass position and the error change to establish a sliding surface, and then introduced the sliding surface and the change of the sliding surface as input variables of a traditional fuzzy controller (TFC) in controlling the suspension system. However, no substantial improvement in the ride comfort could be obtained with the FSMC relative to the TFC because the dynamic effect of the sprung mass acceleration from the bouncing tire during tire rotation was not eliminated. We have developed an enhanced fuzzy sliding mode controller (EFSMC) that maintained not only the original FSMC property but also introduced an assisted FSMC to address and compensate for this problem, and to enhance the road-holding capability of the vehicle. The assisted FSMC differs from the original FSMC only in using the sprung mass acceleration instead of the sprung mass position as a variable of the controller design. The EFSMC exhibits better control performance than either the TFC or the FSMC, in suppressing the acceleration of the vehicle body to improve the ride quality, and in reducing the tire deflection to increase the road-holding ability of a car, as confirmed by experimental results.     

Development and application of a novel radial basis function sliding mode controller

Generally, physical systems have certain non-linear and time-varying behaviours and various uncertainties. It is difficult to establish an appropriate model for controller design. Adaptive and sliding mode control schemes have been employed to solve some of these problems under certain model-based conditions and limitations. Here a novel adaptive radial basis functions sliding mode control is proposed by combining the advantages of the adaptive, neural network and sliding mode control strategies without precise system model information. It has on-line learning ability to deal with the system time-varying and non-linear uncertainties by adjusting the control parameters. The proposed scheme is implemented on a three degree-of-freedom dynamic absorber system. Only five radial basis functions are required for this control system and their weightings can be established and updated continuously by on-line learning. The experimental results show that this intelligent control approach effectively suppresses the vibration amplitude of the main mass due to external disturbances.     

自适应模糊滑模控制器设计

针对传统滑模控制的抖振问题,利用线性化反馈技术,将模糊自适应和滑模控制相结合,设计一种新型的模糊滑模控制器。通过模糊推理和基于Lyapunov函数的稳定性分析,获得模糊控制规则的自适应律,构成自适应模糊滑模控制器,有效解决了传统滑模控制中,需要确定参数摄动和外部干扰上确界不确定性问题,倒立摆上的运行结果表明该方法的有效性。     

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     

Precise friction control for the nonlinear friction system using the friction state observer and sliding mode control with recurrent fuzzy neural networks

This paper deals with a tracking control problem of a mechanical servo system with nonlinear dynamic friction which contains a directly immeasurable friction state variable and an uncertainty caused by incomplete parameter modeling and its variations. In order to provide an efficient solution to these control problems, we propose a composite control scheme, which consists of a friction state observer, a RFNN approximator and an approximation error compensator with sliding mode control. In first, a sliding mode controller and friction state observer are designed to estimate the unknown internal state of the LuGre friction model. Next, a RFNN is developed to approximate an unknown lumped friction uncertainty. Finally, an adaptive error compensator is designed to compensate an approximation error of RFNN. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are executed. Their results give a satisfactory performance of the proposed control scheme.     

Adaptive Sliding Mode Control of Robot Manipulator Based on Second Order Approximation Accuracy and Decomposed Fuzzy Compensator

A kind of adaptive sliding mode control scheme for tracking control of robot manipulator which has structured uncertainties and unstructured uncertainties is proposed in this paper. Multi-input Multi-output fuzzy logical system (FLS) is used as a compensator in the control law to compensate all the uncertainties. To reduce the number of the fuzzy rules and the burden of the computation, we design FLS based on second order approximation theorem which can approximate the uncertain function with less fuzzy rules at arbitrary precision than traditional FLS. Besides, to further reduce the number of the fuzzy rules and the amount of calculation, a new decomposed fuzzy logical system based on the decomposition of membership function is proposed. From the simulation results we can see that the control scheme and the fuzzy compensator proposed in this paper can perform fairly.     

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.     

航空发动机模糊滑模变结构控制研究

针对航空发动机是一个具有时变不确定性的非线性系统,结合模糊控制和滑模变结构控制的特点,提出了一种基于模糊滑模变结构控制的航空发动机控制方法.采用线性滑模面和指数趋近律设计变结构控制器,应用模糊逻辑系统自适应调节切换增益,避免了传统滑模变结构控制系统在到达阶段对不确定性的敏感性,消除了滑模控制中的抖振.通过数字仿真,结果表明所设计的模糊滑模变结构控制器对系统的参数摄动和外部干扰具有不变性,使被控系统在整个控制阶段都具有较强的鲁棒性.     

T-S fuzzy controllers for nonlinear interconnected systems with multiple time delays

This paper investigates the effectiveness of a passive tuned mass damper (TMD) and fuzzy controller in reducing the structural responses subject to the external force. In general, TMD is good for linear systems. We proposed here an approach of Takagi-Sugeno (T-S) fuzzy controller to deal with the nonlinear system. To overcome the effect of modeling error between nonlinear multiple time-delay systems and T-S fuzzy models, a robustness design of fuzzy control via model-based approach is proposed in this paper. A stability criterion in terms of Lyapunov's direct method is derived to guarantee the stability of nonlinear multiple time-delay interconnected systems. Based on the decentralized control scheme and this criterion, a set of model-based fuzzy controllers is then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear multiple time-delay interconnected system and the H/sup /spl infin// control performance is achieved at the same time. Finally, the proposed methodology is illustrated by an example of a nonlinear TMD system.     

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.     

Adaptive Fuzzy Sliding Mode Control Algorithm for a Non-Affine Nonlinear System

This paper concerns the design of robust controller for a nonlinear system that can be represented or approximated in a non-affine form. The control algorithm is based on sliding mode control that incorporates a fuzzy tuning technique, and it superposes equivalent control, switching control, and fuzzy control. An equivalent control law is firstly designed based on a nominal system model that was obtained by using curve fitting techniques under MATLAB. Switching control is then added to guarantee that the state reaches the sliding surface in the presence of parameter and disturbance uncertainties. Also, fuzzy tuning schemes, which can be supported by learning techniques derived from neural networks, are employed to improve control performance and to reduce chattering in the sliding mode. To verify the performance of this controller, an experimental platform of a pneumatically actuated top-guided single-seated control valve, which belongs to a classical complex nonlinear system, was constructed. Also, the experimental results show that high performance and attenuated chatter are achieved and thus verify the validity of the proposed control approach to dynamic systems characterized by severe uncertainties.     

Fuzzy sliding mode control for a class of piezoelectric system with a sliding mode state estimator

A new fuzzy sliding mode hysteresis compensating control strategy for a kind of typical piezoelectric system (PES) is proposed in this paper. Firstly, a typical nonlinear dynamic model of the PES is introduced. In order to compensate the hysteresis of the PES, an ideal linear hysteretic model is introduced by analyzing the characteristic of dynamic hysteretic model. Then, the ideal hysteretic model is transformed into an expected linear model by multiplying a slope conversion factor which can be obtained by experiment. Further, the sliding mode control principle is constructed to calculate the hysteretic compensating control law, which can guarantee the practical hysteretic characteristic to reach the expected linear output feature. Consider the unmeasured hysteresis output of the PES, we further design a sliding mode estimator to estimate the hysteretic part’s output. Finally, we derive the adaptive law of the fuzzy sliding mode controller, and demonstrate its stability through Lyapunov stability theory. The simulation results show the validity of the sliding mode compensator for this kind of nonlinear dynamic model of PES.     

导弹电液伺服机构的自适应模糊滑模跟踪控制

针对导弹电液伺服机构的跟踪控制问题,提出了一种自适应模糊滑模的设计方案.使用具有参数在线调节的自适应模糊控制,逼近滑模控制中的等效控制部分,并确定非线性控制项以保证系统的稳定性.根据滑模控制原理给出四条模糊规则,以平滑不连续控制,达到削弱抖振的目的.仿真结果表明了该方案的有效性.     

基于滑模控制的DC-DC降压变换器实验平台

为了实现DC-DC降压变换器的高精度控制,设计了一种基于滑模控制的输出电压调节器。首先根据DC-DC降压变换器的工作原理建立了系统的动态模型;接着利用转换后的受扰动态模型设计了滑模控制器,同时基于李雅普诺夫函数证明了闭环系统的稳定性;最后使用Matlab/Simulink软件和DC-DC降压变换器硬件电路搭建了实验测试平台。测试结果表明与传统的PID控制方法相比,DC-DC降压变换器系统在所设计的滑模控制器的作用下可以获得更快的动态性能与更强的扰动抑制能力。该实验平台不仅有利于大学生理解和掌握滑模控制理论,还可以提高大学生的工程应用能力。     

H∞ control of electrorheological suspension system subjected to parameter uncertainties

Vehicle suspension is normally used to attenuate unwanted vibration from various road conditions. The successful suppression of the vibration leads to the improvement of ride comfort as well as steering stability of the vehicle. One of attractive candidates to formulate successful vehicle suspension is to use electrorheological (ER) damper. This paper presents robust control performances of ER suspension system subjected to parameter uncertainties associated with sprung mass of the vehicle and time constant of the ER damper. After identifying dynamic bandwidth of a cylindrical ER damper operated with two different ER fluids (one has fast response characteristic, while the other slow response characteristic), a quarter car model is established by incorporating with time constant of the damping force. A robust H_∞ controller, which compensates the sprung mass and time constant uncertainties, is designed in order to suppress unwanted vibration of the vehicle. Control responses such as vertical acceleration of the sprung mass are presented in time and frequency domains. In addition, the effect of time constant of the damping force on the vibration control performance is investigated by undertaking a comparative work between fast and slow dynamic characteristics of the ER damper.     

Intelligent optimal control of single-link flexible robot arm

This paper addresses the design and properties of an intelligent optimal control for a nonlinear flexible robot arm that is driven by a permanent-magnet synchronous servo motor. First, the dynamic model of a flexible robot arm system with a tip mass is introduced. When the tip mass of the flexible robot arm is a rigid body, not only bending vibration but also torsional vibration are occurred. In this paper, the vibration states of the nonlinear system are assumed to he unmeasurable, i.e., only the actuator position can be acquired to feed into a suitable control system for stabilizing the vibration states indirectly. Then, an intelligent optimal control system is proposed to control the motor-mechanism coupling system for periodic motion. In the intelligent optimal control system a fuzzy neural network controller is used to learn a nonlinear function in the optimal control law, and a robust controller is designed to compensate the approximation error. Moreover, a simple adaptive algorithm is proposed to adjust the uncertain bound in the robust controller avoiding the chattering phenomena. The control laws of the intelligent optimal control system are derived in the sense of optimal control technique and Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. In addition, numerical simulation and experimental results are given to verify the effectiveness of the proposed control scheme.     

A supervisory fuzzy neural network controller for slider-crank mechanism

A supervisory fuzzy neural network (FNN) controller is proposed to control a nonlinear slider-crank mechanism in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive coupled with a slider-crank mechanism and a supervisory FNN position controller. The supervisory FNN controller comprises a sliding mode FNN controller and a supervisory controller. The sliding mode FNN controller combines the advantages of the sliding mode control with robust characteristics and the FNN with on-line learning ability. The supervisory controller is designed to stabilize the system states around a defined bound region. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results are provided to show that the proposed control system is robust with regard to plant parameter variations and external load disturbance.     

压电悬臂梁振动模糊滑模控制

针对压电悬臂梁的振动问题,该文提出了一种模糊滑模主动控制策略,以在抑制悬臂梁振动的基础上减小抖振。根据均质梁单元和压电梁单元运动方程引入状态向量,建立了压电悬臂梁的状态空间方程。通过平衡截断法对压电悬臂梁模型进行降阶,以提高计算效率,并以降阶模型为对象设计了模糊滑模控制器。运用模糊规则调节切换增益,饱和函数替换符号函数,有效地减小了滑模控制的抖振现象,利用Lyapunov函数证明其稳定性。结果表明,基于饱和函数的模糊滑模控制不仅能控制压电悬臂梁的振动,还能降低抖振现象。     

An adaptive hierarchical control approach of vehicle handling stability improvement based on Steer-by-Wire Systems

This paper proposes a novel adaptive hierarchical control approach for Steer-by-Wire (SbW) vehicles to improve the handling stability. The high-level stability control scheme contains a variable steering ratio (VSR) strategy based on the adaptive-network-based fuzzy inference system (ANFIS) and an active front steering (AFS) controller designed with the integral sliding mode method by tracking the expected yaw rate, in which the desired front wheel angle is generated to enhance the cornering stability performance. Besides, an adaptive tracking controller (ATC) for the SbW system is designed by using the adaptive sliding mode control method to achieve desired steering performance in the lower level. The proposed adaptive control strategy is validated with different driving circles from ISO standards in simulation tests and hardware-in-the-loop (HiL) experiments. The results demonstrate that the designed control approach improve the vehicle handling stability significantly, even in some extreme driving conditions.     

Switching-type fuzzy sliding mode control of a cart–pole system

The theme for a switching-type fuzzy sliding mode controller for the cart–pole system is developed in this paper. The control strategy is to make a vertical pole straight up and regulate the position of cart simultaneously. This system is nonlinear, unstable and nonminimum phase in consideration of these two control purposes; the conventional control algorithms are difficult to solve such a problem because only one control input can be utilized. By taking the experience of balancing a pole by a finger, we divide the dynamic response of the cart–pole system into approaching and departure conditions. Then the cart can be directed to the desired location with a balanced pole by switching two types of fuzzy sliding mode controllers. The feasibility and robustness of the proposed algorithm are demonstrated by computer simulations and actual experimental results.