A new model-free adaptive sliding controller for active suspension system

Active suspension systems are designed to provide better ride comfort and handling capability in the automotive industry. Since the active suspension system has nonlinear and time-varying characteristics, it is difficult to establish an accurate dynamic model for designing a model-based controller. Here, a functional approximation (FA) based adaptive sliding controller with fuzzy compensation is proposed for an active suspension system. The FA technique is employed to represent the unknown functions, which releases the model-based requirement of the sliding mode control. In addition, a fuzzy control scheme with online learning ability is employed to compensate for the modeling error of the FA with finite number of terms for reducing the implementation difficulty. To guarantee the control system stability, the update laws of the coefficients in the approximation function and the fuzzy tuning parameters are derived from the Lyapunov theorem. The proposed controller is employed on a quarter-car active suspension system. The simulation results and experimental results show that the proposed controller can suppress the oscillation amplitude of the sprung mass effectively. To evaluate the performance improvement of inducing a fuzzy compensator in this FA adaptive controller, the dynamic responses of the proposed hybrid controller are compared with those of FA-based adaptive sliding controller only.     

FUZZY SLIDING MODE CONTROL SYSTEMS WITH ADAPTIVE ESTIMATION

In this paper the design of adaptive sliding mode control for fuzzy systems is discussed. For a complex physical system represented by an amalgamated fuzzy global model that compromises a set of linear models, conditions for the adaptive sliding mode control to stabilize the global fuzzy model are given. The advantage of the control structure is that a priori knowledge of the upper bounds of bounded uncertainties and internal parameters is not required. Numerical simulations are presented to show the effectiveness of the controller.     

一类具有未知死区MIMO系统的自适应模糊控制

针对一类具有未知死区并具有下三角函数控制增益矩阵的不确定MIMO非线性系统, 根据滑模控制原理, 并利用Nussbaum函数的性质, 提出了一种自适应模糊控制器的设计方案. 该方案取消了函数控制增益符号已知和死区模型参数上界、下界已知的条件. 通过引入积分型李亚普诺夫函数及最优逼近误差与死区扰动上界的自适应补偿项，证明了闭环系统是稳定的，跟踪误差收敛到零. 仿真结果表明了该方法的有效性.     

Hybrid adaptive fuzzy identification and control of nonlinearsystems

We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems. We then propose a hybrid adaptive (HA) law for adjusting the parameters. The HA law utilizes two types of errors in the adaptive system, the tracking error and the modeling error. Performance analysis using a Lyapunov synthesis approach proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence. Furthermore, this is achieved at negligible increased implementation cost or computational complexity. We prove a theorem that shows the properties of this hybrid adaptive fuzzy control system, i.e., bounds for the integral of the squared errors, and the conditions under which these errors converge asymptotically to zero are obtained. Finally, we apply the hybrid adaptive fuzzy controller to control a chaotic system, and the inverted pendulum system     

PHANTOM Omni机器人的自适应模糊滑模控制

针对PHANTOM Omni机器人的位置轨迹跟踪问题,采用了一种基于模糊逻辑的自适应模糊滑模控制方案。利用滑模控制中的切换函数作为输入,根据模糊系统的逼近能力设计控制器,并基于李雅谱诺夫方法设计自适应律对控制器所需参数进行实时调节。仿真中将其与传统的滑模控制进行了比较,仿真结果表明：自适应模糊滑模控制能使PHANTOM Omni机器人更好地实现期望的位置轨迹跟踪并有效地减轻抖振现象,从而证明了该方法在PHANTOM Omni机器人上实施的可行性。     

船舶航向非线性系统的多滑模自适应模糊控制

针对参数未知的船舶航向非线性控制系统数学模型,在考虑舵机伺服机构特性的情况下,船舶航向控制问题就成为一个虚拟控制系数未知的非匹配不确定非线性控制问题.基于多滑模设计方法和模糊逻辑系统的逼近能力,提出了一种多滑模自适应模糊控制算法,通过引入非连续投影算法和积分型Lyapunov函数,提高了系统在抑制参数漂移、控制器奇异等方面的能力.借助Lyapunov函数证明了所设计控制器使最终的闭环非匹配不确定船舶运动非线性系统中的所有信号有界,且跟踪误差收敛到零.仿真研究表明:该算法与传统的PID控制相比,具有较好的跟踪能力和自适应能力.     

FPGA‐based adaptive dynamic sliding‐mode neural control for a brushless DC motor

In the adaptive neural control design, since the number of hidden neurons is finite for real‐time applications, the approximation errors introduced by the neural network cannot be inevitable. To ensure the stability of the adaptive neural control system, a switching compensator is designed to dispel the approximation error. However, it will lead to substantial chattering in the control effort. In this paper, an adaptive dynamic sliding‐mode neural control (ADSNC) system composed of a neural controller and a fuzzy compensator is proposed to tackle this problem. The neural controller, using a radial basis function neural network, is the main controller and the fuzzy compensator is designed to eliminate the approximation error introduced by the neural controller. Moreover, a proportional‐integral‐type adaptation learning algorithm is developed based on the Lyapunov function; thus not only the system stability can be guaranteed but also the convergence of the tracking error and controller parameters can speed up. Finally, the proposed ADSNC system is implemented based on a field programmable gate array chip for low‐cost and high‐performance industrial applications and is applied to control a brushless DC (BLDC) motor to show its effectiveness. The experimental results demonstrate the proposed ADSNC scheme can achieve favorable control performance without encountering chattering phenomena. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

An optimal adaptive robust PID controller subject to fuzzy rules and sliding modes for MIMO uncertain chaotic systems

In this paper, an optimal adaptive robust PID controller based on fuzzy rules and sliding modes is introduced to present a general scheme to control MIMO uncertain chaotic nonlinear systems. In this control scheme, the gains of the PID controller are updated by using an adaptive mechanism, fuzzy rules, the gradient search method, and the chain rule of differentiation in order to minimize the sliding surfaces of sliding mode control. More precisely, sliding mode control is used as a supervisory controller to provide sufficient control inputs and guarantee the stability of the control approach. To ascertain the parameters of the proposed controller and avoid trial and error, the multi-objective genetic algorithm is employed to augment the performance of proposed controller. The chaotic system of a Duffing-Holmes oscillator and an industrial robotic manipulator are the case studies to evaluate the performance of the proposed control approach. The obtained results of this study regarding both systems are compared with the outcomes of two notable studies in the literature. The results and analysis prove the efficiency of the proposed controller with regard to MIMO uncertain systems having challenging external disturbances in terms of stability, minimum tracking error and optimal control inputs.     

Adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties

A new design approach of an adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties is presented in this paper. A fuzzy terminal sliding mode controller is designed to retain the advantages of the terminal sliding mode controller and to reduce the chattering occurred with the terminal sliding mode controller. The sufficient condition is provided for the uncertain system to be invariant on the sliding surface. The parameters of the output fuzzy sets in the fuzzy mechanism are adapted on-line to improve the performance of the fuzzy sliding mode control system. The bounds of the uncertainties are not required to be known in advance for the presented adaptive fuzzy sliding mode controller. The stability of the fuzzy control system is also guaranteed. Moreover, the chattering around the sliding surface in the sliding mode control can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed adaptive fuzzy terminal sliding mode controller.     

一类非线性系统的积分变结构模糊自适应跟踪控制

针对一类具有未知常数控制增益的不确定非线性系统,基于变结构控制原理,并利用具有非线性可调参数的模糊系统逼近等价控制,提出一种具有监督控制器的积分变结构模糊自适应跟踪控制策略.该策略通过监督控制器保证闭环系统所有信号有界.进一步,通过引入最优逼近误差的自适应补偿项来消除建模误差的影响.理论分析证明了跟踪误差能够收敛到零.仿真结果表明了该方法的有效性.     

自适应模糊控制在感应电动机调速系统中的应用研究

针对基于直接转矩控制的感应电动机调速系统低速运转时由定子电阻变化引起的转速静差问题,提出一种自适应模糊控制方法,首先给出定子电阻变化量的模糊估计器和感应电动机直接转矩控制的模糊实现,在此基础上提出了自适应模糊控制方案。仿真实验表明,该方案能够解决调速系统在低速范围内的静差问题。     

Adaptive control of a class of nonlinear systems with nonlinearlyparameterized fuzzy approximators

Recently, through the use of parameterized fuzzy approximators, various adaptive fuzzy control schemes have been developed to deal with nonlinear systems whose dynamics are poorly understood. An important class of parameterized fuzzy approximators is constructed using radial basis function (RBF) as a membership function. However, some tuneable parameters in RBF appear nonlinearly and the determination of the adaptive law for such parameters is a nontrivial task. In this paper, we propose a new adaptive control method in an effort to tune all the RBF parameters thereby reducing the approximation error and improving control performance. Global boundedness of the overall adaptive system and tracking to within a desired precision are established with the new adaptive controller. Simulations performed on a simple nonlinear system illustrate the approach     

线控转向系统的自适应高阶滑模控制

车辆线控转向(steer-by-wire,SbW)系统存在摩擦力矩及回正力矩等不确定动态特性,难以实现精确建模与有效控制.为此,提出一种基于自适应模糊逻辑系统的自适应高阶滑模(adaptive higher-order sliding mode, AHOSM)方法,实现SbW系统的有效控制.首先,通过自适应模糊逻辑系统逼近SbW系统的未知动态,使控制器的设计不再需要摩擦力矩及回正力矩的动力学模型;其次,采用高阶滑模和自适应增益技术削弱传统滑模控制器存在的抖振现象;再次,通过构造Lyapunov函数设计增益自适应律补偿逼近误差和系统不确定项对控制精度的影响,该方案不需要系统不确定项的界已知,且能够避免增益过估计现象;最后,通过稳定性分析证明该控制器可以在有限时间内建立实际滑动模态,数字仿真和硬件在环实验进一步验证了该控制方法的有效性和优越性.     

T-S model based indirect adaptive fuzzy control using online parameter estimation

A parameter estimation scheme with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory for the general MIMO Takagi-Sugeno (T-S) fuzzy models. The parameters of the Takagi-Sugeno fuzzy models can be estimated by observing the behavior of the system and with the online parameter estimator, any type of fuzzy controllers works adaptively to the parameter perturbation. In order to show the applicability of the proposed estimator, an existing fuzzy state feedback controller is adopted and indirect adaptive fuzzy control design with the proposed estimator is shown. From the numerical simulations and experiments, it is shown that the derived adaptive law works for the estimation model to follows the parameterized plant model and the overall control system has robustness to the parameter perturbation.     

Stable adaptive fuzzy control of nonlinear systems

A direct adaptive fuzzy controller that does not require an accurate mathematical model of the system under control, is capable of incorporating fuzzy if-then control rules directly into the controllers, and guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded is developed. The specific formula for the bounds is provided, so that controller designers can determine the bounds based on their requirements. The direct adaptive fuzzy controller is used to regulate an unstable system to the origin and to control the Duffing chaotic system to track a trajectory. The simulation results show that the controller worked without using any fuzzy control rules, and that after fuzzy control rules were incorporated the adaptation speed became much faster. It is shown explicitly how the supervisory control forces the state to remain within the constraint set and how the adaptive fuzzy controller learns to regain control     

基于控制器输出误差方法的自适应模糊控制及其在机器人轨迹跟踪控制中的应用

本文提出一种自适应模糊控制器并将之用于机器人轨迹跟踪控制 ,该控制器采用控制器输出误差方法 (COEM) ,根据控制器的输出误差而不是对象的输出误差来在线地调整模糊控制器的参数 ,无须对对象进行辩识 .仿真结果表明该控制器用于机器人轨迹跟踪控制具有很好的性能 ,是一种有效的控制器     

Sliding mode control of wind-induced vibrations using fuzzy sliding surface and gain adaptation

Although fuzzy/adaptive sliding mode control can reduce the chattering problem in structural vibration control applications, they require the equivalent control and the upper bounds of the system uncertainties. In this paper, we used fuzzy logic to approximate the standard sliding surface and designed a dead-zone adaptive law for tuning the switching gain of the sliding mode control. The stability of the proposed controller is established using Lyapunov stability theory. A six-storey building prototype equipped with an active mass damper has been used to demonstrate the effectiveness of the proposed controller towards the wind-induced vibrations.     

一类MIMO非线性系统的直接自适应模糊滑模控制

针对一类具有下三角形函数控制增益矩阵的非线性系统, 基于滑模控制原理, 并利用Ⅱ型模糊系统的逼近能力, 提出了一种直接自适应模糊滑模控制器设计的新方案. 通过引入积分型李雅普诺夫函数及逼近误差自适应补偿项, 证明了闭环系统是全局稳定的, 跟踪误差收敛到零. 仿真结果表明了该方法的有效性.     

Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system

In this paper, a fuzzy-identification-based adaptive backstepping control (FABC) scheme is proposed. The FABC system is composed of a backstepping controller and a robust controller. The backstepping controller, which uses a self-organizing fuzzy system (SFS) with the structure and parameter learning phases to on-line estimate the controlled system dynamics, is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the SFS. The developed SFS automatically generates and prunes the fuzzy rules by the proposed structure adaptation algorithm and the parameters of the fuzzy rules and membership functions tunes on-line in the Lyapunov sense. Thus, the overall closed-loop FABC system can guarantee that the tracking error and parameter estimation error are uniformly ultimately bounded; and the tracking error converges to a desired small neighborhood around zero. Finally, the proposed FABC system is applied to a chaotic dynamic system to show its effectiveness. The simulation results verify that the proposed FABC system can achieve favorable tracking performance even with unknown controlled system dynamics.