Variable universe stable adaptive fuzzy control of nonlinear system

A kind of stable adaptive fuzzy control of nonlinear system is implemented using variable universe method. First of all, the basic structure of variable universe adaptive fuzzy controllers is briefly introduced. Then the contraction-expansion factor that is a key tool of variable universe method is defined by means of integral regulation idea, and a kind of adaptive fuzzy controllers is designed by using such a contraction-expansion factor. The simulation on first order nonlinear system is done. Secondly, it is proved that the variable universe adaptive fuzzy control is asymptotically stable by use of Lyapunov theory. The simulation on the second order nonlinear system shows that its simulation effect is also quite good. Finally a useful tool, called symbolic factor, is proposed, which may be of universal significance. It can greatly reduce the settling time and enhance the robustness of the system.     

Adaptive fuzzy controllers based on variable universe

Adaptive fuzzy controllers by means of variable universe are proposed based on interpolation forms of fuzzy control. First, monotonicity of control rules is defined, and it is proved that the monotonicity of interpolation functions of fuzzy control is equivalent to the monotonicity of control rules. This means that there is not any contradiction among the control rules under the condition for the control rules being monotonic. Then structure of the contraction-expansion factor is discussed. At last, three models of adaptive fuzzy control based on variable universe are given which are adaptive fuzzy control model with potential heredity, adaptive fuzzy control model with obvious heredity and adaptive fuzzy control model with successively obvious heredity.     

Using Adaptive Gain Scheduling LQR Method Control of Arm Driven Inverted Pendulum System Based on PIC18F4431

The arm driven inverted pendulum system is a highly nonlinear model, muhivariable and absolutely unstable dynamic system so it is very difficult to obtain exact mathematical model and balance the inverted pendulum with variable position of the ann. To solve this problem, this paper presents a mathematical model for arm driven inverted pendulum in mid-position configuration and an adaptive gain scheduling linear quadratic regulator control method for the stabilizing the inverted pendulum. The proposed controllers for arm driven inverted pendulum are simulated using MATLAB-SIMULINK and implemented on an experiment system using PIC 18F4431 mieroeontroller. The result of experiment system shows the control performance to be very good in a wide range stabilization of the arm position.     

A Variable Structure Model Reference Adaptive Control

A variable structure model reference adaptive control problem for the nonlinear system is studied in this paper. First, according to the relative degree concept of th nonlinear control system an error equation between the outputs of the reference model and the controlled plant is derived. Then, by using the variable structrue control method, an algorithm of variable structure model reference adaptive control is deduced on the basis of a new concept of reaching law. The definition of the SISO system is introduced into the MIMO nonlinear system. Finally, as an example, a pendulum nonlinear control system is simulated to demonstrated the effectiveness of the method. The results show that the method has some advantages: the design is simple, intuitive and easy to be realized in engineering. Besides, it is of practical significance for the synthesis of nonlinear control systems.     

Adaptive Fuzzy Control for CVT Vehicle

On the simple continuously variable transmission （CVT） driveline model, the design of adaptive fuzzy control system for CVT vehicle is presented. The adaptive fuzzy control system consists of a scaling factor self-tuning fuzzy-PI throttle controller, and a hybrid fuzzy-PID CVT ratio and brake controller. The presented adaptive fuzzy control strategy is vehicle model independent, which depends only on the instantaneous vehicle states, but does not depend on vehicle parameters. So it has good robustness against uncertain vehicle parameters and exogenous load disturbance. Simulation results show that the proposed adaptive fuzzy strategy has good adaptability and practicality value.     

Research on Directly Adaptive Fuzzy Algorithm Based ACC Controller

A directly adaptive fuzzy algorithm is applied in vehicle adaptive cruise control system. The basic principle of the adaptive fuzzy algorithm is analyzed. The initial value of the fuzzy based vector is given by the traditional fuzzy membership. Adaptive law of the adjustable parameters θ is also determined. The directly adaptive fuzzy ACC controller is designed based on Matlab fuzzy toolbox. Matlab-Simulink is adopted to test the function of the adaptive fuzzy ACC controller. The control system is established using a 7 DOF vehicle dynamics model. Simulation results indicate that the principle of the method is correct and it performs well both in cruise and distance keeping.     

Model Free Adaptive Predictive Control of Desulfurization Slurry pH Based on CPS Framework

In order to improve the slurry pH control accuracy of the absorption tower in the wet flue gas desulfurization process, a model free adaptive predictive control algorithm for the desulfurization slurry pH which is based on a cyber physical systems framework is proposed. First, aiming to address system characteristics of non-linearity and pure hysteresis in slurry pH change process, a model free adaptive predictive control algorithm based on compact form dynamic linearization is proposed by combining model free adaptive control algorithm with model predictive control algorithm. Then, by integrating information resources with the physical resources in the absorption tower slurry pH control process, an absorption tower slurry pH optimization control system based on cyber physical systems is constructed. It is turned out that the model free adaptive predictive control algorithm under the framework of the cyber physical systems can effectively realize the high-precision tracking control of the slurry pH of the absorption tower, and it has strong robustness.     

Adaptive variable structure control based on backstepping for spacecraft with reaction wheels during attitude maneuver

An adaptive variable structure control method based on backstepping is proposed for the attitude maneuver problem of rigid spacecraft with reaction wheel dynamics in the presence of uncertain inertia matrix and external disturbances. The proposed control approach is a combination of the backstepping and the adaptive variable structure control. The cascaded structure of the attitude maneuver control system with reaction wheel dynamics gives the advantage for applying the backstepping method to construct Lyapunov functions. The robust stability to external disturbances and parametric uncertainty is guaranteed by the adaptive variable structure control. To validate the proposed control algorithm, numerical simulations using the proposed approach are performed for the attitude maneuver mission of rigid spacecraft with a configuration consisting of four reaction wheels for actuator and three magnetorquers for momentum unloading. Simulation results verify the effectiveness of the proposed control algorithm.     

The Application and Simulation of Fuzzy Adaptive PID in Household Heating Metering System

After heat is metered in each house unit,the heating system is regulated by variable flow.The temperature of the return w ater is controlled to regulate the flow to realize the temperature regulation.According to the characteristics of the temperature control w ith big inertia,pure time-delay and degeneration,a fuzzy adaptive PID controller is designed w ith the advantages of the fuzzy control and PID algorithm,and the simulation model is established according to the characteristics of heating metering system.Simulation results show that the fuzzy adaptive PID controller proposed has small overshoot,short oscillation cycle,high precision and strong anti-jamming capability in comparison w ith conventional PID controller,w hich could meet the requirement of the dynamic and steady-state performance of the heating process.     

A special hierarchical fuzzy neural-networks based reinforcement learning for multi-variables system

Proposes a reinforcement learning scheme based on a special Hierarchical Fuzzy Neural-Networks （HFNN） for solving complicated learning tasks in a continuous multi-variables environment. The output of the previous layer in the HFNN is no longer used as if-part of the next layer, but used only in then-part. Thus it can deal with the difficulty when the output of the previous layer is meaningless or its meaning is uncertain. The proposed HFNN has a minimal number of fuzzy rules and can successfully solve the problem of rules combination explosion and decrease the quantity of computation and memory requirement. In the learning process, two HFNN with the same structure perform fuzzy action composition and evaluation function approximation simultaneously where the parameters of neural-networks are tuned and updated on line by using gradient descent algorithm. The reinforcement learning method is proved to be correct and feasible by simulation of a double inverted pendulum system.     

基于T-S模型的二级倒立摆模糊控制研究

对非线性、强耦合和绝对不稳定的二级倒立摆进行了运动分析,并针对实验用的二级倒立摆,建立了其运动的状态方程.根据实验中运动的特点,提出了基于T-S模型的模糊控制,取得了较好的控制效果.     

一级倒立摆SIRMs动态权重模糊推理算法仿真

为解决倒立摆采用常规模糊控制算法,其推理规则数目随输入变量成指数增长的问题,对直线一级倒立摆设计了基于SIRMs和动态连接权重模型的模糊控制器,该控制器将倒立摆的四输入变量的模糊控制化成单维模糊控制问题来解决,大大减少了模糊规则数。仿真结果表明,该控制算法能够较好地完成倒立摆的运动控制,具有很强的鲁棒性和动静态特性。     

Adaptive control using interval Type-2 fuzzy logic for uncertain nonlinear systems

A new adaptive Type-2 (T2) fuzzy controller was developed and its potential performance advantage over adaptive Type-1 (T1) fuzzy control was also quantified in computer simulation.Base on the Lyapunov method,the adaptive laws with guaranteed system stability and convergence were developed.The controller updates its parameters online using the laws to control a system and tracks its output command trajectory.The simulation study involving the popular inverted pendulum control problem shows theoretically pre...     

基于Sugeno模型的倒立摆模糊控制及实验

为实现对倒立摆非线性不稳定系统的精确控制,利用Sugeno模糊模型将其处理成多个局部线性模型的模糊逼近,并设计了模糊控制器．通过Simulink对倒立摆系统建模仿真,控制效果分析比较．现场实验表明,该算法能够实现对单级倒立摆的稳定控制,具有结构简单、良好的稳定性和快速性等优点,对其他非线性不稳定系统的控制提供了一种有效的途径．     

单级倒立摆智能控制器的设计

针对直线一级倒立摆系统的平衡控制问题,应用模糊控制理论设计了控制器.介绍了数学模型的建立和模糊控制器的设计过程.仿真及时控结果表明,模糊控制方法对于典型非线性自不稳定系统有着很好的控制能力.     

一种基于融合函数的二级倒立摆模糊控制器设计

采用模糊控制理论研究二级倒立摆控制问题。设计融合函数以降低模糊控制器输入变量维数,引入一种与系统动态性能密切相关的性能指标,提出应用混沌优化算法优化加权矩阵来提高控制系统的性能。本方案在实物二级倒立摆上进行了实验,结果证明该设计方法有效。     

智能控制理论在倒立摆控制系统中的应用

倒立摆系统是一个复杂的非线性系统。本文建立了倒立摆的一种非线性数学模型，在分析的基础上，为倒立摆系统设计了一种模糊控制方案。我们对一级倒立摆系统进行了控制，取得了较好的效果。