Robust adaptive control for nonlinear chaotic systems

Tracking control of a nonlinear uncertain Chua's chaotic system is studied. With coordinate transform, Chua's chaotic system is transformed to a general form of a strict feedback control system. Combining the backstepping method with robust control technology, an adaptive parameter control law for a robust output feedback control scheme is developed for output tracking of nonlinear unknown systems. It is shown that the derived robust adaptive controller based on Lyapunov stability theory can guarantee global uniformly bounded ultimate property for all states of the closed loop system, and lead to tracking error decreasing at exponential speed. The simulation results show the effectiveness of the proposed approach.     

Fuzzy Control of Vehicle Semi- active Suspension System via Magneto- rheological Fluid Damper

A quarter - car suspension system in which the MR fluid dampers are used as the semi - active control actuators has been proposed and developed based on the theory analysis of MR fluid dampers characteristic. A adaptive fuzzy logic controller is developed. The completed system is simulated via SIMULINK Toolbox of MATLAB 6.1. Compared to the passive suspension system and simple fuzzy logic controller, the adaptive fuzzy logic controller markedly reduces the vertical acceleration of the sprung mass and effectively improves ride comfort of automobile.     

Exponential Stability Analysis for TS-model-based Fuzzy Chaotic Control Systems With Delays

The issue of exponential stability for TS-model-based fuzzy chaotic control systems with constant or time-varying delays is investigated. A linear-feedback fuzzy controller is designed using parallel dispersal compensation technique for a large class of chaotic control systems. By means of the Lyapunov-Krasovskii functional approach in combination of the linear matrix inequality and differential inequality techniques, the criteria for exponential stability of the TS-model-based fuzzy controller are derived, and the corresponding control laws are presented. The results are easy to be calculated numerically because of the linear matrix inequalities representation.     

Fuzzy Adaptive Variable Structure Control of Speed Process of DC Motor

The load changed of motor affects normal run. If it is not adjusted, the state of motor will become poor, while it affects steady of the system. This paper presents an efficient method for the motor speed controlling, it applies Tacagi-Sugeno's method that each linear submodels are connected for fuzzy algorithm, an algorithm of the fuzzy model identifying and fuzzy control is fulfilled through whole nonlinear model which are replaced with local linear models. It makes speed of motor to maintain steady, and variable structure control is applied to guarantee performance of dynamic flowing for process controlled, the design of stability and robustness is proposed in the scheme. Practice running results show that it is efficient that uncertain dynamic processes are controlled.     

A Kind of Fuzzy System Modelling and Control Based on Neural Networks

Combining artificial neural networks(ANN) with fuzzy system theory,a kind of modelling & control method of fuzzy system based on ANN is presented.The simulation researches have verified that the proposed approach can be applied effectively to a number of control systems which are defficult to build strict mathematical model.     

Research on Fuzzy Control Based Two-level Handstand Pendulum System

The two-level handstand pendulum system based on fuzzy control is designed. It is impossible to adopt classical control theory, for the mathematical modeling of the system is nonlinear, the fuzzy control is proposed to improve the former linear control algorithms. For the key to fuzzy control is control rule, it becomes most important to design suitable fuzzy rules. Two methods, one based on experience and the other based on system simulation, are presented to set u Pan effective rule base. The practice proves that the control result is of well stability. A control example for two-level handstand pendulum system shows that the control law is efficient and more robust than the classical state feedback optimal control law with respect to errors produced by the external disturbances.     

A fuzzy logic multicasting algorithm for ad hoc networks

To increase the multicasting efficiency of Ad hoc networks, a fuzzy logic multicasting algorithm (FLMA) is proposed. FLMA adopts the fuzzy logic to tolerate the imprecise information caused by dynamic network topology. The two input variables of the fuzzy logic system are the relative degree of the additional coverage node number and the relative degree of the residual energy. The deferring time of rebroadcast is the output variable of the fuzzy logic system, which is used to optimize the priority of the nodes to rebroadcast. FLMA reduces the redundant retransmission and the chance of the contention and collision, while balances the energy consumption of the nodes. Simulation results reveal that the FLMA achieves better performance than BCAST in terms of the network lifetime, average end-to-end delay, the average number of drops per node and the throughput.     

Analysis of Fuzzy Control Strategy Based on Energy and Peak Value of Vibration

The effect of damping on vibration is analyzed during the suspension deformation based on the 1/4 vehicle model. The Fuzzy logic control strategy for the semi-active suspension was proposed. By means of dissipating the energy of vibration and decreasing the peak value of the body acceleration via the action of damping, the parameter-self-adjust fuzzy controller was established. The united simulation of ADAMS and Matlab/Simulink were used for demonstrating the efficiency of the controller. Comparing with the practical passive suspension possessing the nonlinear character and the present fuzzy controller, the control system based on the new-style strategy provides better ride comfort and road handling.     

Adaptive Iterative Learning Control of Nonlinear Systems with Unknown Control Direction

An adaptive iterative learning control scheme is proposed for a class of mismatched nonlinear systems with periodic uncertainties and unknown control directions. The control algorithm has the following three features;the control method does not need control direction information; the periodic uncertainties are learned online by a learning control method; and the algorithm can deal with mismatched uncertainties. The proposed method can achieve asymptotical convergence along a learning repetition horizon. A simulation example is provided can demonstrate the effectiveness and feasibility of the control strategy.     

Frequency tracking method and simulation for coriolismass flowmter based on a new adaptive notch filter

In order to improve the long-playing frequency tracking ability in the signal processing for coriolis mass flowmeter (CMF) , a new IIR adaptive notch filter (ANF) is developed via steiglitz-mcBride method (SMM) and applied to filter the sensor output signal whose frequency, amplitude and phase are time-varying following the random walk model. The proposed method can detect the signal frequency fleetly and track the frequency variations continuously. The tracking performances of the proposed method and the adaptive lattice notch filter (LANF) method are investigated with computer simulations. Simulations demonstrate the superiority of the proposed method and show it has advantages of fast convergence rate, insensitivity to initial phase variations, and higher long-playing tracking stability and accuracy than LANF method.     

THE CHARACTERISTICS OF FUZZY AND ADAPTIVE PI COORDINATE DECISION CONTROLLER

On the basis of analysing problems of conventional fuzzy controller in the design of the structure and algorithm, this paper puts forward a kind of control algorithm that is fuzzy and Adaptive PI coordinate decision controller (FACDC) . It adopts the hierarchial control structure imitating intelligent control and combines fuzzy controller and adaptive PI controller. It takes full advantage of two controllers. Since the algorithm of FACDC in model reference fuzzy adaptive algorithm, it avoids the complexity of conventional adaptive algorithm.     

Image edge detection algorithm in fuzzy domain

An image edge detection algorithm in fuzzy domain is proposed, which combines adaptive fuzzy enhancement and multi direction fuzzy morphology to detect the edges of fuzzy image. The adaptive fuzzy enhancement method enhances the fuzzy image within blocks with sliding windows to avoid losses of the real edges resulting from enhancing with single threshold for the whole image and lead strong adaptive ability to image region variance. The multi direction fuzzy mathematical morphology operates on the enhanced fuzzy image with structure elements of multiple different directions to extract the real edges with directionality and restrain non directional noise. Experiments show the algorithm can detect fuzzy image edge effectively with strong antinoise ability.     

Chaotic Synchronization of Discrete Nonlinear Systems on Different Parameters for a Kind of Systems

According to the theory of parameters adaptive control and variable driving, for a kind of discrete nonlinear chaotic systems, a method of chaotic synchronization is presented for different system parameters. In the equation of parameters adaptive control, control coefficients are chaotic signal. Because control coefficients are constant usually for the former parameters adaptive control, so the parameters adaptive control method is very universal, namely the former parameters adaptive control methods are special examples in our control method. Simulation results show that this method is available.     

Temperature Control of High Temperature Melt Viscosity Test Instrument

The construction and function of high temperature melt viscosity test instrument are introduced in this paper. And the character of temperature control system is given. In order to improve dynamic and static performance of the temperature control system, a new method is proposed which combines threshold control with adaptive fuzzy PID control. While the current temperature of the heating furnace is much lower than the given temperature, threshold control works in order to protect system and improve dynamic performance. When the current temperature is close to the given value, adaptive fuzzy PID control works to improve static performance. At last, simulation and experiment results validate the proposed control method.     

Intelligent Control of the Nonlinear Seismic Response of a 3 Story Benchmark Building

Intelligent materials, control devices and intelligent control algorithm research and development in recent years have opened a new world for seismic resistance and disaster reduction in civil engineering. We designed and fabricated a new piezoelectric friction damper. In our research, we regarded the piezoelectric friction damper as the control device. We proposed a fuzzy control algorithm for reducing nonlinear seismic response of a 3 story benchmark building and established the interactive relationships between structural responses and fuzzifier factors, defuzzifier factor. A numerical simulation is carried out to analyze the nonlinear seismic responses of the controlled 3 story benchmark building. The simulation results are compared to those of other control strategies. The results show that the fuzzy control can reduce the nonlinear seismic response of 3 story benchmark building and minimize the structural damage caused by strong earthquakes.     

Hybrid distributed model predictive control using dynamic cooperation

For complex cooperative control problem of multi-agent systems, a distributed model predictive control scheme based on the dynamic cooperative rules is proposed. The collision avoidance constraint is transformed into hybrid rules based on the positions, and the Boolean function term was introduced in the cost function. In order to accommodate the complex time-varying environment, at each sample moment, the dynamic cooperative rules are designed according to the relative positions among the agents and the relative distances between the agents and the destination, so as to determine the weights in the Boolean function. This scheme reinforces consistencies of motion direction and control action, and improves stability and feasibility of distributed predictive control. As the control target can be achieved via a small prediction horizon, this scheme also enhances the real-time ability and practicality of distributed model predictive control. Simulation examples are given to illustrate the effectiveness of the proposed scheme.     

基于PID算法对热风炉热风温度控制及仿真分析

在农作物金银花干燥期间,为了满足干燥热风的温度和风速的稳定性要求,针对热风炉出风口热风温度与设定的温度值滞后和稳定性差等问题,设计了一套以单片机STM32作为核心处理器和模糊自适PID算法作为参数控制器的控制系统,实现模糊自适PID算法对参数进行调整。利用Matlab语言建立模型,对整套控制系统进行仿真分析。结果表明:出风口热风温度的稳定性能达到95%以上,该研究方法提高控制系统的性能,有利于农作物干燥的质量和效率。     

One Auto-optimized Fuzzy Control of Car-engines Based on GA

One new auto-optimized fuzzy control model of car-engines based on genetic arithmetic was introduced aiming at the problem that the parameters of car-engine are of the discreteness, the nonlinear and the uncertain characteristics, with which it is difficult to control accurately the excess air coefficient and the spark advance angle in dynamic procedure when using a conventional control model. The rule of the design scheme of the controller achieving the model based on embedded operating system is discussed. Further more, it is probed that the fuzzy control parameters are optimized automatically using GA based on on-line real-time simulation. The results of experiments show that the dynamic indexes and the fuel economy indexes are all improved greatly and the advantage of the model on reducing fuel consumption is obvious.     

Application of fuzzy Neural Networks to Evaluation of Transversal Economic Benefits

A new method for evaluation of transversal economic benefits is researched by fuzzy neural networks, BP algorithm is used to learn the connection weights of the fuzzy neural networks and partitions of fuzzy subsets. It has been shown by the modeling and evaluating results about the economic benefit index system of ten enterprises that the method has reinforcement learning properties and universalized capabilities. with respect to modeling and evaluating of nonlinear systems which have some uncertainties, the method is available.     

模糊控制系统的垂直切换判决算法分析

针对模糊控制系统中切换时延较长和切换次数较多的问题,研究了一种基于模糊控制系统的垂直切换判决算法。在基于信号强度判决时将RSS作为门限值,提高系统判决能力;同时,将网络参数和服务类型作为判决因素,结合层次分析法引入并行的模糊控制系统,缩短了切换判决的时间、选择适合用户的最佳网络,做出垂直切换。仿真结果表明,该算法减少了切换次数,降低了切换时延,增强了系统的性能。与传统的切换算法相比较,该算法对切换的判决因素考虑的更全面,兼顾了用户终端的使用环境及成本问题,有效地保证了网络的服务质量。