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.     

Nonlinear Predictive Control of Small Sag cable with Axial Displacement

An approach for the control of a small sag cable is presented.The approach is based on a nonlinear predictive control scheme that determines the required axial displacement input so that the predicted response match the desired Results.It is accomplished by minimizing the norm squared local errors between the predicted and desired values.Finally simulation results that use the new control strategy to demonstrate the effective are show.     

Analysis on Control Strategy for a Kind of Uncertain Complexity System

In practical industrial fields, it is very difficult to control uncertain complexity object effectively, because there are various uncertainties from internal dynamic characteristic and external, surroundings disturbance for controlled object. Its complexity is not only shown in structure, but also in information relation, so it is uneasy to implement it on technology. Aimed at selecting puzzle of control strategy for uncertain complexity system control, the authors describe cybernetics model based on analysis on some kinds of control strategy and contrastive research, and the model is applied in physical engineering system successfully. Finally, the authors point out that intelligent control strategy should be selected for uncertain complexity system control, and that Human Simulation Intelligent Control is a wise choice.     

Predictive Control Approach for Active Power Filter Based on Neural Networks

In this paper, a predictive control approach using neural networks for active power filter is proposed. The control system of active power filter using this method make using of the internal model control technology based on neural networks, meanwhile, to solve its questions such as lag because of calculating using neural networks, a predictive model based on neural networks is introduced. Simulation analysis shows that this control approach can compensate the lag of system and take advantage of self-adaptive characteristic of neural networks. Good result can be obtained.     

A Kind of Synthetical Fuzzy Control System of Carburization

Carburization is a complex manufacturing process with serious disturbance and time delay,which is multi-variable and uncertain.It is difficult to set up an exact mathematic model.Moreover,by means of conventional control methods and experience,satisfying control result can't be obtained.In this paper,according to the characteristic of controlled-object,Fuzzy control method has been chosen to design a Fuzzy-PI controller and a mixed-PID Fuzzy controller.The result of simulation shows that the robust performance and control result of the new Fuzzy controllers is better than the conventional PID controller.The overshoot is smaller.It is helpful to improve the quality of carburizing workpieces.     

L2 Controller Design for Nonlinear Systems with Dead- Zone Modification

An adaptive robust L2design method with dead -zone modification is proposed for a class of nonlinear systems with external disturbances and uncertain parameters. With priori knowledge of the bounds of the parametric uncertainties, the smooth projection method and the dead zone modification technology are incorporated into the adaptive laws to prevent parameter drifting and reduce the computation burden. The number of parameter estimates is minimal for the adaptive system. The algorithm can guarantee the tracking errors asymptotically converge to the desired dead zone if the external disturbances are zero. The system satisfies L2 disturbance rejection criterion when the external disturbances are considered.     

Complex Correlation System Control Based on Human Simulation Intelligence

Because there are lots of difficulties in mathematic modeling for uncertainty complex object, so it is not suitable to utilize traditional methods in some aspect such as identification, analysis, synthetical design and implementation. Especially, for the idea of system modeling, it is very different in comparison with traditional mathematic modeling, therefore, it is very difficult to study control problem for uncertainty system in technology and theory. On the basis of summarizing characteristics of uncertainty complex object, present to establish generalized control model, and apply intelligent control strategy to implement control effectively. In addition, authors also studied control algorithm and system structure. Engineering practice shows that it is effective and successful to establish generalized control model, and select intelligent control strategy for uncertainty control system.     

The compensation method for the random time-delay of the feedbackchannel and the forward channel in the networked control system

An improved predictive control method is presented to compensate the random time delay in the networked control system. The feedback time delay is compensated by predictive controller based on softened increment input strategy. The forward time delay is unknown for controller, so an extra feedback loop is added to compensate the delay by estimating the error between the actual control signal effected on plant and the output of controller in historical moment. For the controlled system with unknown or slowly varying parameters, the networked feedback correction algorithm is discussed based on a modified recursive least-squares identi cation algorithm. The system stability is analyzed and the simulation results show that the time delay in the networked control system can be accurately compensated. The excellent network performance is ensured with this strategy.     

Equivalent Duffing System Method of Nonlinear System Dynamic Reliability Analysis

Equivalent linear system method is the main method of nonlinear structural system random response analysis. While it would generate big error when the results of equivalent linear system method are used to analyze the structural dynamic reliability. Through minimum mean square error principle, general nonlinear system was converted to equivalent Duffing nonlinear system, based on which the structural dynamic reliability was analyzed. The accurate steady state analytic solution of Duffing nonlinear system can be worked out by FPK equation, so it is not only convenient but also accurate to analyze structural dynamic reliability by using the equivalent nonlinear system method. It is also shown that the analysis results of equivalent nonlinear system method presented here is reliable and the calculation accuracy is higher than equivalent linear system method apparently through the example analysis.     

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.     

An Adaptive Control (AC) Strategy for Internal Grinding with Power Sensor

An adaptive control (AC) strategy for internal grinding with power sensor is proposed.The strategy consists of control strategy of grinding without empty travel,fuzzy control strategy of grinding with constant power,and state monitoring control strategy of grinding wheel.The fuzzy control strategy with constant power is discussed emphatically.     

The Adaptive Robust Control Based On Synthetical Algorithm

An adaptive robust control based on synthetical algorithm was presented in this paper. The main character is to put the expert knowledge into parameter identification to realize the robust estimation, and combine the generalised minimum variance control with the pole assignment control for improving the robustness of the controller. Numerical simulation has proved that the control scheme suggested by authors quite prevailed over the general adaptive controller in control effects.     

A Real-time Expert System Based on Adaptive Predication of Process State

The combination of the measured real time state acquisition from the process with predictive process for the fature state is used to predicate the variable tendency for process state. This method is useful to avoid delay and enhance real time ability of the control information of the expert system. Also a described knowledge and structure of real time process state are proposed. This system can be applied to other kind of practice systems with slow time variable, uncertainty and delay.     

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.     

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.     

Minimum Error Variance Adaptive Controlling Technique Applied on the Auto Regulation System of the Draw Frames

Firstly, the uncertain factors in drawing process, which cause the changes of the sensor's displacement and the sensor's vibration, are analyzed in terms of mechanics and statistics. Systemic randomictiy and non linearity affect the performance of traditional auto regulation equipment. Using the minimum error variance adaptive controlling technique to solve the uncertainty of controlled system is the ideal method. Secondly, using generalized least square method to achieve the principle and method of the system identifying model is described. Thirdly, the design steps of the minimum error variance adaptive adjustor and the method of the computer simulation are discussed. The result of test running shows that the output of the system follows the input of the system better, the system restrains the random disturbance more efficiently, and the performance of auto regulation is improved apparently.     

PID Control for Multivariable System Based on Ameliorative RBF Neural Networks

Aiming at systems which are of characteristics of multi-input and multi-output, nonlinearity and time-variation in the industrial control fields, this paper presents a intelligent PID control method based on ameliorative RBF neural networks, which constructs RBF neural networks identifier on-line and identifies a controlled object on-line by means of adopting the nearest neighbor-clustering algorithm, and adjusts parameters of PID controller on-line and realizes decoupiing control of multivariable, nonlinear and time-variation system. The simulation result indicates that the controller can get parameters which are optimal under some control law, it makes the decoupled system, compared to the PID control method based on the conventional RBF neural networks, has perfect dynamic and static performances, possesses the advantages of high precision, quick response speed and is of great adaptability and robustness.     

Study of Multivariable Adaptive Fuzzy Control Algorithm With Hierarchical Structure

In this paper a class of multivariable fuzzy adaptive con trol algorithm withdecoupling design is propoed,The proposed simplification controller is based on the decompositiontheorem of muItivarinble fuzzy conditional statement andhierarchical multirules structure,Thefuzzy modification of controller's rule is achieved by looking for a medifiable coefficient table forrestraining disturbance.Simulation results demonstrate the effectiveness of the controller.     

A Kind of Synthetical Control Algorithms with Extensive Adaptability

A kind of synthetical control algorithms is presented in this paper.The controlstratege achieved by com bination cloed-lcop pole assignment and modified Smith predictor as wellas self-tuning control is useful to reailze extensive adapability and satisfactory prformence-robust-ness. Digital simulation experiment demonstrated the availability.     

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.