Robust direct adaptive fuzzy control for nonlinear MIMO systems

For a class of nonlinear multi-input multi-output systems with uncertainty, a robust direct adaptive fuzzy control scheme was proposed. The feedback control law and adaptive law for parameters were derived based on Lyapunov design approach. The overall control scheme can guarantee that the tracking error converges in the small neighborhood of origin, and all signals of the closed-loop system are uniformly bounded. The main advantage of the proposed control scheme is that in each subsystem only one parameter vector needs to be adjusted on-line in the adaptive mechanism, and so the on-line computing burden is reduced. In addition, the proposed control scheme is a smooth control with no chattering phenomena. A simulation example was proposed to demonstrate the effectiveness of the proposed control algorithm.     

Science Letters：On stochastic optimal control of partially observable nonlinear quasi Hamiltonian systems

A stochastic optimal control strategy for partially observable nonlinear quasi Hamiltonian systems is proposed. The optimal control forces consist of two parts. The first part is determined by the conditions under which the stochastic optimal control problem of a partially observable nonlinear system is converted into that of a completely observable linear system. The second part is determined by solving the dynamical programming equation derived by applying the stochastic averaging method and stochastic dynamical programming principle to the completely observable linear control system. The response of the optimally controlled quasi Hamiltonian system is predicted by solving the averaged Fokker-Planck-Kolmogorov equation associated with the optimally controlled completely observable linear system and solving the Riccati equation for the estimated error of system states. An example is given to illustrate the procedure and effectiveness of the proposed control strategy.     

An adaptive strategy for controlling chaotic system

This paper presents an adaptive strategy for controlling chaotic systems. By employing the phase space reconstruction technique in nonlinear dynamical systems theory, the proposed strategy transforms the nonlinear system into canonical form, and employs a nonlinear observer to estimate the uncertainties and disturbances of the nonlinear system, and then establishes a state-error-like feedback law. The developed control scheme allows chaos control in spite of modeling errors and parametric variations. The effectiveness of the proposed approach has been demonstrated through its applications to two well-known chaotic systems : Duffing oscillator and Rǒssler chaos.     

Marginal linearization method in modeling on fuzzy control systems

Marginal linearization method in modeling on fuzzy control systems is proposed, which is to deal with the nonlinear model with variable coefficients. The method can turn a nonlinear model with variable coefficients into a linear model with variable coefficients in the way that the membership functions of the fuzzy sets in fuzzy partitions of the universes are changed from triangle waves into rectangle waves. However, the linearization models are incomplete in their forms because of their lacking some items. For solving this problem, joint approximation by using linear models is introduced. The simulation results show that marginal linearization models are of higher approximation precision than their original nonlinear models.     

Process modeling and optimizing control based on sparse nonuniformly sampled data

<正> In this paper,a process modeling and related optimizing control for nonuniformly sampled ( NUS)systems are addressed.By using a proposed nonuniform integration filter and subspace method estimation,an identification method of NUS systems is developed,based on which either an output soft sensor or ahidden state estimator is developed.The optimizing control is implemented by replacing the sparsely-measured/immeasurable variable with the estimated one.Examples of optimizing control problem are given.The proposed optimizing control strategy in the simulation examples is verified to be very effective.     

An Output-error-based Iterative Learning Control Algorithm for Linear Discrete-time Dynamic System

This paper improves the iterative learning control algo-rithm for nonlinear discrete-time dynamic systemswhich proposed by D.-H.Hwang et.al.,and make itpossible to use in the system which can give output erroronly.Then a sufficient condition for asymptotical conve-rgence of iterative learning algorithm is proposed.Thealgotithm can be used to a class of nonlinear systems withunknown but periodic parameters.     

Adaptive Excitation Control with L2 Disturbance Attenuation for Multi-Machine Power Systems

Generator excitation control plays an important role in improving the dynamic performance and stability of power systems. This paper is concerned with nonlinear decentralized adaptive excitation control for multi-machine power systems. Based on a recursive design method, an adaptive excitation control law with L2 disturbance attenuation is constructed. Furthermore, it is verified that the proposed control scheme possesses the property of decentralization and the robustness in the sense of L2-gain. As a consequence, transient stability of a multi-machine power system is guaranteed, regardless of system parameters variation and faults.     

NN adaptive pole placement method for sintering finish point control

A neural network model with a special structure, which is divided into linear and nonlinear parts, was proposed for identification of a nonlinear system. In this model, the nonlinear part of the object is treated as a measured disturbance, and is compensated by a feed forward method; an adaptive pole placement algorithm is used to control the linear part of the object. The simulation results show that the identification efficiency and accuracy are improved when the new controller is applied to sintering finish point control.     

Fuzzy Variable Structure Control of Photovoltaic MPPT System

In order to reduce chattering phenomenon of variable structure control, a fuzzy variable structure control method is adopted and applied in the photovoitaic maximum power point tracking （MPPT） control system. Firstly, the electric features of PV cells and a dynamic model of photovoitaic system with a DGDC buck converter are analysed. Then a hybrid fuzzy variable structure controller is designed. The controller is composed of a fuzzy variable structure control term and a supervisory control term. The former is the main part of the controller and the latter is used to ensure the stability of the system. Finally, the conventional variable structure control method and the fuzzy variable structure control method are applied respectively. The comparing of simulation results shows the superiority of the latter.     

Modified Projective Synchronization of a New Hyperchaotic System with Known or Unknown Parameters

This work presents two different methods-- nonlinear control method and adaptive control approach to achieve the modified projective synchronization of a new hyperchaotic system with known or unknown parameters. Based on Lyapunov stability theory, nonlinear control method is adopted when the parameters of driving and response systems are known beforehand; when the parameters are fully unknown, adaptive controllers and parameters update laws are proposed to synchronize two different hyperchaotic system and identify the unknown parameters. Moreover, the rate of synchronization can be regulated by adjusting the control gains designed in the controllers. The corresponding simulations are exploited to demonstrate the effectiveness of the proposed two methods.     

A Text Categorization System with Soft Real-Time Guarantee

In order to provide predictable runtime performante for text categorization （TC） systems, an innovative system design method is proposed for soft real time TC systems. An analyzable mathematical model is established to approximately describe the nonlinear and time-varying TC systems. According to this mathematical model, the feedback control theory is adopted to prove the system＇s stableness and zero steady state error. The experiments result shows that the error of deadline satisfied ratio in the system is kept within 4 of the desired value. And the number of classifiers can be dynamically adjusted by the system itself to save the computa tion resources. The proposed methodology enables the theo retical analysis and evaluation to the TC systems, leading to a high-quality and low cost implementation approach.     

Modeling and stabilization for a class of nonlinear networked control systems: a T-S fuzzy approach

The stabilization problem for a class of nonlinear networked control systems （NCSs） is investigated in this paper. A Takagi Sugeno （T-S） fuzzy model is employed to represent the nonlinear controlled plant in the NCSs. Based on parallel distributed compensation （PDC） technique, a novel model of the nonlinear NCSs is established firstly in consideration of both network-induced delay and packet losses in transmission. Then a stability condition of the nonlinear NCSs is presented in terms of linear matrix inequalities （LMIs） by using the Lyapuno~Krasovskii functional method and the delay input approach. Furthermore, the control design of the nonlinear NCSs is developed by means of LMIs. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.     

Robust H∞ Control of Hamiltonian System with Uncertainty

This paper investigates the robust H∞ problem for a class of generalized forced Hamiltonian systems with uncertainties. The robust L2-gain was proved for the Hamiltonian with a sufficient condition for stable control of multimachine power systems expressed as a matrix algebraic inequality. A similar sufficient condition was then extended to the robust H∞ control of Hamiltonian systems to construct the state feedback H∞ control law. A numerical example is given to verify the validity of the proposed control scheme, which shows the effectiveness and promising application of the method.     

An integrated scheme of neural network and optimal predictive control

An approach of adaptive predictive control with a new structure and a fast algorithm of neural network (NN) is proposed. NN modeling and optimal predictive control are combined to achieve both accuracy and good control performance. The output of nonlinear network model is adopted as a measured disturbance that is therefore weakened in predictive feed-forward control. Simulation and practical application show the effectiveness of control by the proposed approach.     

PREPARATION AND PROPERTIES OF MAGNETICALLY RESPONSIVE MICROCAPSULES OF THE DRUG FOR INJECTION USE

Microcapsules consisting of human serum albumin, magnetite and a prototype drug (5-fluroouracil) are obtained from emulsion by heat-stabilization. The most probable diametre of the magnet-sensitive microcapsules is 0.15 pm. The microcapsules satisfy the requirement of the magnetic carrier of drugs for the use of injection from the view points of size and shape. Flowing in an aqueous suspension, the microcapsules can be retained easily by the control of a magnetic field. The drug can be released gradually from the microcapsules when they are in physiological saline. The microcapsules prepared at the stabilization temperature of 200C have the least variable of permeation property. A structure model of the microcapsules has been proposed.     

Robust fault tolerant control of uncertain time-delay linear systems

Robust fault tolerant control for a class of time-delay linear systems with parameter uncertainties is studied, and a time-delay related state feedback control is proposed. On the basis of Lyapunov method , we prove that the proposed control law has integrity against sensor and/or actuator failures if the correspondent sufficient condition can be satisfied. A heuristic algorithm is also provided to facilitate the realization of the fault tolerant control. Finally, a simulation example is presented to show the effectiveness of the proposed approach.