Sun Youxian

Support vector machine based nonlinear model multi-step-ahead optimizing predictive control

A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-a-head optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.

Adaptive predictive control based on wavelet approximation models

Discusses approximate modelling and adaptive control of discrete time linear time-varying systems (LTVS). A novel model, the representation of LTVS in the wavelet time-frequency space, is established based on discrete wavelet transforms. The joint time-frequency properties of wavelet analysis are appropriate for describing the nonstationary characteristic of LTVS. In this model parameter estimation is fulfilled by means of some algebraic calculations. Finally, an adaptive predictive control scheme is proposed based on this representation. Simulation results demonstrate its good behaviour.

Mean-field Theory for Some Bus Transport Networks with Random Overlapping Clique Structure

Transport networks, such as railway networks and airport networks, are a kind of random network with complex topology. Recently, more and more scholars paid attention to various kinds of transport networks and try to explore their inherent characteristics. Here we study the exponential properties of a recently introduced Bus Transport Networks (BTNs) evolution model with random overlapping clique structure, which gives a possible explanation for the observed exponential distribution of the connectivities of some BTNs of three major cities in China. Applying mean-field theory, we analyze the BTNs model and prove that this model has the character of exponential distribution of the connectivities, and develop a method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the exponents. By comparing mean-field based theoretic results with the statistical data of real BTNs, we observe that, as a whole, both of their data show similar character of exponential distribution of the connectivities, and their exponents have same order of magnitude, which show the availability of the analytical result of this paper.

Optimal centralized state fusion estimation for multi-sensor system with correlated measurement noise

Many centralized fusion estimation algorithms assumed that measurement noises among sensors are uncorrelated, but it is not true when there exist the same unmodeled measurement noise source. In this paper, based on the matrix similarity transform, covariance matrix of correlated measurement noise is successfully parallel decomposed and the linear measurement models are transformed. Optimal centralized fusion estimation algorithm is presented. When measurement noises are uncorrelated, the results here are reduced to the standard optimal centralized fusion estimation algorithms.

H ∞ analysis and controller synthesis via a descriptor system approach

The problem of Hinfin analysis and synthesis of linear uncertain systems is addressed via parameter-dependent Lyapunov functions. The polyqudratic Hinfin stability as well as the affine quadratic Hinfin stability is defined, and the corresponding sufficient conditions of LMI form are derived based on a descriptor system approach. By refraining the slack matrices, Hinfin state feedback controllers could also be constructed by LMI constraints. The effectiveness is demonstrated by a numerical example.

Study and application of dynamic matrix control in refinery processes

Advanced process control (APC) technologies have been widely used to improve plant profitability and reliability in the last several decades. Dynamic matrix control (DMC) is one of these technologies, and plays an important role in refinery and petrochemical plant APC practice. There are many factors that may impact the success of an APC project, including technological and non-technological issues. The engineering aspects of APC project are discussed and explained by the details of execution of a crude unit APC application. The purpose of this paper is to highlight how to integrate DMC technology and fundamental process control knowledge for getting the APC project done.

Robust guaranteed cost control with H/sub /spl infin//-/spl gamma/ disturbance attenuation performance

The paper is concerned with the design of robust guaranteed cost controller with H/sub /spl infin//-/spl gamma/ disturbance attenuation performance for linear systems with norm bounded parameter uncertainties and disturbances.

Fuzzy neural network control technique and its application in a complex intersection

For a lack of complex intersection effective control method, a novel single intersection intelligent control technique was presented. Signal lamp setting, phase division, pedestrian press-button response, signal lamp advance-off and late-on were discussed in detail. Being a function of traffic flow saturation degree the cycle time was optimized by one fuzzy neural network, and the variable splits were adjusted based on historical and real-time traffic information. The goal was to make the intersection expedite, vehicles stop rate shortest and average vehicle delay time less. However, the crossing-crosswalk safety of pedestrian had to be assured firstly. This control technique has been used successfully in the intersection of No. 9 road and No. 12 road in Xiasha economic technical development area Hangzhou, China, and the application results have shown to be very good

A traffic flow model and intelligent control technique for urban trunk road

This paper uses large-scale systems decomposition-coordination principle, fuzzy theory and neural networks technique to solve the problem of real time coordinated control for urban trunk road. Firstly, a neural macroscopic dynamic model based on the Kashani model for urban trunk road is proposed. Secondly, a two-level coordinated fuzzy control method implemented by neural network is presented. Based on the traffic volume data measured from each intersection the coordinated layer is coordinated with the number vehicle between the adjacent intersections. The operating layer adjusts on-line signal cycle and splits of every intersection. The object of the control method is to unblock the traffic trunk road and to shorten the average vehicle delay time. The simulation shows the proposed method has better performance than the fixed time control method.

Optimal fusion estimation covariance of multisensor data fusion on tracking problem

A measurement fusion function is defined and shown to decide the covariance of measurement fusion. If two multisensor data fusion systems have identical measurement fusion function, then the systems have identical covariances. The greater the function is, the less covariance the fusion system can obtain.