Hou Chao-zhen

Time-varying Sliding Mode Controls in Rigid Spacecraft Attitude Tracking

To solve the problem of attitude tracking of a rigid spacecraft with an either known or measurable desired attitude trajectory, three types of time-varying sliding mode controls are introduced under consideration of control input constraints. The sliding surfaces of the three types initially pass arbitrary initial values of the system, and then shift or rotate to reach predetermined ones. This way, the system trajectories are always on the sliding surfaces, and the system work is guaranteed to have robustness against parameter uncertainty and external disturbances all the time. The controller parameters are optimized by means of genetic algorithm to minimize the index consisting of the weighted index of squared error (ISE) of the system and the weighted penalty term of violation of control input constraint. The stability is verified with Lyapunov method. Compared with the conventional sliding mode control, simulation results show the proposed algorithm having better robustness against inertia matrix uncertainty and external disturbance torques.

A Integrated Map Matching Algorithm Based on Fuzzy Theory for Vehicle Navigation System

Most car navigation systems estimate the car position from dead reckoning and the Global Positioning System (GPS). However, because of the unknown GPS noise, the estimated position has an undesirable error. To solve this problem, a map-matching method is introduced, which uses a digital road map to correct the position error. In this paper, based on analyzing the pure geometry and possibility statistics map matching algorithms, a integrated map matching algorithm based on fuzzy logic is proposed by synthesizing the merits of two methods and the topology relation of the real road network, and using the concept of the fuzzy theory. The real road experiments demonstrate the effectiveness and applicability of the proposed algorithm

A Soft Sensing Method Based on Process Neural Network

A new method of soft sensing based on process neural network (PNN) is proposed in this paper. PNN is an extent of traditional neural network, and it is a new configuration of artificial neural network put forward in recent years. The thesis discuss some modified algorithms for raising training speed of PNN, these algorithms are based on function orthogonal basis expansion which exist low-speed convergence in network training. An improved algorithm for BP network based on function orthogonal basis expansion in process neural network for soft sensing is researched. After increasing the normalizing rule on original algorithm, and introducing function momentum adjustment item and learning rate automatically adjustment method for network weight function, which has means of zero and standard deviations of one, the training time of learning algorithm for process neural network is reduced, and a good effect is represented by simulation in wastewater treatment system

Application of dynamic programming theory to the alignment of SINS

This paper aims at discussing how to apply, the dynamic programming theory to the alignment of SINS on the background of land navigation with its own idea of closed-loop. By taking the state as the feedback and optimizing the control strategy it shows that the system on how to proceed from the dynamic programming theory and how to find the strategy can help to achieve the alignment on the condition of the quadratic performance index.

Fuzzy Control for Networked Control System

Feedback control systems wherein the control loops are closed through a real-time network are called networked control systems (NCS). The insertion of the communication network in the feedback control loop makes the design of controller in NCS complex. In this paper, a fuzzy control method for networked control system is introduced. In order to evaluate feasibility, a networked control system for double inverted pendulum control is implemented on campus network. The experimental result shows that the fuzzy control is an effective method for NCS due to its robustness against parameter uncertainty.

Mathematic Model and Optimal Control Method Based on Hybrid Intelligent System

A mathematic model based on RBF neural network and genetic algorithm for multivariable optimal control with the lowest operational cost by limiting total substrate discharge in activated sludge process are discussed in this paper. It shows that the RBF neural network has preferable convergence for modeling the process. Genetic algorithm is an effective searching method to resolve the optimal problem in this case. Based on satisfying the requirements of precision, binary coding is used to express units, and 20 bits of binary digits express DO, Qw separately. According to the adaptive degree of units, which can be operated genetically through genetic operator, superior units can be saved, inferior ones are eliminated, and a group of new units can be obtained. The optimization strategy made up of RBF neural network and genetic algorithms is adopted. After achieving the discharge standard of biochemical oxygen demand, the control rule for variables to make operation cost be least is found.

One modified method of characteristics used to analyze the multiconductor transmission lines

Abstract To solve the coupling effect of multiconductor transmission lines excited by external electromagnetic wave, the modified method of characteristics is proposed. The modified method of characteristics which can compute the terminal induced voltages excited by the external electromagnetic wave when the terminal networks or interconnection networks contain the dynamic elements is introduced. The simulation results indicate that the modified method can analyze the terminal induced voltages when the terminal networks or the interconnection networks contain the dynamic elements excited by the external electromagnetic wave. And the results are compared with the results acquired by FDTD method, the two results are completely same. So one effective modified method is implemented to compute the transmission lines.