基于Petri Fault Net的磁浮列车悬浮系统故障分析

随着磁浮列车技术的商业化应用,磁浮列车系统故障诊断越来越受到重视。以中低速磁浮列车为背景,在建立磁浮列车悬浮系统故障模型的基础上,针对磁浮列车悬浮系统的复杂性,将基于Petri Net的故障诊断理论Petri Fault Net应用于磁浮列车悬浮系统的故障诊断中。给出了基于Petri网的最小割集寻找方法;根据磁浮列车2001年以来的运行数据统计,计算出了节点的P概率/T概率;在此基础上进行了故障定位和影响分析。列车的试验运行验证了提出的理论准确性。     

Reservation-based feedback scheduling of MPC tasks

A reservation-based feedback scheduling (FS-CBS) of a set of model predictive control (MPC) tasks is presented to optimize the global control performance subject to limited computational resource. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The FS-CBS is shown robust against the varying of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.     

主动悬架最优控制性能函数的加权系数研究

为了分析汽车主动悬架最优控制性能函数中加权系数的影响，对某轿车建立了整车七自由度动力学模型，设计了基于随机最优控制策略的状态反馈控制器。通过ADAMS＆MATLAB＼simlllink联合仿真，探讨了性能函数加权系数对悬架控制力的最大幅值及悬架性能的影响，得到了该轿车性能函数中加权系数的变化与主动悬架最优控制力的最大幅值及悬架性能的关系．这为主动悬架最优控制性能函数加权系数的确定提供了参考依据。     

坦克稳定器状态补偿控制研究

基于状态观测、补偿理论，设计坦克稳定器控制系统。通过遗传算法优化控制器参数，结合Monte-Carlo随机试验方法，进行系统的性能鲁棒分析。动态仿真试验表明在考虑系统的本质非线性环节、高频低幅外扰的情况下，所设计的控制器比PI控制具有更高的精度，更小的超调量，比滑模控制具有更强的扰动抑制能力，并且克服了本质非线性环节的影响，具有快速随动、稳态无静差、扰动不敏感的性能。     

Adaptive control method for nonlinear time-delay processes

Two complex properties,varying time-delay and block-oriented nonlinearity,are very common in chemical engineering processes and not easy to be controlled by routine control methods.Aimed at these two complex properties,a novel adaptive control algorithm the basis of nonlinear OFS(orthonormal functional series) model is proposed.First,the hybrid model which combines OFS and Volterra series is introduced.Then,a stable state feedback strategy is used to construct a nonlinear adaptive control algorithm that can guarantee the closed-loop stability and can track the set point curve without steady-state errors.Finally,control simulations and experiments on a nonlinear process with varying time-delay are presented.A number of experimental results validate the efficiency and superiority of this algorithm.     

Best tracking performance for integrator and dead time plant

The optimal tracking performance for integrator and dead time plant in the case where plant uncertainty and control energy constraints axe to be considered jointly is inrestigated.Firstly,an average cost function of the tracking error and the plant input energy over a class of stochastic model errors are defined.Then,we obtain an internal model controller design method that minimizes the average performance and further studies optimal tracking performance for integrator and dead time plant in the simultaneous presence of plant uncertainty and control energy constraint.The results can be used to evaluate optimal tracking performance and control energy in practical designs.     

Universal construction of control Lyapunov functions for a class of nonlinear systems

A method is developed by which control Lyapunov functions of a class of nonlinear systems can be constructed systematically.Based on the control Lyapunov function,a feedback control is obtained to stabilize the closed-loop system.In addition,this method is applied to stabilize the Benchmark system.A simulation shows the effectiveness of the method.     

Distributed power control algorithm based on game theory for wireless sensor networks

Energy saving is the most important issue in research and development for wireless sensor networks. A power control mechanism can reduce the power consumption of the whole network.Because the character of wireless sensor networks is restrictive energy,this paper proposes a distributed power control algorithm based on game theory for wireless sensor networks which objects of which are reducing power consumption and decreasing overhead and increasing network lifetime.The game theory and OPNET simulation shows that the power control algorithm converges to a Nash Equilibrium when decisions are updated according to a better response dynamic.     

The self-organizing worm algorithm

A new multi-modal optimization algorithm called the self-organizing worm algorithm (SOWA) is presented for optimization of multi-modal functions.The main idea of this algorithm can be described as followsdisperse some worms equably in the domain;the worms exchange the information each other and creep toward the nearest high point;at last they will stop on the nearest high point.All peaks of multi-modal function can be found rapidly through studying and chasing among the worms.In contrast with the classical multi-modal optimization algorithms,SOWA is provided with a simple calculation,strong convergence,high precision,and does not need any prior knowledge.Several simulation experiments for SOWA are performed,and the complexity of SOWA is analyzed amply.The results show that SOWA is very effective in optimization of multi-modal functions.     

NECESSARY AND SUFFICIENT CONDITION FOR GLOBAL CONTROLLABILITY OF A CLASS OF AFFINE NONLINEAR SYSTEMS

In this paper, we investigate the global controllability of a class of n-dimensional affine nonlinear systems with n- 1 controls and constant control matrix. A necessary and sufficient condition for its global controllability has been obtained by using the methods recently developed. Furthermore, we generalize the above result to a class of affine nonlinear systems with a block-triangular-like structure. Finally, we will give three examples to show the applications of our results.