基于混合优化的车载三轴光电跟踪策略

由于存在一个冗余横倾轴,三轴光电跟踪系统能够解决两轴光电跟踪系统的跟踪盲区问题,实现针对目标的全空间跟踪。针对车载三轴光电跟踪系统,在分析系统运动学特性的基础上,建立了系统的运动学模型,并提出一种基于混合优化算法的车载三轴联动全空间光电跟踪策略。通过对系统运动学模型的研究,分析三轴转动角度之间的关系,从而将三轴转角增量组合的三变量优化问题简化为求解单变量最优问题,然后应用混合优化算法得到最优的三轴角增量组合。仿真和实验结果证明,所设计的三轴跟踪策略可以得到更小的三轴转动角增量组合,能够实现车载三轴光电跟踪系统的三轴联动全空间连续跟踪运动,提高了系统的跟踪性能,具有良好的实际应用价值。     

基于粒子群优化的导弹分数阶控制器设计

针对传统控制方法控制品质易受气动参数变化影响的问题,结合分数阶微积分理论和LQR最优控制技术,设计了改进的导弹分数阶控制器。首先对于导弹动力学模型进行状态重组,应用LQR技术采用输出反馈,得到基于导弹最优跟踪指标的三回路控制结构,进而构建了广义分数阶控制器结构。为优化控制器参数选择,提出一种综合频域和时域性能的适应值函数,通过粒子群(PSO)算法整定控制器参数。仿真结果表明分数阶控制器具有良好的稳态、动态特性性能。     

空地制导武器最优攻击弹道及跟踪控制研究

针对UCAV对地面目标实施精确打击的问题, 提出了制导武器在多约束条件下的最优攻击弹道及跟踪控制方法。首先建立了弹体三自由度运动学模型, 对非线性运动学方程进行了变换, 无需线性化处理, 利用最优控制中的极小值原理设计了具有时间和终端角度约束的最优攻击末弹道, 可满足精确实时打击要求。随后结合某型空地制导武器弹体数学模型, 利用滑模变结构控制原理设计了跟踪控制器, 实现了对最优攻击末弹道的跟踪。数字仿真结果表明, 所设计的最优攻击末弹道可实现, 并能够在给定约束条件下实施对目标的实时精确打击; 所设计的跟踪控制器鲁棒性好, 跟踪精度高, 并具有良好的动态特性和稳态品质。     

多目标环境中的认知雷达跟踪方法

研究了多目标环境中的认知雷达目标跟踪问题,提出了一种基于波形优化和快速粒子滤波的多目标跟踪方法。在量测模型中,基于采样的接收数据建立量测方程,以克服多目标跟踪中的数据关联问题;在状态模型中,与量测模型相匹配,联合估计目标运动状态（位置、速度）和散射系数。为实现多目标跟踪和提高跟踪性能,从联合收发自适应处理角度出发设计跟踪算法和发射波形:1）接收自适应。由于量测数据的维数以及跟踪模型的非线性程度较高,为实现对多目标的有效跟踪以及降低跟踪算法的运算复杂度,采用改进的粒子滤波方法对目标状态进行实时估计;2）发射自适应。考虑到信噪比与跟踪性能关系以及量测模型的特点,基于最优信噪比准则实现了对发射波形的优化。仿真结果表明文中所提出的跟踪方法能够有效的跟踪上目标,且所设计的自适应波形的跟踪性能优于传统固定波形。      

多扩展目标跟踪中基于多特征优化的传感器控制方法

针对多扩展目标的优化跟踪问题,该文在有限集统计(FISST)理论框架下,提出一种能够综合优化多扩展目标跟踪性能的传感器控制方法。首先,该文给出加权广义最优子模式分配(WGOSPA)距离构造多扩展目标跟踪多特征估计在其统计平均周围的广义离差,进而研究提出多特征融合下的传感器控制最优决策方法,并利用序贯蒙特卡罗(SMC)技术研究传感器控制最优决策过程的数值求解方法,然后利用伽马高斯逆威沙特多伯努利(GGIW-MBer)滤波器实现所提出的传感器控制策略。最后通过仿真实验验证了所提算法的有效性。     

跨音速飞行状态下的一种智能控制方法

讨论了跨音速段飞行状态下复杂的气动特性及其影响,为解决飞机飞行过程中纵向按速度静不稳定这一主要矛盾,设计了一种模糊神经网络控制器,控制器以角度跟踪误差及其微分信号为输入来控制相应的控制舵面,实现对姿态的跟踪控制;以速度跟踪误差及其微分信号为输入来控制油门的偏转,实现对速度的跟踪控制.在此基础上,进一步利用遗传算法对模糊神经网络控制器进行了优化设计,并给出了遗传算法各个参数的选取原则.仿真结果也验证了所设计的控制系统具有良好的性能.     

牛顿迭代法在草图约束优化问题中的改进

为完成草图约束问题的快速高效求解,文中提出基于人工蜂群的牛顿迭代混合算法来求解草图约束问题。其首先用蜂群算法对初值进行全局搜索,并将得到的初值作为牛顿法的初值进行草图约束问题的求解,保证了迭代速度,又避免了问题求解过程中陷入局部最优值,较好地把两种算法的优势结合起来,从而提高草图约束问题求解的速度以及成功率。仿真数据显示,该算法结果是可靠的,有较强的数值稳定性,是一种理想的求解草图约束问题的方法。     

基于PPSO-MPC的多雷达协同反隐身指示搜索任务规划

针对ESM/雷达协同反隐身探测中的指示搜索问题,引入模型预测控制(Model Predictive Control,MPC)理论,给出指示搜索任务规划的MPC框架,建立指示搜索的目标状态预测模型和在线滚动优化模型.针对模型求解,引入粒子群优化(Particle Swarm Optimization,PSO)算法,设计了高维矩阵粒子编码方式,引入尺度计算因子处理边界约束,引入概率模型处理离散变量,设计实现了一种"多主节点-单从节点"的 (Multi-Master-Single-Slave,MM-SS)多种群并行计算策略.仿真结果表明,所建立的模型能够在不确定、多目标环境下实现对多雷达的高效协同控制,所提出的模型求解算法能够实现对滚动优化问题的快速、高效求解,即模型和算法的有效性得到了验证.     

基于混合遗传算法的多目标问题规划

遗传算法在问题优化中的应用已有了许多研究，但对于大型多目标规划问题而言，由于其问题特性和计算量大而限制了遗传算法的应用。为探索新的问题求解方法，提出了一种基于遗传算法和梯度算法的问题优化混合算法。用梯度法每次迭代得到的结果来改进遗传算法的群体，而用遗传算法的最优个体与梯度算法的迭代解相比较，选择其中的最优点作为梯度法下一步迭代的初始点。通过保持迭代过程的最优解，加快了搜索速度，并保证收敛于全局最优解。算例表明该方法兼具遗传算法的全局搜索能力和梯度算法的局部搜索的特点，且具有良好的工程适应性。     

基于通感融合的无人机预编码及飞行轨迹设计

无人机(UAVs)具有机动性强,低成本及易部署等特性,通过搭载通信及感知设备,支持通信与感知技术的高效资源共享,无人机可作为融合通信与传感技术的高性能空中平台。该文针对多输入多输出(MIMO)无人机使能的联合通信、感知场景,综合考虑无人机飞行能量、多天线传输及用户业务需求等限制条件,建模无人机通信、感知预编码及飞行轨迹联合优化问题为多目标优化问题,以实现通信用户最低速率最大化及目标最小发现概率最大化。由于通信用户最低速率最大化问题为非凸优化问题,难以直接求解,将原优化问题分解为通信预编码设计子问题及无人机轨迹设计子问题,并采用交替迭代法依次求解两个子问题直至算法收敛,其中,对于通信预编码设计子问题,提出一种基于迫零(ZF)算法的求解策略;对于无人机轨迹设计子问题,提出一种基于连续凸逼近(SCA)算法的求解策略。基于所得到的无人机最优轨迹,将无人机感知位置选择问题建模为加权距离和最小化问题,进而应用泛搜索算法优化确定目标感知位置,并设计基于ZF算法的通信感知预编码联合优化策略,以实现通信感知性能的联合优化。最后通过仿真验证了该文所提算法的有效性。     

High-speed and high-precision motion control with an optimal hybrid feedforward controller

An optimal feedforward controller which makes a hybrid usage of the shift (q) and delta (/spl delta/) operators is proposed for high-speed and high-precision digital motion control systems. Uncancellable discrete-time zeros arising from sampling the continuous-time plant at high rates, which make the mathematical inverse unstable, are handled in a natural way. The controller is optimized to have good performance in both low and high frequency ranges, and it is able to handle uncancellable discrete-time zeros in the right half plane. The optimization problem is generalized to an H/sup /spl infin// problem. Convex minimization is used to find the solution to the optimization problem. Simulation results and experiments carried out on an MC-510V Matsuura vertical machining center show superior performance of the proposed optimal hybrid feedforward controller.     

Optimal mobility control with energy constraint in delay tolerant networks

Owing to the uncertainty of transmission opportunities between mobile nodes, the routing in delay tolerant networks (DTNs) exploits the mechanism of store‐carry‐and‐forward. In this routing mechanism, mobility plays an important role, and we need to control the mobility of nodes around the network to help with carrying messages from the source to the destination. This is a difficult problem because the nodes in the network may move arbitrarily and it is difficult for us to determine when the nodes should move faster to help the data transmission while considering the complicated energy consumption in such a network. At the same time, for most DTNs, the system energy is limited, and energy efficient algorithms are crucial to maximizing the message delivery probability while reducing the delivery cost. In this paper, we investigate the problem of energy efficient mobility speed control in epidemic routing of DTN. We model the message dissemination process under variable mobility speed by a continuous‐time Markov model. With this model, we then formulate the optimization problem of the optimal mobility control for epidemic routing and obtain the optimal policy from the solution of this optimization problem. Furthermore, extensive numerical results demonstrate that the proposed optimal policy significantly outperforms the static policy with constant speed, in terms of energy saving. Copyright © 2012 John Wiley & Sons, Ltd.     

Neural-network-based predictive learning control of ram velocity in injection molding

In this paper, we develop a predictive learning controller for ram velocity of injection molding based on neural networks. We first introduce a model of describing the injection molding, including the time horizon and the batch index. The feedback control plus biased function is proposed for controlling this plant. More specifically, a radial basis function (RBF) network is used to approximate the biased function based on the time horizon. The weights in the RBF are determined by a predictive control scheme based on the batch index. For this algorithm, relevant convergence is investigated. Simulation results reveal that the proposed control can achieve our claims.     

Mechatronic design and injection speed control of an ultra high-speed plastic injection molding machine

This paper presents pragmatic techniques for mechatronic design and injection speed control of an ultra high-speed plastic injection molding machine. Practical rules are proposed to select specifications of key mechatronic components in the hydraulic servo system, in order to efficiently construct an industry-level machine. With reasonable assumptions, a mathematical model of the injection speed control system is established and open-loop experimental data are then employed to validate the system model. By the model, a gain-scheduling PI controller and a fuzzy PI controller are presented, compared and then implemented into a digital signal processor (DSP) using standard C programming techniques. Experimental results are conducted to show that the two proposed controllers are capable of achieving satisfactory speed tracking performance. These developed techniques may provide useful references for engineers and practitioners attempting to design pragmatic, low-cost but high-performance ultra high-injection speed controllers.     

Nonlinear air-to-fuel ratio and engine speed control for hybrid vehicles

Internal combustion spark ignition engine management systems regulate the fuel, spark, and idle air subsystems to achieve sufficient engine performance at acceptable fuel economy and tailpipe emission levels. Engine control units also monitor other engine processes, using a suite of sensors, and periodically check the system actuators' operation to satisfy legislated onboard diagnostics. The majority of production engines regulate the air-to-fuel ratio using a speed-density, or air-flow, control strategy. In this approach, the mass of air drawn into a given cylinder is calculated using the engine speed, manifold absolute pressure, and inlet air temperature. Based on the air mass, appropriate fuel amounts are injected to achieve stoichiometric operation. However, the wide range of operating conditions, inherent induction process nonlinearities, and gradual component degradations due to aging have prompted research into model-based algorithms. In this paper, a nonlinear model-based control strategy will be proposed for simultaneous air-to-fuel ratio control and speed tracking in hybrid electric vehicles. The motivation for engine speed management resides in the integrated control of the engine and a continuously variable transmission for increased efficiency. The proposed backstepping controller uses an observer to reduce the inputs to manifold air mass (e.g., manifold absolute pressure and inlet air temperature) and engine speed. The underlying engine model describes the air intake, fuel injection, and rotational dynamics. For comparison purposes, an existing multisurface sliding mode controller and an integrated speed-density air-to-fuel controller with attached engine speed regulation have been implemented. The performance of each controller is studied using an analytical engine model with representative numerical results presented and discussed to provide insight into the overall performances.     

注塑机温度控制方法分析与仿真

注塑机中料筒温度是注塑机工作过程中的重要被控参数。传统注塑机温度控制系统存在的超调量大、调节时间长等诸多不足,在分析多种控制算法,运用工程软件Matlab 7.0对模糊PID控制和常规PID控制进行仿真分析的基础上,提出了以模糊PID作为注塑机温度检测控制的控制算法,设计出模糊控制器,实现注塑机温度的实时控制,对于提高注塑机温度控制精度具有较好的效果。     

Genetic algorithm tuning of Lyapunov-based controllers: anapplication to a single-link flexible robot system

In this paper, a systematic controller design approach is proposed to guarantee both closed-loop stability and desired performance of the overall system by effectively combining genetic algorithms (GAs) with Lyapunov's direct-controller design method. The effectiveness of the approach is shown by using a simple and efficient decimal GA optimization procedure to tune and optimize the performance of a Lyapunov-based robust controller for a single-link flexible robot. The feedback gains of the controller are tuned by the GA optimization process to achieve good results for tip motion control of the single-link flexible robot based on some suitable fitness functions. The paper includes results of simulation experiments demonstrating the effectiveness of the proposed genetic algorithm approach     

Random Walk Guided Decap Embedding for Power/Ground Network Optimization

The reliability of power/ground networks is becoming significantly important in modern integrated circuits, while decap insertion is a main approach to enhance the power grid safety. In this brief, we propose a fast and efficient decap allocation algorithm, and adequately consider the leakage effect of decap. This approach borrows the idea of random walks to perform circuit partitioning and does subsequent decap insertion based on locality property of partitioned area, which avoids solving a large nonlinear programming problem in traditional decap optimization process. The optimization flow also integrates a refined leakage current model for decaps which makes it more practical. Experimental results show that our proposed method can achieve approximate 15 X speed up over the optimal budget method within the acceptable error tolerance. Also this algorithm only causes a few penalty area to compensate the leakage effect.     

Hybrid self-organizing fuzzy and radial basis-function neural-network controller for gas-assisted injection molding combination systems

This study proposed a self-organizing fuzzy controller (SOFC) to manipulate a gas-assisted injection molding combination system (GAIMCS) and determined the control performance of the system. However, both the learning rate and the weighting distribution of the SOFC are difficult to select and are fixed after selection. To address this problem, this study developed a hybrid self-organizing fuzzy and radial basis-function neural-network controller (HSFRBNC) for GAIMCSs. The HSFRBNC uses a radial basis-function neural-network to regulate the parameters of the SOFC for achieving appropriate values in real time. It not only overcomes the difficulty of finding appropriate parameters of the SOFC but also reduces the time needed to establish suitable fuzzy control rules for manipulating the GAIMCS. Experimental results showed that the HSFRBNC has better control performance than the SOFC in controlling the GAIMCS.     

Fuzzy Gain-Scheduling Proportional–Integral Control for Improving Engine Power and Speed Behavior in a Hybrid Electric Vehicle

With the increased emphasis on improving fuel economy and reducing emissions, hybrid electric vehicles (HEVs) have emerged as very strong candidates to achieve these goals. The power-split hybrid system, which is a complex hybrid powertrain, exhibits great potential to improve fuel economy by determining the most efficient regions for engine operation and thereby high-voltage (HV) battery operation to achieve overall vehicle efficiency optimization. To control and maintain the actual HV battery power, a sophisticated control system is essential, which controls engine power and thereby engine speed to achieve the desired HV battery maintenance power. Conventional approaches use proportional-integral (PI) control systems to control the actual HV battery power in power-split HEV, which can sometimes result in either overshoots of engine speed and power or degraded response and settling times due to the nonlinearity of the power-split hybrid system. We have developed a novel approach to intelligently controlling engine power and speed behavior in a power-split HEV using the fuzzy control paradigm for better performances. To the best of our knowledge, this is the first reported use of the fuzzy control method to control engine power and speed of a power-split HEV in the applied automotive field. Our approach uses fuzzy gain scheduling to determine appropriate gains for the PI controller based on the system's operating conditions. The improvements include elimination of the overshoots as well as approximate 50% faster response and settling times in comparison with the conventional linear PI control approach. The improved performances are demonstrated through simulations and field experiments using a ford escape hybrid vehicle.