基于改进遗传算法立体车库存取调度优化

研究了巷道堆垛式立体车库的结构及运行原理,其具有自动化程度高、搬运器运行无空行程、存取方便快捷等优点,但也有自身的不足之处.针对巷道堆垛类立体车库的车辆出入口相对较少造成的用户的平均等待时间较长、堆垛机的工作负荷较大等缺点,从调度策略的角度进行分析,利用改进遗传算法对立体车库进行调度策略的优化,建立了以总存取时间为目标函数的数学模型,采用混合编码,改良的OX交叉算子对车库存取序列进行优化,以使总的存取车时间最少,即用户的平均等待时间最少.经过仿真实验的验证该算法得到了较好的收敛,并得到较小的存取车总时间,获得了预期的车辆存取序列,在与其他调度优化方法比较时得出结论,该种优化方法所得结果较为实用.     

异构平台下卷积神经网络推理模型自适应划分和调度方法

针对卷积神经网络（CNN）在异构平台执行推理时存在硬件资源利用率低、延迟高等问题，提出一种CNN推理模型自适应划分和调度方法。首先，通过遍历计算图提取CNN的关键算子完成模型的自适应划分，增强调度策略灵活性；然后，基于性能实测与关键路径-贪婪搜索算法，在CPU-GPU异构平台上根据子模型运行特征选取最优运行负载，提高子模型推理速度；最后利用张量虚拟机（TVM）中跨设备调度机制，配置子模型的依赖关系与运行负载，实现模型推理的自适应调度，降低设备间通信延迟。实验结果表明，与TVM算子优化方法在GPU和CPU上的推理速度相比，所提方法在模型推理准确度无损前提下，推理速度提升了5.88%～19.05%和45.45%～311.46%。     

多目标等量分批柔性作业车间调度集成优化方法

针对多目标等量分批柔性作业车间调度问题,提出了一种集成优化方法。构建了一种以完工时间最短、生产成本最低为优化目标的多目标等量分批柔性调度集成优化模型。提出并设计了一种改进的非支配排序遗传算法对模型加以求解。算法中引入面向对象技术处理复杂的实体逻辑关系,采用三段式分段编码技术分别对分批方案、加工顺序、设备进行编码,采用三段式分段交叉和变异的混合遗传算子实现遗传进化,采用两种精细化调度技术进行解码以缩短流程时间。通过案例分析验证了所提方法的有效性。     

基于遗传算法的车间调度问题

基于遗传算法对车问调度问题进行了优化，给出了自适应的遗传算子，进行了遗传算法参数设置。采用这种编码方案、遗传算子和参数使得遗传操作大大简化，能达到有效的调度作用。最后，使用标准实例对算法有效性收敛速度进行了验证，并作了简要分析。     

批量生产柔性作业车间多目标精细化调度方法

提出了一种批量生产柔性作业车间多目标精细化调度方法。针对批量生产柔性作业车间多目标调度问题特点,建立了一类以完工时间最短和制造成本最低为优化目标的等量分批柔性作业车间调度多目标优化模型。提出了5种批量生产柔性作业车间精细化调度技术;设计了一种改进的NSGA II算法对模型进行求解。算法中引入面向对象技术处理复杂的实体逻辑关系,使用矩阵编码技术进行编码,采用分段交叉和分段变异的遗传算子实现遗传进化,应用上述5种精细化调度技术于解码过程以提高设备利用率。通过案例分析验证了该方法的有效性。     

基于遗传算法的电力系统VAR优化调度研究

电力系统无功功率(VAR)优化对电力系统的经济运行具有重要意义，文章在分析不同初始种群生成方法对GA性能的影响后，改进GA求解电力系统VAR优化调度问题。在VAR优化调度模型的基础上，构建了基于GA的VAR优化算法。利用GA对决策变量进行实数编码，使用二进制竞标赛策略选择子代个体，结合模拟二进制交叉(SBX)和多项式变异方案作为交叉算子和变异算子。评估了带借位减法指令(SWB)、Niderreiter准随机抽样(NQR)、非对齐系统(NAS)和高斯采样(GS)4种初始种群生成方法。通过对IEEE-30总线系统进行实验，结果表明：采用均匀抽样的SWB伪随机发生器生成初始种群的GA,在电力系统VAR优化调度计算得到的有功功率损耗最低，仅为4.00%。     

多星地面站设备优化调度方法研究

通过对各种动态调度方法进行分析、对比比较，提出将扩展Petri网与启发式调度规则相结合来建立多星调度模型并实现多星地面设备优化调度的方法，为解决多星地面站设备调度问题提供了新的途径。     

公交车和驾驶员集成调度算法研究

为解决公交车及驾驶员调度优化问题,本文给出了一种基于免疫计算的调度优化方案。设计了公交车和驾驶员调度问题的数学模型,给出了非劣邻域支配的多目标免疫优化算法的框架、基于实数编码的比例克隆算子和领域变异算子以及支配抗体的拥挤距离公式,并在仿真环境下进行了实验。实验结果表明,本文算法能有效地解决公交车及驾驶员调度优化问题,具有较好的应用价值。     

基于成本的车间调度模型及遗传算法求解

制造企业的成本优化，无论在学术界还是工业界，始终是一个热点研究课题。从成本优化思想出发，建立了基于成本的多产品切换作业车间调度问题的经济指标模型，并应用具有新型交叉、变异算子的遗传算法作为求解该调度问题的方法，其目的是使企业的综合生产成本（主要包括多产品切换成本、产品作为在制品的存储成本以及产品拖期惩罚等）最低。仿真实例的运行结果表明了该调度模型和优化算法的有效性，且适用于过程工业及离散工业的生产调度系统，实现生产调度和经济效益的有效集成。     

Pareto遗传算法在货位配置中的应用研究

固定货架是自动化立体仓库应用最广泛的存储设备，货位配置是否优化直接影响货架的稳定性和存取操作效率。因此，建立了货位配置优化问题的数学模型，提出了采用Pareto遗传算法解决多目标组合优化问题，可得到Pareto最优解集。此算法包括5个基本算子：选择、变异、交叉、小生境技术、Pareto集合过滤器。通过仿真实验验证了将Pareto遗传算法应用在实际货位配置优化问题中，取得了较好的结果。     

On distributed constrained formation control in operator–vehicle adversarial networks

We consider an operator–vehicle network where each vehicle is remotely maneuvered by an operator. The objective of the operators is to steer the vehicles to the desired formation subject to the given state and input constraints. Each operator–vehicle pair is attacked by an adversary who is able to maliciously replay the control commands sent from the operator. To play against attackers, we come up with a novel distributed resilient algorithm based on the receding-horizon control methodology, and show that the algorithm is able to allow vehicles, on the one hand, satisfy state and input constraints, and on the other hand, asymptotically achieve the desired formation despite replay attacks. With slight modifications, our proposed algorithm shows an analogous resilience to denial-of-service attacks.     

Control of multivehicle systems in the presence of uncertain dynamics

In this paper, we present a cooperative control architecture for high-order multivehicle systems having non-identical nonlinear uncertain dynamics. The proposed methodology consists of a local cooperative controller and a vehicle-level controller for each vehicle. The former controller receives the relative output measurements of the neighbouring vehicles in order to solve a containment problem formulated on a leader–follower framework. Specifically, the leaders generate trajectories in which the vehicles (followers) converge to the convex hull formed by those of the leaders. For a special case with one leader, this controller synchronises the output of the vehicles with the output of the leader. The latter controller receives the internal-state measurements for suppressing the nonlinear uncertain dynamics of the vehicle by using a decentralised adaptive control approach. The interaction topology between vehicles is described by undirected graphs and extensions to directed graphs are further discussed. The stability and convergence properties of the proposed cooperative control architecture are analysed by using the results from linear algebra and the Lyapunov theory. Several numerical examples are provided to demonstrate the efficacy of the proposed cooperative control architecture.     

Fault-tolerant traction control of electric vehicles

This paper investigates a new traction control approach that requires neither chassis velocity nor information about tire-road conditions. Plant fault subject to the uncertainties of the mathematical model and slightly sensor fault are concerned. For general traction control of vehicles, the variation of model behavior may break down the steering stability if the chassis velocity is not monitored. This paper presents a fault-tolerant approach based on the maximum transmissible torque estimation (MTTE) scheme which has the ability to prevent electric vehicles from skidding. A PI-type disturbance observer is employed to enhance the steering stability of the MTTE approach. This proposed approach does not require both the differentiator and the inversion of the controlled plant. Finally, illustrated examples are given for evaluating the fault-tolerant performance and feasibility of the presented anti-slip strategy.     

Adaptive Nonsingular Terminal Sliding Mode Control Design for Near Space Hypersonic Vehicles

This paper presents an adaptive nonsingular terminal sliding mode approach for the attitude control of near space hypersonic vehicles (NSHV) in the presence of parameter uncertainties and external disturbances. Firstly, a novel nonsingular terminal sliding surface is developed and its finitetime convergence is analyzed. Then, an adaptive nonsingular terminal sliding mode control law is proposed, which is chattering free. In the proposed approach, all parameter uncertainties and external disturbances are lumped into one term, which is estimated by an adaptive uncertainty estimation for eliminating the boundary requirement needed in the conventional control design. Subsequently, stability of the closed-loop system is proven based on Lyapunov theory. Finally, the proposed approach is applied to the attitude control design for NSHV. Simulation results show that the proposed approach attains a satisfactory performance in the presence of parameter uncertainties and external disturbances.      

Delta 算子不确定系统的滑模变结构控制

研究算子系统滑模变结构控制的综合问题.首先,利用线性矩阵不等式方法给出了切换面存在的充分条件,分析了算子变结构控制系统的到达条件,将连续系统和离散系统统一到算子系统.基于指数趋近律,给出滑模变结构控制一般方法.其次,给出一类算子不确定系统的滑模控制器设计,分析了准滑动模态的渐近稳定性,使得系统具有良好的动态性能.最后,用一个仿真实例说明在采样周期取值非常小的前提下,该方法设计的滑模变结构控制仍具有可行性和有效性.     

A real-time impedance-based singularity and joint-limits avoidance approach for manual guidance of industrial robots

This paper studies real-time manual guidance considering singularity and joint-limits avoidance using impedance control in an industrial scenario. The operator is responsible for keeping the end-effector (EE) away from the robot’s singularity and joint-limits. The proposed approach detects the singularity and joint-limits in real-time. Then, virtual stiffness and damping are added to target stiffness and damping as the robot is getting close to the singularity or joint-limit. A criterion is presented for detection of singularity by combining manipulability ellipsoid and condition number. Also a new joint to Cartesian space transformation is formulated in order to convert joint stiffness and damping to Cartesian stiffness and damping for joint-limits avoidance method. The presented approach is applied on a SCARA robot. An experiment is performed in this paper to investigate singularity and joint-limits avoidance separately as well as together. Increase in stiffness and damping warn the operator of the possibility of singularity or joint-limits allowing the operator to changes the EE path. The proposed approach, eliminates the need for a robotics expert by allowing any operator with no knowledge about robot singularity and joint-limits to interact and teach the robot in a safe, real-time and time-saving manner.     

Time-varying formation control of a collaborative heterogeneous multi agent system

In this paper, coherent formation control of a multi-agent system in the presence of time-varying formation is studied. For special application of rescue and surveillance, a set of agents, consisting of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) are considered. Due to different degrees of freedom of the UAVs and the UGVs, the collaboration between the agents confronts many problems. A Lyapunov based controller is presented to stabilize the swarming and lead the system to a rigid formation using decentralized control approach. In the proposed control signal of each agent, a signal of the neighbors’ error is considered to cope with variation in performance and to provide synchronization, which means that the state error of the agents converges to zero nearly at the same time. The decentralized approach provides reliability of the performance in unknown environment, since the controller of each agent is designed based on local knowledge. This algorithm is evaluated in simulation and the results approve the accepted performance of the proposed approach.     

Electric vehicles charging control in a smart grid: A model predictive control approach

The paper presents an event driven model predictive control (MPC) framework for managing charging operations of electric vehicles (EV) in a smart grid. The objective is to minimize the cost of energy consumption, while respecting EV drivers' preferences, technical bounds on the control action (in compliance with the IEC 61851 standard) and both market and grid constraints (by seeking the tracking of a reference load profile defined by the grid operator). The proposed control approach allows “flexible” EV users to participate in demand side management (DSM) programs, which will play a crucial role in improving stability and efficiency of future smart grids. Further, the natural MPC formulation of the problem can be recast into a mixed integer linear programming problem, suitable for implementation on a calculator. Simulation results are provided and discussed in detail.     

基于观测器的水下机器人神经网络稳定自适应控制

给出了基于观测器的水下机器人神经网络自适应控制算法．控制算法由3部分组成：输出反馈控制、神经网络以及滑模项，其中输出反馈控制为了保证系统的初始稳定性；神经网络用于逼近系统的非线性动力学；滑模项用于补偿和抑制系统的外部扰动、神经网络逼近误差等．控制算法中所需要的速度量由状态观测器来提供．基于Lyapunov稳定理论给出了系统闭环稳定条件和稳定域．水池试验结果验证了算法的有效性．     

Robust RHC for wheeled vehicles with bounded disturbances

The robust receding horizon control (RHC) synthesis approach is developed in this paper, for the simultaneous tracking and regulation problem (STRP) of wheeled vehicles with bounded disturbances. Considering the bounded disturbances, we firstly provide a robust positively invariant (RPI) set and associated feedback controller for the perturbed vehicles, which contribute to the foundation of the robust RHC synthesis approach. Then, by extending the tube‐based approach introduced in the article of Mayne et al (robust model predictive control of constrained linear systems with bounded disturbances in Automatica, 2005, vol. 41) to the STRP of wheeled vehicles, we employ the designed RPI set to determine the robust tube and terminal state region, and further construct a nominal optimal control problem. The actual control input is implemented by correcting the solved nominal input with the designed feedback controller. Following the contributed properties of the developed RPI set and extended tube‐based approach, a robust RHC algorithm is finally proposed with the guarantees of recursive feasibility and robust convergence, which can also be adapted for real‐time implementation. Additionally, due to the elaborate control design, the effect of disturbances can be completely nullified to achieve better tracking performance. The effectiveness and advantage of the proposed approach are illustrated by two simulation examples.