Environmental Adaptive Control of a Snake-like Robot With Variable Stiffness Actuators

This work investigates adaptive stiffness control and motion optimization of a snake-like robot with variable stiffness actuators. The robot can vary its stiffness by controlling magnetorheological fluid(MRF) around actuators. In order to improve the robot's physical stability in complex environments, this work proposes an adaptive stiffness control strategy. This strategy is also useful for the robot to avoid disturbing caused by emergency situations such as collisions. In addition, to obtain optimal stiffness and reduce energy consumption, both torques of actuators and stiffness of the MRF braker are considered and optimized by using an evolutionary optimization algorithm. Simulations and experiments are conducted to verify the proposed adaptive stiffness control and optimization methods for a variable stiffness snake-like robots.     

HPSO-based fuzzy neural network control for AUV

A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.     

Optimization management of hybrid energy source of fuel cell truck based on model predictive control using traffic light information

Energy optimization management can make fuel cell truck (FCT) power system more efficient, so as to improve vehicle fuel economy. When the structure of power source system and the torque distribution strategy are determined, the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources. The paper simulates the assistance of the intelligent transport system (ITS) and carries out the eco-velocity planning using the traffic signal light. On this basis, in order to further improve the energy efficiency of FCT, a model predictive control (MPC)-based energy source optimization management strategy is innovatively developed, which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption. Under the scenarios of signalized intersections, based on the planned eco-velocity, the off-line simulation results show that the proposed MPC-based energy source management strategy (ESMS) can reduce hydrogen consumption of fuel cell up to 7\% compared with the existing rule-based ESMS. Finally, the Hardware-in-the-Loop (HiL) simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.     

A modified consensus algorithm for multi-UAV formations based on pigeon-inspired optimization with a slow diving strategy

This paper considers the formation control problem for a group of unmanned aerial vehicles（UAVs） employing consensus with different optimizers.A group of UAVs can never accomplish difficult tasks without formation because if disordered they do not work any better than a single vehicle,and a single vehicle is limited by its undeveloped intelligence and insufficient load.Among the many formation methods,consensus has attracted much attention because of its effectiveness and simplicity.However,at the beginning of convergence,overshoot and oscillation are universal because of the limitation of communication and a lack of forecasting,which are inborn shortcomings of consensus.It is natural to modify this method with lots of optimizers.In order to reduce overshoot and smooth trajectories,this paper first adopted particle swarm optimization（PSO）,then pigeon-inspired optimization（PIO） to modify the consensus.PSO is a very popular optimizer,while PIO is a new method,both work but still retain disadvantages such as residual oscillation.As a result,it was necessary to modify PIO,and a pigeon-inspired optimization with a slow diving strategy（SD-PIO） is proposed.Convergence analysis was performed on the SD-PIO based on the Banach fixed-point theorem and conditions sufficient for stability were achieved.Finally,a series of comparative simulations were conducted to verify the feasibility and effectiveness of the proposed approach.     

Solving Strategy for Repetitious Simulation of Multi-domain Physical System Based on Modelica

Repetitious simulation after modifying parameters of multi-domain physical system based on Modelica often appears in model experiment and optimization design. At present, the solvers based on Modelica need calculate all the coupled blocks during every simulation run after updating parameters. Based on discussing scale decomposition methods of simulation model, subdivision solving strategy and minimum solving strategy are put forward to improve the efficiency of repetitious simulation, by which the numerical solution of the simulation model can be achieved by only calculating the solving sequence influenced by altered parameters. A simplified model of aircraft is used to demonstrate the efficiency of the strategies presented.     

A learning particle swarm optimization algorithm for odor source localization

This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.     

Grasping Force Optimization Algorithm of Soft Multi-fingered Hand Based on Safety Margin Detection

The classical gradient flow optimization algorithm requires a valid initial point before starting the recursive algorithm,and the existing methods can’t guarantee that the initial values fully satisfy the friction cone constraints of contact point in the optimization process of gradient flow algorithm.In order to improve safety margin and prevent the finger from slipping at contact point,we present an iterative method of safe initial values with safety margin detection and develop a gradient flow optimization algorithm based on the safe initial values.Firstly,the safety margin is defined which more intuitively reflects the margin of the grasping forces at contact point.The resulting safe initial values can be achieved by the detection of desired safety margin at each iteration.Secondly,the safe initial values are usually not optimal,even with the valid initial values,and it can’t always ensure that the finger contact force always satisfies the friction cone constraints during the optimization.It is an effective way to eliminate the unreliable initial values in the optimization and obtain a safer initial values by increasing the safety margin.By transforming the safe initial values into an initial point of the gradient flow algorithm,the final optimized values of grasping forces can be generated efficiently by gradient flow iteration.Grasp examples of the soft multi-fingered hand indicate the effectiveness of the general solution of the force optimization algorithm based on safety margin detection.The method eliminates the shortcomings of the gradient flow optimization process caused by the initial value problem and provides a more accurate and reliable force optimization result for multi-fingered dexterous manipulation.     

A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid

Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.     

On-board torquemanagement approach to the E-COSM benchmark problem with a prediction-based engine assignment

This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles (HEVs) on a road with a slope. We assume that HEVs are in a connected environment with real-time vehicle-to-everything information, including geographic information, vehicle-to-infrastructure information and vehicle-to-vehicle information. The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time. The proposed strategy includes multiple rules and model predictive control (MPC). The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode. To improve fuel economy, the optimal energy management strategy is primarily considered, and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints, a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon. Therefore, this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed. To validate the proposed optimization strategy, a powertrain control simulation platform in a traffic-in-the-loop environment is constructed, and case study results performed on the constructed platform are reported and discussed.     

Seam tracking and visual control for robotic arc welding based on structured light stereovision

A real-time arc welding robot visual control system based on a local network with a multi-level hierarchy is developed in this paper. It consists of an intelligence and human-machine interface level, a motion planning level, a motion control level and a servo control level. The last three levels form a local real-time open robot controller, which realizes motion planning and motion control of a robot. A camera calibration method based on the relative movement of the end-effector connected to a robot is proposed and a method for tracking weld seam based on the structured light stereovision is provided. Combining the parameters of the cameras and laser plane, three groups of position values in Cartesian space are obtained for each feature point in a stripe projected on the weld seam. The accurate three-dimensional position of the edge points in the weld seam can be calculated from the obtained parameters with an information fusion algorithm. By calculating the weld seam parameter from position and image data, the movement parameters of the robot used for tracking can be determined. A swing welding experiment of type Ⅴgroove weld is successfully conducted, the results of which show that the system has high resolution seam tracking in real-time, and works stably and efficiently.     

Adaptive visual servoing for the robot manipulator with extreme learning machine and reinforcement learning

In this study, a novel image-based visual servo (IBVS) controller for robot manipulators is investigated using an optimized extreme learning machine (ELM) algorithm and an offline reinforcement learning (RL) algorithm. First of all, the classical IBVS method and its difficulties in accurately estimating the image interaction matrix and avoiding the singularity of pseudo-inverse are introduced. Subsequently, an IBVS method based on ELM and RL is proposed to solve the problem of the singularity of the pseudo-inverse solution and tune adaptive servo gain, improving the servo efficiency and stability. Specifically, the ELM algorithm optimized by particle swarm optimization (PSO) was used to approximate the pseudo-inverse of the image interaction matrix to reduce the influence of camera calibration errors. Then, the RL algorithm was adopted to tune the adaptive visual servo gain in continuous space and improve the convergence speed. Finally, comparative simulation experiments on a 6-DOF robot manipulator were conducted to verify the effectiveness of the proposed IBVS controller.     

优化复杂函数的粒子群-鸽群混合优化算法

针对复杂函数优化问题,提出一种两阶段混合优化算法。对基本粒子群和鸽群算法进行改进,引入惯性因子和跳跃算子增强了粒子群算法的搜索能力,提出干扰算子增加了鸽群算法的种群多样性。将改进后的两种算法相结合,形成两阶段混合优化算法,同时定义了一种多样性函数对种群进行实时监测,以保证种群的多样性。采用两组经典测试函数,对算法性能进行测试。结果表明,算法适用于求解复杂函数优化问题,且具有较好的收敛速度和收敛精度。     

基于DCS的电力节能优化控制系统的设计与实现

为了解决电力系统的节能优化问题,本文在传统的PSO节能控制方法的基础上,提出了一种多重自适应的粒子群优化算法,应用分散控制系统设计与实现了一种新的电力节能优化控制系统。数值仿真的结果说明了使用所提出的粒子群算法的基于DCS的电力节能优化控制系统在电力调度最佳节点的搜索精确度要高于相同条件下的一般的电力控制系统。使用所提算法的电力节能优化控制系统,能有效地对电力能耗进行优化,且具有较高的实用性。     

基于WFPSO算法的云虚拟机放置策略

随着云计算技术的大规模应用,云应用的交互更加依赖于网络,较差网络拓扑的选择,增加了应用在网络中的通信流量,严重影响应用的运行效率和服务质量。为解决此问题,提出了一种基于粒子群优化算法的虚拟机放置策略。该策略通过建立云环境内部时延模型,利用改进的粒子群优化算法求解目标函数,来降低应用的时延,提高运行效率。并在CloudSim平台上进行仿真实验,实验结果表明,该策略的响应时间低于基本粒子群优化算法（PSO）,并且修改后的PSO算法在不影响收敛精度的前提下较大幅度地提高粒子群算法的收敛速度,提高了云环境中应用的运行效率。     

异构网络中基于鸽群优化算法的D2D资源分配机制

针对异构蜂窝网络中D2D(device-to-device)通信用户复用蜂窝用户上行信道产生的频谱资源分配优化问题,提出一种基于改进离散鸽群算法(PIO)的D2D通信资源分配机制.通过设置信干噪比(SINR)门限值保证用户的通信服务质量(QoS),使用基于改进地图-指南针算子和认知因子的离散鸽群算法(IMCBPIO)为D2D用户进行资源分配,并采用基于接收SINR的闭环功率控制算法动态调整用户的发送功率,以减少用户与基站以及用户与用户之间存在的干扰.仿真结果表明,所提出方案能够有效抑制异构网络中由于引入D2D用户后导致的干扰,降低通信用户的中断概率,大大提高频谱利用率和系统吞吐量.     

基于HSDPA混合群集智能算法的多用户调度

传统的基于粒子群最优化的混合启发式算法和模拟退火算法往往以牺牲解的质量或者求解速度来实现有效的调度,为了解决这一问题,提出了一种基于高速下行分组接入（HSDPA）标准的混合群集智能算法。首先假定HSDPA标准所指定的是现实性不完善的信道状态信息（CSI）反馈,并以有限集合的形式存在于信道指示符（CQI）中;接着在最优化过程中,利用模拟退火算法和粒子群最优化算法各自的优点设计混合群集智能算法;最后利用混合算法进行数据处理,得到最优解的同时降低了复杂度,从而实现提升系统通量,达到调度最优化的目的。实验结果表明,与传统的基于粒子群最优化的算法相比,所提的混合算法取得了更好的调度效果。     

粒子群改进算法在SBR曝气过程控制中的应用

SBR工艺是污水处理广泛采用的方法,针对曝气过程难于控制,探讨了粒子群改进算法在过程参数优化控制中的应用。文中剖析了曝气过程的控制论特性、控制难点,研究了控制策略,探讨了改进的PSO算法,基于仿人智能控制,构造了优化控制算法。借助仿真实验,对曝气过程控制作了对比研究,过程响应验证了基于粒子群改进控制算法的良好控制品质。仿真实验结果表明,基于粒子群改进控制算法在曝气过程控制中的应用是可行、合理与有效的。     

四足机器人抗重心偏移步态优化

为解决四足机器人在其质心偏离躯干几何中心时的稳定性问题,提出了一种基于改进粒子群算法的优化方法.使用基于Hopf模型的振荡器搭建中枢模式发生器(central pattern generator,CPG)网络拓扑结构,通过对足端进行轨迹规划进而确定CPG模型相关参数,并对CPG单元间的耦合系数矩阵进行优化,使其能够输出...     

一种基于教与学的混合灰狼优化算法

针对灰狼优化算法(GWO)存在收敛精度不高、易陷入局部最优的不足,提出一种基于教与学的混合灰狼优化算法(HGWO).首先,采用佳点集理论进行种群初始化,提高初始种群的遍历性;其次,提出一种非线性控制参数策略,在迭代前期增加全局搜索能力,避免算法陷入局部最优,在迭代后期增加局部开发能力,提高收敛精度;最后,结合教与学算法(TLBO)和粒子群优化算法,修改原位置更新公式以优化算法搜索方式,从而提升算法的收敛性能.为验证HGWO算法的有效性,选取9种标准测试函数,将HGWO算法、GWO算法以及其他群体智能优化算法和其他改进GWO算法进行仿真实验.实验结果表明,所提出的HGWO算法性能优于GWO算法和其他群体智能优化算法,且在改进算法中具有一定优势.