A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Genetic Algorithm

This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module, system records the data sequences of flighttests as inputs (control signals for servos) and outputs (aircraft’s attitude and velocity information).After data preprocessing, thesystem constructs the horizontal and vertical dynamic model for the small unmanned aerial rotorcraft using adaptive geneticalgorithm.The identified model is verified by a series of simulations and tests.Comparison between flight data and the one-stepprediction data obtained from the identification model shows that the dynamic model has a good estimation for real unmannedaerial rotorcraft system.Based on the proposed dynamic model, the small unmanned aerial rotorcraft can perform hovering,turning, and straight flight tasks in real flight tests.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

ADAPTIVE GENETIC ALGORITHM BASED ON SIX FUZZY LOGIC CONTROLLERS

The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.

Data⁃Based Feedback Relearning Algorithm for Robust Control of SGCMG Gimbal Servo System with Multi⁃source Disturbance

Single gimbal control moment gyroscope(SGCMG)with high precision and fast response is an important attitude control system for high precision docking,rapid maneuvering navigation and guidance system in the aerospace field.In this paper,considering the influence of multi-source disturbance,a data-based feedback relearning(FR)algorithm is designed for the robust control of SGCMG gimbal servo system.Based on adaptive dynamic programming and least-square principle,the FR algorithm is used to obtain the servo control strategy by collecting the online operation data of SGCMG system.This is a model-free learning strategy in which no prior knowledge of the SGCMG model is required.Then,combining the reinforcement learning mechanism,the servo control strategy is interacted with system dynamic of SGCMG.The adaptive evaluation and improvement of servo control strategy against the multi-source disturbance are realized.Meanwhile,a data redistribution method based on experience replay is designed to reduce data correlation to improve algorithm stability and data utilization efficiency.Finally,by comparing with other methods on the simulation model of SGCMG,the effectiveness of the proposed servo control strategy is verified.

An improved self-calibration approach based on adaptive genetic algorithm for position-based visual servo

An improved self-calibrating algorithm for visual servo based on adaptive genetic algorithm is proposed in this paper. Our approach introduces an extension of Mendonca-Cipolla and G. Chesi＇s self-calibration for the positionbased visual servo technique which exploits the singular value property of the essential matrix. Specifically, a suitable dynamic online cost function is generated according to the property of the three singular values. The visual servo process is carried out simultaneous to the dynamic self-calibration, and then the cost function is minimized using the adaptive genetic algorithm instead of the gradient descent method in G. Chesi＇s approach. Moreover, this method overcomes the limitation that the initial parameters must be selected close to the true value, which is not constant in many cases. It is not necessary to know exactly the camera intrinsic parameters when using our approach, instead, coarse coding bounds of the five parameters are enough for the algorithm, which can be done once and for all off-line. Besides, this algorithm does not require knowledge of the 3D model of the object. Simulation experiments are carried out and the results demonstrate that the proposed approach provides a fast convergence speed and robustness against unpredictable perturbations of camera parameters, and it is an effective and efficient visual servo algorithm.

Optimal Tracking Control for a Class of Unknown Discrete-time Systems with Actuator Saturation via Data-based ADP Algorithm

为有致动器浸透和未知动力学的分离时间的系统的一个班的一个新奇最佳的追踪控制方法在这份报纸被建议。计划基于反复的适应动态编程(自动数据处理) 算法。以便实现控制计划，一个 data-based 标识符首先为未知系统动力学被构造。由介绍 M 网络，稳定的控制的明确的公式被完成。以便消除致动器浸透的效果， nonquadratic 表演功能被介绍，然后一个反复的自动数据处理算法被建立与集中分析完成最佳的追踪控制解决方案。为实现最佳的控制方法，神经网络被用来建立 data-based 标识符，计算性能索引功能，近似最佳的控制政策并且分别地解决稳定的控制。模拟例子被提供验证介绍最佳的追踪的控制计划的有效性。

A New Fuzzy Adaptive Genetic Algorithm

Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution. A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.

Dynamically Reconfigurable Encryption System of the AES

Reconfigurable computing has grown to become an important and large field of research, it offers advantages over traditional hardware and software implementations of computational algorithms. The Advanced Encryption Standard （AES） algorithm is widely applied in government department and commerce. This paper analyzed the AES algorithms with different cipher keys, adopted a novel key scheduler that generated the round key real-time, proposed a dynamically reconfigurable encryption system which supported the AES algorithm with different cipher keys, and designed the architecture of the reconfigurable system. The dynamically reconfigurable AES system had been realized on FPGA. The result proves that the reconfigurable AES system is flexible, lower cost and high security level.

Dynamic self-adaptive ANP algorithm and its application to electric field simulation of aluminum reduction cell

Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.

多场景下基于传感器的行为识别

针对基于传感器的行为识别任务中识别场景单一且固定的问题,提出一种多场景下基于传感器的行为识别迁移模型,由基于传感器的动态感知算法(dynamic perception algorithm,DPA)和自适应场景的行为识别迁移方法(adaptive scene human recognition,AHR)两部分组成,解决在固定场景下对传感器的依赖性以及在场景转换时识别模型失效的问题。DPA提出两阶段迁移模式,将行为识别阶段和模型迁移阶段同步推进,保证模型在传感器异动发生后仍能持续拥有识别能力。进一步提出AHR场景迁移方法,实现模型在多场景下的行为识别能力。实验验证该模型具有更优的适应性和可扩展性。

基于自适应动态粒子群优化的RAK-SVD方法

K均值奇异值分解(K-SVD)算法是一种行之有效的地震资料去噪方法,但由于其稀疏分解存在不确定性,需要引入正则项对其改进。为此,在常规粒子群算法的基础上,提出了一种自适应动态粒子群算法优化正则化参数的正则化近似K-SVD(RAK-SVD)去噪方法。首先通过修改字典原子和相关参数,解决了由于常规粒子群算法的惯性参数固定不变,导致后期搜索效率下降的问题;其次将正则化系数引入近似K-SVD(AK-SVD)方法,明显提升了去噪效果;最后利用自适应动态粒子群算法自动优选AK-SVD方法中的正则化参数,提高了稀疏分解的确定性,在对强反射信号进行去噪的同时加强了对弱信号的保护。模型测试和实际应用均表明,该方法有利于弱信号的提取和识别,不仅能够显著改善弱地震信号的去噪效果,还提升了计算效率。该方法具有一定的实际应用价值。

基于AGPF的目标定位精度改善方法

针对传统遗传算法粒子滤波容易因遗传操作参数恒定不变而陷入局部最优的问题,在遗传算法粒子滤波中引入自适应方法,提出自适应遗传算法粒子滤波。根据粒子适应度的大小,动态调节遗传操作的交叉、突变概率,从而在尽可能多地保留优势粒子的同时更有效地产生新的优势粒子,跳出局部最优。将自适应遗传算法粒子滤波应用于动态目标定位模型,并将其与遗传算法粒子滤波的性能进行比较。结果表明,自适应方法的引入可以增加算法有效粒子数,有效解决算法早熟问题,改善滤波精度,对于提高动态目标定位精度是有效的。

基于新型细菌觅食优化算法的飞机动态泊位问题

随着航空运输业的发展,传统手动设计泊位方案已难以满足日益增长的外包维修需求.在外包模式下,如何快速给出高效的动态泊位方案关系到维修任务订单的准点交付,是飞机维修服务公司亟待解决的重要问题.针对飞机泊位进出顺序及碰撞检测特点,构建带时间窗的飞机维修泊位模型.设计自适应趋化学习及交叉协作策略,提出新型细菌觅食优化算法,并设计一系列约束处理机制.研究结果表明,提出的基于矩形碰撞检测方法可有效预防并判断飞机间碰撞阻塞情况.新型细菌觅食优化算法在解决飞机动态泊位问题上展现出搜索精度高、稳定性强等特点.所得高效智能化泊位调度方案有助于在保证维修安全的情况下提升飞机维修服务提供商的维修服务效率,改进维修资源利用率与维修系统的柔性,为企业实现高质量发展打下良好基础.

基于自适应ST算法的机器人轨迹跟踪研究

为实现更精确地机器人轨迹跟踪控制,提高系统的抗干扰能力,研究提出基于自适应搜索和追踪算法的机器人轨迹跟踪方法,先建立机器人模型和跟踪轨迹,再采用支持实时动态参数调整的自适应ST算法。实验表明,三种不同的控制方法均能够使水下机器人达到所需的位置和姿态。其中,指令滤波控制方法表现出更快的平均收敛时间,仅为1.3秒。结果表明,研究所提方法能实现更短的调节时间,减小轨迹跟踪误差,同时保持稳定性和抗干扰性。

多策略融合改进的自适应蜉蝣算法

为改进蜉蝣算法全局搜索能力较差、种群多样性较小和自适应能力弱等问题,提出一种多策略融合改进的自适应蜉蝣算法(MIMA)。采用Sin混沌映射初始化蜉蝣种群,使种群能够均匀分布在解空间中,提高初始种群质量,增强全局搜索能力;引入Tent混沌映射和高斯变异对种群个体进行调节,增加种群多样性的同时调控种群密度,增强局部最优逃逸能力;引入不完全伽马函数,重构自适应动态调节的重力系数,建立全局搜索和局部开发能力之间更好的平衡,进而提升算法收敛精度,有利于提高全局搜索能力;采用随机反向学习(ROBL)策略,增强全局搜索能力,提高收敛速度并增强稳定性。利用经典测试函数集进行算法对比,并利用Wilcoxon秩和检验分析算法的优化效果,证明改进的有效性和可靠性。实验结果表明:所提算法与其他算法相比,寻优精度、收敛速度、稳定性都取得了较大提升。

融合改进哈里斯鹰和改进动态窗口的机器人动态路径规划

针对动态环境的移动机器人路径规划问题,提出了一种改进哈里斯鹰算法(IHHO)与改进动态窗口算法(IDWA)的融合算法(IHHO-IDWA)。首先,针对哈里斯鹰算法后期搜索性能不足等问题,提出了融合自适应混沌和核心种群动态划分策略、融合黄金正弦策略以及动态云最优解扰动策略来提高算法的性能。其次,针对动态窗口算法存在规划的路径长和易陷入死锁等问题,提出了三个改进策略:增加子函数,保证算法能够规划出更短的路径;提出自适应权重策略,平衡算法局部避障能力和全局搜索性能;设定初始航向角,避免路径冗余。最后,通过测试函数、CEC2014函数的数值实验和静态、动态路径规划实验,验证了IHHO和IDWA性能有明显提升;通过50×50大型动态地图验证了融合算法较对照组算法规划的路径缩短了11.51%,证明了该方法的优越性。

基于多策略改进的蜜獾优化算法

针对蜜獾算法(Honey Badger Algorithm,HBA)在解决复杂优化问题时存在收敛速度慢,容易陷入局部最优的问题,提出了一种基于多策略改进的蜜獾优化算法(IHBA).首先,引入Sobol序列初始化种群,增加种群的多样性;其次,引入动态自适应密度因子和黄金正弦策略,快速切割解空间,平衡全局搜索与局部搜索,提升寻优效率;然后,引入柯西变异策略,优化最优解,避免算法陷入局部最优.为验证算法的性能,选取7种智能算法与IHBA对比,对10个经典测试函数进行寻优对比和Wilcoxon秩和检验,仿真结果表明IHBA算法的收敛速度和寻优精度都得到了提升;最后,将IHBA用于求解2个实际的工程优化问题,仿真结果表明IHBA在解决工程优化问题时具有较好的鲁棒性和实用性.

高速列车纵向动力学建模与自适应RBFNN控制

高速列车由多节车厢链接而成的结构特性导致其高速运行在变路况线路条件下难以有效地对其进行优化控制。针对上述问题,提出一种高速列车纵向动力学模型与径向基函数神经网络(RBFNN)控制策略。考虑列车车钩力和复杂线路条件,分析整列车前后的不同受力情况,建立列车纵向动力学模型。针对该模型无外加干扰时设计一种理想反馈控制律,引入RBFNN对理想控制输出进行拟合,在考虑干扰项影响的情况下,通过设计参数估计自适应律代替神经网络权值的调整,并对其进行Lyapunov稳定性证明。采用京石武高铁北京西—郑州东段的CRH380B型高速列车真实线路运行数据进行仿真模拟,并在相同条件下与反演滑模(BSSM)控制器的仿真结果进行对比。仿真结果表明所提控制器更能有效应对复杂路况变化和外界干扰,对高速列车具有更好的控制效果,改善其运行的平稳性及高效性。

面向动态公交的离散分层记忆粒子群优化算法

随着智慧城市、智慧交通的发展,移动互联网和公交智能基础设施以及相关数据的不断完善,通过用户手机预约公交服务的新型公交运营方式——动态公交,已经成为许多城市公交发展的重要探索方向。但目前,对动态公交问题的建模、算法研究不足。基于这一研究现状,提出动态公交问题模型和面向动态公交的离散分层记忆粒子群优化(PSO)算法。首先给出动态公交问题的目标函数和约束条件,给出动态公交问题的解的形式,并定义解的编辑距离;其次提出使用数据驱动的预计算路径集生成PSO算法的优质初始解的方法,给出基于解的编辑距离的PSO算法中粒子的变异概率和自适应收敛系数的计算方式;最后提出将粒子群分层求解的方法,其中低层粒子群可复用、可继承,从而减少单时间片内、时间片间复制和重初始化带来的性能损耗。基于重庆市北碚区蔡家岗街道的真实场景和亿级历史数据建立仿真环境进行实验,实验结果表明:相对于不分层PSO算法,分层PSO算法通过复用和继承能缩短超80%计算用时;自适应参数和变异机制能帮助算法更稳定地收敛到更优解;相对于传统公交系统,动态公交能在同等运力限制下,提高22%的乘客接单率,节省39.1%的乘客出行时间,所提算法能满足公交运营商在片区内进行动态公交调度的需求;相对于对比算法,所提算法平均缩短了85.3%的计算用时,并且在仅耗用80%里程的情况下提高了至少12%的接单率。

复杂环境下多无人机协同目标跟踪路径规划

为解决多无人机在复杂环境下协同目标跟踪的路径规划问题,提出基于模型预测控制(model predictive control,MPC)和先进自适应化学反应优化(advanced adaptive chemical reaction optimization,AACRO)算法相结合的方法。基于目标跟踪模型,使用集中式MPC作为路径规划问题的实时控制框架,设计5个指标成本函数在多种约束条件下优化跟踪性能,获取无人机的最优跟踪路径;针对上述多维问题的复杂程度,使用一种新型智能算法解算MPC控制策略。仿真结果表明:该方案具备有效性和可行性,对无人机群协同目标跟踪具有重要的应用价值。