一种基于零脱靶量的最优制导律设计

基于导弹和目标的三维相对运动关系,提出了一种三维非线性的最优制导律.在导弹和目标的三维相对运动方程的基础上,区别于以往的以视线角和视线角速率作为状态变量的方法,而采用以相对距离和相对速度作为状态变量的方法建立了一种新的状态方程,然后基于零脱靶量的思想,利用最优控制相关理论,设计了一种三维非线性的最优制导律.分别针对匀速运动的目标和大机动目标,用所设计的制导律和比例导引律分别进行了数学仿真,结果表明,所设计的最优制导律能有效地拦截机动目标,其性能优于比例导引律.     

自适应动态规划综述

自适应动态规划(Adaptive dynamic programming, ADP)是最优控制领域新兴起的一种近似最优方法, 是当前国际最优化领域的研究热点. ADP方法 利用函数近似结构来近似哈密顿--雅可比--贝尔曼(Hamilton-Jacobi-Bellman, HJB)方程的解, 采用离线迭代或者在线更新的方法, 来获得系统的近似最优控制策略, 从而能够有效地解决非线性系统的优化控制问题. 本文按照ADP的结构变化、算法的发展和应用三个方面介绍ADP方法. 对目前ADP方法的研究成果加以总结, 并对这 一研究领域仍需解决的问题和未来的发展方向作了进一步的展望.     

越肩发射空空导弹复合制导仿真研究

研究优化制导系统性能,越肩发射空空导弹的特点,要求转向准确攻击目标.为了优化制导系统,用最优控制和H∞控制理论设计全弹道复合制导规律.根据极小值原理,在推力矢量控制下设计了以在给定的时间内,使平行于初始视线方向上的速度分量达到最大,并使终端速度最大为指标的最佳快速转弯的初制导律.对末制导律是利用H∞鲁棒控制理论设计,对目标机动不作任何限制的鲁棒制导规律;然后用这两种制导律的加速度指令为参量构造了一个连续函数作为交接班的导引规律,实现弹道的平滑过渡,并对对全制导段弹道进行数字仿真,结果表明所设计的复合制导律为优化制导系统提供了参考.     

基于数据的自学习优化控制:研究进展与展望

自适应动态规划(Adaptive dynamic programming, ADP)方法可以解决传统动态规划中的"维数灾"问题, 已经成为控制理论和计算智能领域最新的研究热点. ADP方法采用函数近似结构来估计系统性能指标函数, 然后依据最优性原理来获得近优的控制策略. ADP是一种具有学习和优化能力的智能控制方法, 在求解复杂非线性系统的最优控制问题中具有极大的潜力. 本文对ADP的理论研究、算法实现、相关应用等方面进行了全面的梳理, 涵盖了最新的研究进展, 并对ADP的未来发展趋势进行了分析和展望.     

基于飞行时间估计带时间约束的末制导律设计

针对某些导弹要求限制末端攻击时间的作战要求,本文提出一种带末端攻击时间约束的新型制导律。所提出的制导律是通过线性最优控制方法的解而得到的,该制导律是比例导引律和攻击时间误差反馈的组合,此攻击时间误差有别于通过比例导引律所设定的攻击时间。本文用到飞行时间估算方法来完善所提出的制导律。所得制导律形式简单、实用。数字仿真结果证明所提出的制导律能够导引多枚导弹在期望的攻击时间同时命中固定目标。     

基于DDPG算法的末制导律设计研究

末制导律设计是拦截系统中的关键技术,常用的比例制导律及其变型在目标大机动时性能下降,且受到导航比的影响.提出基于DDPG算法的末制导律设计方法,通过对拦截问题的环境状态和动作(控制量)进行设计,实现了从仿真环境交互数据中学习回报最优的制导律;与传统方法相比,该无模型方法更具灵活性;针对强化学习方法动作集假设偏置弱带来训练效率低的问题,进一步提出将导航比作为决策优化参数,加速了训练过程并实现动态调整比例制导律中的导航比.对比实验表明,两种强化学习末制导律设计方法获得了优于比例制导律及其变型的拦截效果,展现出良好的研究前景和潜在的应用价值.     

带ε误差限的近似最优控制

近似动态规划方法求解非线性系统最优控制, 需要迭代无限步才能得到最优控制律. 本文提出了一种ε–近似最优控制算法, 选择ε误差限, 通过自适应迭代不断逼近哈密顿– 雅可比– 贝尔曼(HJB)方程的解, 应用神经网络实现在有限步迭代后得到带ε误差限的近似最优控制律. 计算机仿真结果表明了该算法的有效性.     

终端约束条件下末端制导律研究综述

现代战场环境的日益复杂化和激烈化,多约束条件下制导律的设计成为研究热点.本文首先介绍了末端制导律的研究背景和研究意义,概述了由于环境的复杂,目标的高机动性,及飞行器运动的非线性、强耦合性和多时变性性等因素带来制导律设计上的困难.然后对具有多终端约束条件下末端制导律的设计方法进行分类,将其分为单终端约束制导律和多终端约束制导律,并对具有终端角度、弹着时间、和终端速度的某一种或几种约束的制导律方法进行归纳总结.最后对下面4种末端制导律的发展趋势进行了展望:三维制导律、制导控制一体化、多飞行器协同制导和多模复合末制导.     

考虑攻击角度和视场角约束的自适应终端滑模制导律

针对导弹拦截机动目标的末制导问题,基于有限时间滑模控制理论设计一种带有攻击角度和导弹视场角约束的制导律.首先,将导弹末制导问题转化为带有状态约束的制导系统稳定问题,设计一种新型的非奇异终端滑模面和时变的障碍Lyapunov函数,给出一种终端滑模制导律的设计方法,并针对目标机动的不确定性设计一种对目标机动上界的自适应估计;然后,通过稳定性理论证明制导系统的状态变量最终是有限时间收敛的,并且结合时变的障碍Lyapunov函数和滑模面的设计特性证明在末制导过程中视场角约束条件始终不会被违背,相比于现有的考虑视场角约束的制导律,该制导律不存在指令转换,能够加快制导系统收敛速率,增强制导系统的抗干扰能力;最后,通过仿真实验验证所提出制导方法的有效性.     

考虑参数优化的BTT导弹三维非线性制导律

针对BTT（bank-to-turn）导弹制导过程中的通道耦合问题,设计了一种考虑制导参数优化的新型的三维非线性制导律.首先,采用旋量描述方法构建弹目视线方位模型,采用矢量描述方法构建弹目视线角速度模型,从而得到了导弹制导的三维非线性模型;然后,将制导律分为制导控制项和耦合补偿项.基于制导控制项最优设计相应的目标函数.同时,在不损失制导信息的情况下,将制导模型转化为线性形式;最后,分别针对无终端约束和有终端约束情况,基于二次型最优方法得到了三维制导律.该制导律既解决了通道解耦,其制导参数又满足一定物理意义下的最优性.仿真结果验证了本文所设计制导律的有效性.     

A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints

In this paper, a novel neural-network-based iterative adaptive dynamic programming (ADP) algorithm is proposed. It aims at solving the optimal control problem of a class of nonlinear discrete-time systems with control constraints. By introducing a generalized nonquadratic functional, the iterative ADP algorithm through globalized dual heuristic programming technique is developed to design optimal controller with convergence analysis. Three neural networks are constructed as parametric structures to facilitate the implementation of the iterative algorithm. They are used for approximating at each iteration the cost function, the optimal control law, and the controlled nonlinear discrete-time system, respectively. A simulation example is also provided to verify the effectiveness of the control scheme in solving the constrained optimal control problem.     

Exoatmospheric interception problem solved using output feedback law

Interception problems are often dealt with by separating guidance and autopilot design. Guidance law can be obtained using optimal control theory and autopilot design is performed on a linearized system. In this paper, we introduce a new approach that determines a global guidance and autopilot law, based on direct output feedback design. Application of this method to exoatmospheric interception problem results in good performances. Extension to endoatmospheric case is under investigation.     

Adaptive composite suboptimal control for linear singularly perturbed systems with unknown slow dynamics

In this article, using singular perturbation theory and adaptive dynamic programming (ADP) approach, an adaptive composite suboptimal control method is proposed for linear singularly perturbed systems (SPSs) with unknown slow dynamics. First, the system is decomposed into fast‐ and slow‐subsystems and the original optimal control problem is reduced to two subproblems in different time‐scales. Afterward, the fast subproblem is solved based on the known model of the fast‐subsystem and a fast optimal control law is designed by solving the algebraic Riccati equation corresponding to the fast‐subsystem. Then, the slow subproblem is reformulated by introducing a system transformation for the slow‐subsystem. An online learning algorithm is proposed to design a slow optimal control law by using the information of the original system state in the framework of ADP. As a result, the obtained fast and slow optimal control laws constitute the adaptive composite suboptimal control law for the original SPSs. Furthermore, convergence of the learning algorithm, suboptimality of the adaptive composite suboptimal control law and stability of the whole closed‐loop system are analyzed by singular perturbation theory. Finally, a numerical example is given to show the feasibility and effectiveness of the proposed methods.     

Real-time neural-network midcourse guidance

The approximation capability of artificial neural networks has been applied to the midcourse guidance problem to overcome the difficulty of deriving an on-board guidance algorithm based on optimal control theory. This approach is to train a neural network to approximate the optimal guidance law in feedback form using the optimal trajectories computed in advance. Then the trained network is suitable for real-time implementation as well as generating suboptimal commands. In this paper, the advancement of the neural-network approach to the current level from the design procedure to the three-dimensional flight is described.     

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.     

强化学习与自适应动态规划:从基础理论到多智能体系统中的应用进展综述

近年来,强化学习与自适应动态规划算法的迅猛发展及其在一系列挑战性问题(如大规模多智能体系统优化决策和最优协调控制问题)中的成功应用,使其逐渐成为人工智能、系统与控制和应用数学等领域的研究热点.鉴于此,首先简要介绍强化学习和自适应动态规划算法的基础知识和核心思想,在此基础上综述两类密切相关的算法在不同研究领域的发展历程,着重介绍其从应用于单个智能体(控制对象)序贯决策(最优控制)问题到多智能体系统序贯决策(最优协调控制)问题的发展脉络和研究进展.进一步,在简要介绍自适应动态规划算法的结构变化历程和由基于模型的离线规划到无模型的在线学习发展演进的基础上,综述自适应动态规划算法在多智能体系统最优协调控制问题中的研究进展.最后,给出多智能体强化学习算法和利用自适应动态规划求解多智能体系统最优协调控制问题研究中值得关注的一些挑战性课题.     

Target approach in biological systems

In certain applications such as prosthetics or manipulation in a hostile environment it is necessary to approach a selected target with an artificial arm. In this paper specific features of target approach in biological systems are outlined having in view the design of automata with similar performances. The theoretical basis for the design of servoarms is found in optimal control theory. A control law serving the above purpose is developed, the synthesis procedure for the optimal controller is presented as well as some experimental checks on a human operator.     

Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems

This paper proposes a novel finite-time optimal control method based on input–output data for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. In this method, the single-hidden layer feed-forward network (SLFN) with extreme learning machine (ELM) is used to construct the data-based identifier of the unknown system dynamics. Based on the data-based identifier, the finite-time optimal control method is established by ADP algorithm. Two other SLFNs with ELM are used in ADP method to facilitate the implementation of the iterative algorithm, which aim to approximate the performance index function and the optimal control law at each iteration, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme.