论文标题
通过模型预测控制的V形成
V-Formation via Model Predictive Control
论文作者
论文摘要
我们提出了最新的结果，该结果证明了将一群鸟类视为模型预测控制（MPC）的V形成问题的力量。可以从合成连续空间和连续时间马尔可夫决策过程（MDP）的最佳计划（MDP）的问题的问题来理解V型MPC婚姻，该计划是达到最小化给定成本功能的目标状态。首先，我们考虑ARE，这是一种用于生成最佳计划（动作序列）的近似算法，该计划将MDP的初始状态采用到其成本低于指定（收敛）阈值的状态。 ARES使用粒子群优化，对后退的地平线和粒子群都具有自适应尺寸。受重要性分裂的启发，选择了地平线的长度和粒子数量，以使至少一个粒子达到下一个级别状态。可以将ARE视为使用自适应后退视野，又称自适应MPC（AMPC）的模型预测控制（MPC）算法。接下来，我们现在分布式AMPC（DAMPC），这是与当地社区一起使用的AMPC的分布式版本。我们介绍了自适应邻域大小的调整，从而由基于成本的Lyapunov功能确定了在全球系统状态下进行评估的。我们的实验表明，DAMPC在每个时间步骤中仅使用局部信息和分布式共识形式的同时，DAMPC的性能几乎和集中式AMPC一样。最后，受到对网络物理系统的安全攻击的启发，我们介绍了控制器攻击游戏（CAG），其中两个玩家，一个控制者和一个攻击者具有对立的目标。我们制定了一种称为V-Formation Games的CAG的特殊情况，攻击者的目标是防止控制器获得V型。我们演示了控制器设计中的适应性如何有助于克服某些攻击。
We present recent results that demonstrate the power of viewing the problem of V-formation in a flock of birds as one of Model Predictive Control (MPC). The V-formation-MPC marriage can be understood in terms of the problem of synthesizing an optimal plan for a continuous-space and continuous-time Markov decision process (MDP), where the goal is to reach a target state that minimizes a given cost function. First, we consider ARES, an approximation algorithm for generating optimal plans (action sequences) that take an initial state of an MDP to a state whose cost is below a specified (convergence) threshold. ARES uses Particle Swarm Optimization, with adaptive sizing for both the receding horizon and the particle swarm. Inspired by Importance Splitting, the length of the horizon and the number of particles are chosen such that at least one particle reaches a next-level state. ARES can alternatively be viewed as a model-predictive control (MPC) algorithm that utilizes an adaptive receding horizon, aka Adaptive MPC (AMPC). We next present Distributed AMPC (DAMPC), a distributed version of AMPC that works with local neighborhoods. We introduce adaptive neighborhood resizing, whereby the neighborhood size is determined by the cost-based Lyapunov function evaluated over a global system state. Our experiments show that DAMPC can perform almost as well as centralized AMPC, while using only local information and a form of distributed consensus in each time step. Finally, inspired by security attacks on cyber-physical systems, we introduce controller-attacker games (CAG), where two players, a controller and an attacker, have antagonistic objectives. We formulate a special case of CAG called V-formation games, where the attacker's goal is to prevent the controller from attaining V-formation. We demonstrate how adaptation in the design of the controller helps in overcoming certain attacks.
