18,615 research outputs found
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach
Optimal control of nonlinear partially-unknown systems with unsymmetrical input constraints and its applications to the optimal UAV circumnavigation problem
Aimed at solving the optimal control problem for nonlinear systems with
unsymmetrical input constraints, we present an online adaptive approach for
partially unknown control systems/dynamics. The designed algorithm converges
online to the optimal control solution without the knowledge of the internal
system dynamics. The optimality of the obtained control policy and the
stability for the closed-loop dynamic optimality are proved theoretically. The
proposed method greatly relaxes the assumption on the form of the internal
dynamics and input constraints in previous works. Besides, the control design
framework proposed in this paper offers a new approach to solve the optimal
circumnavigation problem involving a moving target for a fixed-wing unmanned
aerial vehicle (UAV). The control performance of our method is compared with
that of the existing circumnavigation control law in a numerical simulation and
the simulation results validate the effectiveness of our algorithm
Model-Free -Policy Iteration Based on Damped Newton Method for Nonlinear Continuous-Time H Tracking Control
This paper presents a {\delta}-PI algorithm which is based on damped Newton
method for the H{\infty} tracking control problem of unknown continuous-time
nonlinear system. A discounted performance function and an augmented system are
used to get the tracking Hamilton-Jacobi-Isaac (HJI) equation. Tracking HJI
equation is a nonlinear partial differential equation, traditional
reinforcement learning methods for solving the tracking HJI equation are mostly
based on the Newton method, which usually only satisfies local convergence and
needs a good initial guess. Based upon the damped Newton iteration operator
equation, a generalized tracking Bellman equation is derived firstly. The
{\delta}-PI algorithm can seek the optimal solution of the tracking HJI
equation by iteratively solving the generalized tracking Bellman equation.
On-policy learning and off-policy learning {\delta}-PI reinforcement learning
methods are provided, respectively. Off-policy version {\delta}-PI algorithm is
a model-free algorithm which can be performed without making use of a priori
knowledge of the system dynamics. NN-based implementation scheme for the
off-policy {\delta}-PI algorithms is shown. The suitability of the model-free
{\delta}-PI algorithm is illustrated with a nonlinear system simulation.Comment: 10 pages, 8 figure
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