Abstract
This paper considers the model-free optimal consensus problem of networked Euler-Lagrange systems without velocity measurements. By employing the position information, a novel neural network-based velocity observer is built for each agent to estimate the unmeasurable velocity vector and unknown system model. Based on the estimated velocity information, we propose distributed optimal control policies depended on the solutions to the coupled Hamilton-Jacobi-Bellman (HJB) equations. Then, a model-free policy iteration (PI) algorithm is provided to learn the coupled HJB equations online. To implement the PI algorithm, the critic-action neural networks are built and their weights are updated based on the gradient descent method. The uniform ultimate boundedness of the integrated observer estimation errors, the integrated consensus errors, and the weight estimation errors for the observer-critic-action neural networks is demonstrated by the Lyapunov technique. Finally, the numerical simulation on a directed network with six nonlinear manipulators is presented to validate the theoretical results.
Highlights
Distributed control of networked Euler-Lagrange systems (NELSs) has attracted lots of attention due to Euler-Lagrange equations are usually utilized to describe the dynamical behaviors of many practical physical systems, such as robotic manipulators, power electronic systems and actuated autonomous vehicles
We address the distributed optimal consensus problem for NELSs without velocity measurements
adaptive dynamic programming (ADP)-BASED OPTIMAL CONSENSUS CONTROLLER DESIGN To synthesize the system performance, the optimal consensus problem of NELSs will be transformed into a distributed optimal regulation of second-order MASs
Summary
Distributed control of networked Euler-Lagrange systems (NELSs) has attracted lots of attention due to Euler-Lagrange equations are usually utilized to describe the dynamical behaviors of many practical physical systems, such as robotic manipulators, power electronic systems and actuated autonomous vehicles. 1) By using only the position information of each agent, we design a novel neural network-based observer to estimate the velocity information and the system dynamic model. Yet, the EulerLagrange systems can only obtain the position information To address this issue, an adaptive observer will be designed for each agent to estimate the unknown velocity information and the unknown system model. ADP-BASED OPTIMAL CONSENSUS CONTROLLER DESIGN To synthesize the system performance, the optimal consensus problem of NELSs will be transformed into a distributed optimal regulation of second-order MASs . OPTIMAL CONSENSUS CONTROLLER DESIGN The local neighbor position and velocity consensus errors for agent i are defined as follows eqi =. It is difficult to find the solutions due to the nonlinear nature of the coupled HJB equations
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