Mass Game Simulator: An Entertainment Application of Multiagent Control

Multiagent control system (MACS) has become a promising solution for solving complex control problems. Using the advantages of MACS-based design approaches, a novel solution for advanced control of mechatronic systems has been developed in this paper. The study has aimed at integrating learning control into MACS. Specifically, learning feedforward control (LFFC) is implemented as a pattern for incorporation in MACS. The major novelty of this work is that the feedback control part is realized in a real-time periodic MACS, while the LFFC algorithm is done on-line, asynchronously, and in a separate non-real-time aperiodic MACS. As a result, a MACS-based LFFC design method has been developed. A second-order B-spline neural network (BSN) is used as a function approximator for LFFC whose input-output mapping can be adapted during control and is intended to become equal to the inverse model of the plant. To provide real-time features for the MACS-based LFFC system, the open robot control software (OROCOS) has been employed as development and runtime environment. A case study using a simulated linear motor in the presence of nonlinear cogging and friction force as well as mass variations is used to illustrate the proposed method. A MACS-based LFFC system has been designed and implemented for the simulated plant. The system consists of a setpoint generator, a feedback controller, and a time-index LFFC that can learn on-line. Simulation results have demonstrated the applicability of the design method.

Download Full-text

Distributed Multiagent Control Approach for Multitarget Tracking

Mathematical Problems in Engineering ◽

10.1155/2015/903682 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10

Author(s):

Liang Ma ◽

Kai Xue ◽

Ping Wang

Keyword(s):

Information Fusion ◽

Multitarget Tracking ◽

Control Approach ◽

Numerical Studies ◽

Utility Optimization ◽

Partially Observed ◽

Markov Decision ◽

Multiagent Control ◽

Bernoulli Filter ◽

Effectiveness And Efficiency

In multiagent systems, tracking multiple targets is challenging for two reasons: firstly, it is nontrivial to dynamically deploy networked agents of different types for utility optimization; secondly, information fusion for multitarget tracking is difficult in the presence of uncertainties, such as data association, noise, and clutter. In this paper, we present a novel control approach in distributed manner for multitarget tracking. The control problem is modelled as a partially observed Markov decision process, which is a NP-hard combinatorial optimization problem, by seeking all possible combinations of control commands. To solve this problem efficiently, we assume that the measurement of each agent is independent of other agents’ behavior and provide a suboptimal multiagent control solution by maximizing the local Rényi divergence. In addition, we also provide the SMC implementation of the sequential multi-Bernoulli filter so that each agent can utilize the measurements from neighbouring agents to perform information fusion for accurate multitarget tracking. Numerical studies validate the effectiveness and efficiency of our multiagent control approach for multitarget tracking.

Download Full-text