Adaptive Fuzzy Reinforcement Learning for Flock Motion Control - Qu, Shuzheng (Université d'Ottawa / University of Ottawa, 2022)
The flock-guidance problem enjoys a challenging structure where multiple optimization objectives are solved simultaneously. This usually necessitates different control approaches to tackle various objectives, such as guidance, collision avoidance, and cohesion. The guidance schemes, in particular, have long suffered from complex tracking-error dynamics. Furthermore, techniques that are based on linear feedback or output feedback strategies obtained at equilibrium conditions either may not hold or degrade when applied to uncertain dynamic environments. Relying on potential functions, embedded within pre-tuned fuzzy inference architectures, lacks robustness under dynamic disturbances. This thesis introduces two adaptive distributed approaches for the autonomous control of multi-agent systems. The first proposed technique has its structure based on an online fuzzy reinforcement learning Value Iteration scheme which is precise and flexible. This distributed adaptive control system simultaneously targets a number of flocking objectives; namely: 1) tracking the leader, 2) keeping a safe distance from the neighboring agents, and 3) reaching a velocity consensus among the agents. In addition to its resilience in the face of dynamic disturbances, the algorithm does not require more than the agent’s position as a feedback signal. The effectiveness of the proposed method is validated with two simulation scenarios and benchmarked against a similar technique from the literature. The second technique is in the form of an online fuzzy recursive least squares-based Policy Iteration control scheme, which employs a recursive least squares algorithm to estimate the weights in the leader tracking subsystem, as a substitute for the original reinforcement learning actor-critic scheme adopted in the first technique. The recursive least squares algorithm demonstrates a faster approximation weight convergence. The time-invariant communication graph utilized in the fuzzy reinforcement learning method is also improved with time-varying graphs, which can smoothly guide the agents to reach a speed consensus. The fuzzy recursive least squares-based technique is simulated with a few scenarios and benchmarked against the fuzzy reinforcement learning method. The scenarios are simulated in CoppeliaSim for a better visualization and more realistic results.
Keywords: reinforcement; multi-agent; value iteration; policy iteration