This paper proposes a cooperative coevolutionary approach to multi-robot formation control. To deal with the formation control problem, the concept of a cooperative coevo-lution (CC) framework is incorporated with model predictive control (MPC) such that candidates of all robots coevolve toward a Nash equilibrium in a distributed way. Using the Nash-equilibrium strategy, the robots can quickly move to a desired formation from their initial locations. The stability is guaranteed via a novel repair algorithm that enforces each candidate to satisfy a derived condition for asymptotic stability. The cooperative coevolutionary particle swarm optimization (CCPSO) is adopted and modified to fit into the formation control problem. Simulations are performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the CC-based MPC. Also, the proposed MPC shows a better performance compared to sequential quadratic programming (SQP)-based MPC.

In this paper, a novel cooperative coevolution (CC)-based model predictive control (MPC) is proposed to deal with formation control problem. While conventional evolutionary algorithm (EA)-based MPC approaches cannot guarantee the stability, the proposed CC-based MPC guarantees the asymptotic stability regardless of the optimality of the solution that the CC-based algorithm generates with a small number of individuals. To guarantee the stability, a terminal state constraint is found, and then a repair algorithm is applied to all candidate solutions to meet the constraint. The cooperatively coevolving particle swarm optimization (CCPSO), most recently developed algorithm among CC-based EAs, is adopted and modified to fit into the formation control problem. Experiments are performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the CC-based MPC.

This paper presents a novel interval type-2 fuzzy logic control architecture for flocking system when the system has noisy sensor measurements. The traditional type-1 fuzzy logic controller (FLC) using precise type-1 fuzzy sets cannot fully model and handle the uncertainties of sensor data. However, type-2 FLC using type-2 fuzzy sets with a footprint of uncertainty (FOU) produces better performances under noisy environments. In this paper, therefore, we present a reactive control architecture for flocking algorithm that is based on interval type 2 FLC to implement the flocking behaviors consisting separation, obstacle avoidance, and velocity matching behaviors. The type-2 based control system could cope with the uncertainties of noisy sensor measurements and resulted in good performances that outperformed the type-1 FLC.