Cooperative control of multiple mobile robot using particle swarm optimization for tracking two passive target
This paper proposes a cooperative control method for multiple mobile robots to track two passive targets in unknown environment on the basis of a particle swarm optimization (PSO). By adjusting the left and right wheels speed of the mobile robot using the proposed control method, each mobile robot can move towards the target on the basis of information about the previous best location of each particle and its neighbors. The simulation results demonstrate the validity of the proposed cooperative control method of multiple mobile robots for tracking two passive targets.
Obstacle avoidance extremum seeking control based on constrained derivative-free optimization
A new control scheme based on extremum seeking control (ESC) which employs a constrained derivative-free optimization algorithm has been proposed in this paper. A theorem has been formulated to prove the convergence result of ESC based on constrained derivative-free optimization. Generalized pattern search method with filter algorithm for constraint is used to generate a sequence of ESC control state. Since generalized pattern search (GPS) method does not require continuously differentiable and Lipschitz conditions, noise cancellation algorithm is added to the proposed ESC algorithm which is then used for multi-agent robot system. The obstacles are expressed as constraint functions instead of the traditional way of calculating the performance function of obstacles. Simulation results illustrate a multi-agent obstacle avoidance system which utilized the control algorithm to avoid obstacles that appear on the path of multi-agent robots. Based on the simulation results, it can be observed that multi-agents maintain their formation as per initial condition and follow the target without colliding into obstacles while navigating in a noisy environment. Performance comparison of the proposed algorithm with a reference algorithm shows the efficiency of the proposed algorithm.
Self-adaptive memetic algorithm: an adaptive conjugate gradient approach
Combining hill climbing methods that search for the optimum points in a bounded region of search space with genetic algorithm is effective in the cases that the search space or optimization problem is complicated. However, parameter setting of selected HC can influence the performance of the algorithm significantly. In this paper a run-time self-adaptation strategy is utilized to discover the most appropriate HC parameters for the problem at hand. The HC method is used in this work is conjugate gradient that is an efficient gradient based hill climber for a wide range of problems. Traditionally, key parameters of the conjugate gradient are tuned by some deterministic or predetermined adaptive rules. But in our self-adaptation approach these parameters are encoded in genotypes and coevolved alongside the solutions and adjusted based on regional or generational conditions of individuals in the evolution process. Another advantage of this individualistic approach is that it puts forth different hill climbing capabilities to each individual and this prevents undesirable convergence of solutions to a local optimum that is a side effect of ordinary memetic algorithm. This proposed method not only adds no extra computation load to the genetic algorithm but also eliminates computation burden of parameter adjustment of hill climbing operator. Results of applying this approach on several test functions are demonstrated to illustrate improvements achieved using our self adaptive memetic algorithm in comparison with ordinary memetic algorithm.