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Go to Editorial ManagerMobile robots use simultaneous localization and mapping (SLAM) techniques for generating maps of unknown environments through navigating its. In this work, firstly SLAM technique was considered based on extended Kalman filter (EKF) which it was implemented and evaluated at unknown environments with different number of landmarks to estimate mobile robot’s position and build a map for navigated environment at the same time. Then, the detectable landmarks will play an important role in controlling the overall navigation process as well EKF-SLAM technique’s performance. After that, three intelligent optimization algorithms are proposed to enhance the performance of the EKF-SLAM trajectory for the mobile robot, these algorithms are: particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO) and genetic optimization (GA). MATLAB simulation results show that CPSO algorithm outperforms PSO and GA algorithms in terms of minimizing the mean square error (MSE1) with increasing the number of landmarks, where MSE1 is the mean square error of EKF-SLAM according to the actual trajectory. The simulation results show also the performance of EKF-SLAM trajectory is better than the performance of the Odometry trajectory and becomes best with using intelligent optimization algorithms.
In this work, the design procedure of a hybrid robust controller for crane system is presented. The proposed hybrid controller combines the linear quadratic regulator (LQR) properties with the sliding mode control (SMC) to obtain an optimal and robust LQR/SMC controller. The crane system which is represented by pendulum and cart is used to verify the effectiveness of the proposed controller. The crane system is considered one of the highly nonlinear and uncertain systems in addition to the under-actuating properties. The parameters of the proposed LQR/SMC are selected using Particle Swarm Optimization (PSO) method. The results show that the proposed LQR/SMC controller can achieve a better performance if only SMC controller is used. The robustness of the proposed controller is examined by considering a variation in system parameters with applying an external disturbance input. Finally, the superiority of the proposed LQR/SMC controller over the SMC controller is shown in this work.