Amjad J. Humaidi

Ibraheem Kasim Ibraheem, Amjad J. Humaidi

In this paper, a novel finite - time Nonlinear Extended State Observer (NLESO) is proposed and employed in Active Disturbance Rejection Control (ADRC) to stabilize a nonlinear system against system's uncertainties and discontinuous disturbances using output feedback technique. The first task was to aggregate the uncertainties, disturbances, and any other undesired nonlinearities in the system into a single term called the "generalized disturbance". Consequently, the ESO estimates the generalized disturbance and cancel it from the input channel in an online fashion. A peaking phenomenon that existed in Linear ESO (LESO) has been reduced significantly by adopting a saturation - like nonlinear function in the proposed Nonlinear ESO (NLESO). Stability analysis of the NLEO is studied using finite - time Lyapunov theory, and the comparisons are presented over simulations on Permanent Magnet DC (PMDC) motor to confirm the effectiveness of the proposed observer concerning LESO.

Fatin H. Ajeil, Ibraheem Kasim Ibraheem, Mouayad A. Sahib et al.

The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed algorithm consists of three modules. The first module forms an optimized path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance and follows path smoothness criteria. The second module detects any infeasible points generated by the proposed hybrid PSO-MFB Algorithm by a novel Local Search (LS) algorithm integrated with the hybrid PSO-MFB algorithm to be converted into feasible solutions. The third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. The simulation results indicate that this method generates an optimal feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Moreover, compared to recent path planning techniques, simulation results show that the proposed hybrid PSO-MFB algorithm is highly competitive in terms of path optimality.