This work proposes an efficient and safe single-layer Nonlinear Model Predictive Control (NMPC) system based on LiDAR to solve the problem of autonomous navigation in cluttered environments with previously unidentified static and dynamic obstacles of any shape. Initially, LiDAR sensor data is collected. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, is used to cluster the (Lidar) points that belong to each obstacle together. Moreover, a Minimum Euclidean Distance (MED) between the robot and each obstacle with the aid of a safety margin is utilized to implement safety-critical obstacle avoidance rather than existing methods in the literature that depend on enclosing the obstacles with a circle or minimum bounding ellipse. After that, to impose avoidance constraints with feasibility guarantees and without compromising stability, an NMPC for set-point stabilization is taken into consideration with a design strategy based on terminal inequality and equality constraints. Consequently, numerous obstacles can be avoided at the same time efficiently and rapidly through unstructured environments with narrow corridors. Finally, a case study with an omnidirectional wheeled mobile robot (OWMR) is presented to assess the proposed NMPC formulation for set-point stabilization. Furthermore, the efficacy of the proposed system is tested by experiments in simulated scenarios using a robot simulator named CoppeliaSim in combination with MATLAB which utilizes the CasADi Toolbox, and Statistics and Machine Learning Toolbox. Two simulation scenarios are considered to show the performance of the proposed framework. The first scenario considers only static obstacles while the second scenario is more challenging and contains static and dynamic obstacles. In both scenarios, the OWMR successfully reached the target pose (1.5m, 1.5m, 0°) with a small deviation. Four performance indices are utilized to evaluate the set-point stabilization performance of the proposed control framework including the steady-state error in the posture vector which is less than 0.02 meters for position and 0.012 for orientation, and the integral of norm squared actual control inputs which is 19.96 and 21.74 for the first and second scenarios respectively. The proposed control framework shows a positive performance in a narrow-cluttered environment with unknown obstacles.

Magnetic Levitation System (MLS) objective is to levitate objects to the desired height without any contact. MLS is highly nonlinear and inherently unstable. Such a system imposes a challenge when designing robust and high-performance controllers. This paper presents the design of a Sliding Mode (SM) controller with an Integral term called SM-I controller to achieve the desired levitation against nonlinearities and uncertainties of the system. The controller parameters are tuned using the Equilibrium Optimizer (EO) algorithm. The Effectiveness of the proposed controller is validated by simulation results. Simulations are performed for servo tracking with and without perturbations in the MLS parameters. The proposed controller is compared with the conventional SM, LQR, and PID controllers to show its superiority. The results prove that the SM-I is more efficient than the other controllers.

This paper presents a robust nonlinear controller design for a magnetic levitation system (MLS). The feedback linearization method is utilized to transform the nonlinear model of MLS into the controller form. The controller is then designed and its parameters are optimized by the Salp Swarm Algorithm (SSA). Extensive MATLAB simulations are performed to evaluate the performance of the proposed controller as well as to compare it with PID and LQR controllers which are optimized by the SSA as well. Obtained results demonstrate that the proposed controller successfully tracks different kinds of reference signals (step, sine, and square) even in the presence of the system parameter perturbations and outperforms the PID and LQR controllers.