This paper presents a MATLAB-based application to teach the guidance, navigation, and control concepts of a quadrotor to undergraduate students, using a graphical user interface (GUI) and 3-D animations. The Simulink quadrotor model is controlled by a proportional integral derivative controller and a linear quadratic regulator controller. The GUI layout's many components can be easily programmed to perform various experiments by considering the simulation of the quadrotor as a plant; it incorporates control systems (CS) fundamentals such as time domain response, transfer function and state-space form, pole-zero location, root locus, frequency domain response, steady-state error, position and disturbance response, controller design and tuning, unity, and the use of a Kalman filter as a feedback sensor. 3-D animations are used to display the quadrotor flying in any given condition selected by the user. For each simulation, users can view the output response in the form of 3-D animations, and can run time plots. The quadrotor educational tool (QET) helps students in the CS laboratory understand basic CS concepts. The QET was evaluated based on student feedback, grades, satisfaction, and interest in CS.

This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical simulation. Finally, simulation based experiments are performed to verify the technique. Results suggest that the proposed method can accurately work, in real-time under limited processing resources.

This paper presents a novel proximal planning and control (PPC) formulation for an unmanned ground vehicle (UGV) which is affected by skidding and slip disturbances. The control approach also considers the presence of moving and static obstacles in the context of operation. The PPC technique is divided into three parts; first, a nonlinear model predictive control (NMPC) based path-planner is designed to periodically generate an updated feasible trajectory for reaching the goal pose, under the constraint of avoiding collisions with dynamic and static objects which are present in the context. In particular, a proximal averaged Newton-type method for optimal control (PANOC) is used to implement NMPC. Second, the velocity commands are produced via evolutionary programming (EP) based on kinematic control (KC). Third, a dynamic control process with an extended state observer (ESO) is introduced to estimate disturbances whose magnitudes are unknown but bounded. Finally, to verify the performance of the proposed scheme, simulations are performed for the platform operating in the presence of static obstacles (SO) and moving obstacles (MO), whose trajectories may be nonlinear and difficult to be accurately predicted. Additionally, we have investigated and confirmed that the proposed PPC is able to operate in real time under limited CPU processing resources.

This paper presents a nonlinear model predictive control (NMPC) scheme for the medium-level control of a small-scale autonomous bulldozer to accurately and safely displace crushed materials of masonry in construction contexts. For this purpose, the controller is required to minimize the error between the achieved and required paths; additionally, the control actions must be smooth for minimizing the mistreatment of equipment, which is intended to operate in the long term, as it is usual in industrial-grade solutions. The proposed NMPC based path-follower can adequately handle the platform’s constraints and usual perturbations. In terms of state estimation, a map-based localizer is implemented via an extended Kalman filter (EKF) based on the platform nominal process model and light detecting and ranging (LiDAR) and inertial measurement unit (IMU) sensors measurements. The localizer provides estimates of the platform’s pose, necessary for the MPC controller’s state feedback. An actual experiment on a modified UGV (Clearpaths Husky A-200) is performed to validate the performance of the proposed control scheme. The UGV is retrofitted with a blade (for pushing material) and appropriate sensors for the necessary perception tasks. Experimental results indicate that the proposed control scheme is robust and suitable for safely pushing the crushed materials, presenting appropriately low deviation from the nominal path and requiring reasonably low processing time.