The implementation of control algorithms oriented to robotic assistance and rehabilitation tasks for people with motor disabilities has been of increasing interest in recent years. However, practical implementation cannot be carried out unless one has the real robotic system availability. To overcome this drawback, this article presents the development of an interactive virtual reality (VR)-based framework that allows one to simulate the execution of rehabilitation tasks and robotic assistance through a robotic standing wheelchair. The virtual environment developed considers the kinematic and dynamic model of the standing human–wheelchair system with a displaced center of mass, since it can be displaced for different reasons, e.g.,: bad posture, limb amputations, obesity, etc. The standing wheelchair autonomous control scheme has been implemented through the Full Simulation (FS) and Hardware in the Loop (HIL) techniques. Finally, the performance of the virtual control schemes has been shown by means of several experiments based on robotic assistance and rehabilitation for people with motor disabilities.

Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller.

Transport, rescue, search, surveillance, and disaster relief tasks are some applications that can be developed with unmanned aerial vehicles (UAVs), where accurate trajectory tracking is a crucial property to operate in a cluttered environment or under uncertainties. However, this is challenging due to high nonlinear dynamics, system constraints, and uncertainties presented in cluttered environments. Hence, uncertainties in the form of unmodeled dynamics, aerodynamic effects, and external disturbances such as wind can produce unstable feedback control schemes, introducing significant positional tracking errors. This work presents a detailed comparative study between controllers such as nonlinear model predictive control (NMPC) and non-predictive baseline feedback controllers, with particular attention to tracking accuracy and computational efficiency. The development of the non-predictive feedback controller schemes was divided into inverse differential kinematics and inverse dynamic compensation of the aerial vehicle. The design of the two controllers uses the mathematical model of UAV and nonlinear control theory, guaranteeing a low computational cost and an asymptotically stable algorithm. The NMPC formulation was developed considering system constraints, where the simplified dynamic model was included; additionally, the boundaries in control actions and a candidate Lyapunov function guarantees the stability of the control structure. Finally, this work uses the commercial simulator DJI brand and DJI Matrice 100 UAV in real-world experiments, where the NMPC shows a reduction in tracking error, indicating the advantages of this formulation.

The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on the capture and detailed analysis of aerial images (photogrammetry). However, the photogrammetry approach presents limitations, such as an increased amount of useless data and potential issues related to image resolution that negatively impact the detection process during high-altitude flights. In this work, we develop a visual servoing control system with dynamic compensation using nonlinear model predictive control (NMPC) applied to a UAV. This system is capable of accurately tracking the middle of the underlying PV array at various frontal velocities and height constraints, ensuring the acquisition of detailed images during low-altitude flights. The visual servoing controller is based on extracting features using RGB-D images and employing a Kalman filter to estimate the edges of the PV arrays. Furthermore, this work demonstrates the proposal in both simulated and real-world environments using the commercial aerial vehicle (DJI Matrice 100), with the purpose of showcasing the results of the architecture. Our approach is available to the scientific community in: https://github.com/EPVelasco/VisualServoing_NMPC.

This article proposes an enhancement to estimate unmodeled dynamics within the simplified dynamic model of a quadcopter by integrating three key methodologies: Nonlinear Model Predictive Control (NMPC), a Momentum Observer Dynamics (MOD), and an adaptive control law. Termed as Adaptive NMPC with MOD, this integrated approach leverages NMPC, implemented using the CasADi framework, for real-time decision-making, while the momentum observer facilitates system state estimation and uncertainty mitigation. Simultaneously, the adaptive control law adjusts parameters to estimate errors in unmodeled dynamics. Through digital twin and Model in Loop (MiL) simulations, the effectiveness of this framework is demonstrated. Specifically, the study focuses on the simplified quadcopter model, acknowledging often overlooked inherent dynamics resulting from the simplification by not considering the nonlinearities induced by the drone’s attitude angles. Addressing these unmodeled dynamics is critical, and the Adaptive NMPC with MOD method emerges as a robust solution, showcasing its potential across various scenarios.