System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs.

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.

Virtual Reality-Based Framework to Simulate Control Algorithms for Robotic Assistance and Rehabilitation Tasks through a Standing Wheelchair.

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.

Three-Dimensional Unified Motion Control of a Robotic Standing Wheelchair for Rehabilitation Purposes.

Technological advances in recent years have shown interest in the development of robots in the medical field. The integration of robotic systems in areas of assistance and rehabilitation improves the user's quality of life. In this context, this article presents a proposal for the unified control of a robotic standing wheelchair. Considering primary and secondary tasks as control objectives, the system performs tasks autonomously and the change of position and orientation can be performed at any time. The development of the control scheme was divided in two parts: (i) kinematic controller to solve the desired motion problem; and (ii) dynamic compensation of the standing wheelchair-human system. The design of the two controllers considers the theory of linear algebra, proposing a low computational cost and an asymptotically stable algorithm, without disturbances. The stability and robustness analysis of the system is performed by analyzing the evolution of the control errors in each sampling period. Finally, real experiments of the performance of the developed controller are performed using a built and instrumented standing wheelchair.