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In this study, the authors solve the problem of uniform global asymptotic stabilisation and global exponential tracking for an underactuated ship with only two propellers. A unified backstepping design methodology is proposed to tackle both the stabilisation and tracking problems. The obvious advantage of this framework is that the controller design procedure is systematic and analytically simple. The study also addresses the tracking problem with constant bias of environmental disturbances. Simulation results are provided to validate our theoretical results. [ABSTRACT FROM AUTHOR]

The number of wheelchair users has increased at an alarming rate throughout the last decade due to the increased number of individuals with upper/lower extremity dysfunctions resulting from a stroke, spinal cord injury, trauma, accident, or workplace injuries. Wheelchair-mounted assistive robots have been playing an important role in assisting the activities of daily living (ADLs) of these individuals. This research focuses on the design optimization of wheelchair-mounted assisted robots for essential ADLs. The genetic algorithm (GA) was used to estimate the link length parameters of 5 degrees of freedom (DoFs), 6DoFs, and 7DoFs wheelchair-mounted robots to cover the seven workspaces commonly used in ADLs. The objective function used in the GA includes singularity avoidance, collision avoidance, and ADL coverage. The results show that the proposed optimized 5DoFs robot can cover 25%, 6 DoFs robot can cover 69%, and 7 DoFs robot can cover 71% of essential ADL workspaces.

The number of individuals with upper or lower extremities dysfunction (ULED) has considerably increased in the past few decades, resulting in a high economic burden for their families and society. Individuals with ULEDs require assistive robots to fulfill all their activities of daily living (ADLs). However, a theory for the optimal design of assistive robots that reduces energy consumption while increasing the workspace is unavailable. Thus, this research presents an algorithm for the optimal link length selection of an assistive robot mounted on a wheelchair to minimize the torque demands of each joint while increasing the workspace coverage. For this purpose, this research developed a workspace to satisfy a list of 18 ADLs. Then, three torque indices from the literature were considered as performance measures to minimize; the three torque measures are the quadratic average torque (QAT), the weighted root square mean (WRMS), and the absolute sum of torques (AST). The proposed algorithm evaluates any of the three torque measures within the workspace, given the robot dimensions. This proposed algorithm acts as an objective function, which is optimized using a genetic algorithm for each torque measure. The results show that all tree torque measures are suitable criteria for assistance robot optimization. However, each torque measures yield different optimal results; in the case of the QAT optimization, it produces the least workspace with the minimum overall torques of all the joints. Contrarily, the WRMS and AST optimization yield similar results generating the maximum workspace coverage but with a greater overall torque of all joints. Thus, the selection between the three methods depends on the designer's criteria. Based on the results, the presented methodology is a reliable tool for the optimal dimensioning of assistive robots.

Within Industry 4.0, drones appear as intelligent devices that have brought a new range of innovative applications to the industrial sector. The required knowledge and skills to manage and appropriate these technological devices are not being developed in most universities. This paper presents an unmanned aerial vehicle (UAV)-based smart educational mechatronics system that makes use of a motion capture (MoCap) laboratory and hardware-in-the-loop (HIL) to teach UAV knowledge and skills, within the Educational Mechatronics Conceptual Framework (EMCF). The macro-process learning construction of the EMCF includes concrete, graphic, and abstract levels. The system comprises a DJI Phantom 4, a MoCap laboratory giving the drone location, a Simulink drone model, and an embedded system for performing the HIL simulation. The smart educational mechatronics system strengthens the assimilation of the UAV waypoint navigation concept and the capacity for drone flight since it permits the validation of the physical drone model and testing of the trajectory tracking control. Moreover, it opens up a new range of possibilities in terms of knowledge construction through best practices, activities, and tasks, enriching the university courses.