AC servomotors are widely used in industrial robot manipulators to drive high dynamic loads; thus, an appropriate selection and control of the motors contributes to a better performance at specific tasks. In this paper, a concurrent multi-objective dynamic optimization method is proposed for optimal selection and control of Synchronous AC servomotors. Three objective functions are optimized, energy consumption, tracking error and total weight of motors. Regarding the importance of the reducers to drive the manipulator, our methodology considers as an equality constraint the closed-loop dynamic model of the whole system, where the powertrain (motor - reducer - load) at each actuated-joint is considered. The multi-objective optimization problem is solved by using a genetic algorithm with continuous and discrete variables. The efficiency of the proposed methodology is validated via simulations of an industrial robot.

This paper aims to concurrently select and control off-the-shelf BLDC motors of industrial robots by using a synergistic model-based approach. The BLDC motors are considered with trapezoidal back-emf, where the three-phase (a,b,c) dynamics of motors are modeled in a mechatronic powertrain model of the robot for the selection and control problem, defining it as a multi-objective dynamic optimization problem with static and dynamic constraints. Since the mechanical and electrical actuators’ parameters modify the robot’s performance, the selection process considers the actuators’ parameters, their control input, operational limits, and the mechanical output to the transmission of the robot joints. Then, three objective functions are to be minimized, the motor’s energy consumption, the tracking error, and the total weight of installed motors on the robot mechanism. The control parameterization approach via a cascade controller with PI controllers for actuators’ voltage and a PID controller for actuators’ torque is used to solve the multi-objective dynamic optimization problem. Based on simulations of the closed-loop system, a Pareto front is obtained to examine trade-offs among the objective functions before implementing any actuators in the existing robotic system. The proposed method is tested on an experimental platform to verify its effectiveness. The performance of an industrial robot with the actuators originally installed is compared with the results obtained by the synergic approach. The results of this comparison show that 10.85% of electrical power can be saved, and the trajectory tracking error improved up to 57.41% using the proposed methodology.

Around the world many people loss a body member for many reasons, where advances of technology may be useful to help these people to improve the quality of their lives. Then, designing a technologically advanced prosthesis with natural movements is worthy for scientific, commercial, and social reasons. Thus, research of manufacturing, designing, and signal processing may lead up to a low-cost affordable prosthesis. This manuscript presents a low-cost design proposal for an electromyographic electronic system, which is characterized by a neural network based process. Moreover, a hand-type prosthesis is presented and controlled by using the processed electromyographic signals for a required particular use. For this purpose, the user performs several movements by using the healthy-hand to get some electromyographic signals. After that, the obtained signals are processed in a neural network based controller. Once an usable behavior is obtained, an exact replica of controlled motions are adapted for the other hand by using the designed prosthesis. The characterization process of bioelectrical signals was performed by training twenty characteristics obtained from the original raw signal in contrast with other papers in which seven characteristics have been tested on average. The proposed model reached a 95.2% computer test accuracy and 93% accuracy in a real environment experiment. The platform was tested via online and offline, where the best response was obtained in the online execution time.

Although additive manufacturing is a relatively new technology, it has been widely accepted by industry and academia due to the wide variety of prototypes that can be built. Furthermore, using mobile robots to carry out different tasks allows greater flexibility than using manipulator robots. In that sense, and based on those above, this article focuses on the design and assembly of a multi-configurable mobile robot that is capable of changing from a differential to an omnidirectional configuration. For this purpose, a sequential mechatronic design/control methodology was implemented to obtain an affordable platform via additive manufacturing which is easily scalable and allows the user to change from one configuration to another. As a proof of concept, this change is made manually. Fabrication, construction, and assembly processes for both structures are presented. Then, a hierarchical control law is designed. In this sense and based on Lyapunov’s method, a low-level controller is developed to control the angular speed of the wheels to a desired angular speed, and a medium-level controller controls the robot’s attitude to follow a desired Cartesian trajectory. Finally, the control strategies are implemented in both prototype configurations, and through experimental results, the theoretical analysis and the construction of the mobile robot are validated.

A multi-objective optimization method is proposed for optimal motor selection, control, and planning for a point-to-point trajectory of a robot manipulator, where three objective functions are proposed to be minimized: the total weight of actuators, the execution time and velocity transitions between planned points, and the tracking error of the task. A concurrent approach is proposed where the powertrain dynamics of the robot is taken into account, that is, motor, gearbox, and load at each actuated joint. To solve the concurrent optimization problem, a genetic algorithm is used, where a representative set of non-dominated solutions form the Pareto-front. The method is tested for a 3-degree-of-freedom manipulator by selecting a particular solution.