The field of robotics has grown a lot over the years due to the increasing necessity of industrial production and the search for quality of industrialized products. The identification of a system requires that the model output be as close as possible to the real one, in order to improve the control system. Some hybrid identification methods can improve model estimation through computational intelligence techniques, mainly improving the limitations of a given linear technique. This paper presents as a main contribution a hybrid algorithm for the identification of industrial robotic manipulators based on the recursive least square (RLS) method, which has its matrix of regressors and vector of parameters optimized via the Kalman filter (KF) method (RLS-KF). It is also possible to highlight other contributions, which are the identification of a robotic joint driven by a three-phase induction motor, the comparison of the RLS-KF algorithm with RLS and extended recursive least square (ERLS) and the generation of the transfer function by each method. The results are compared with the well-known recursive least squares and extended recursive least squares considering the criteria of adjustable coefficient of determination ( R _a ² ) and computational cost. The RLS-KF showed better results compared to the other two algorithms (RLS and ERLS). All methods have generated their respective transfer functions.

The use of industrial robots has grown over the years, making production systems increasingly efficient. Within this context, some limitations appear that can delay the productive process causing damages to the production. These limitations are robot stops, for example. Stops can be caused by various factors, such as accidents, collisions of manipulator robots with operators or other equipment. The main contribution of this research is to improve the Artificial Potential Field (APF) using Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) by optimizing the APF parameters in collision avoidance. We present as results: the trajectories generated by a planar manipulator robot; the position errors between the final position and the last position of the generated trajectories; and the computational cost of the PSO, GA and DE algorithms to find the parameters of the APF algorithm.

Scheduling residential loads for financial savings and user comfort may be performed by smart home controllers (SHCs). For this purpose, the electricity utility’s tariff variation costs, the lowest tariff cost schedules, the user’s preferences, and the level of comfort that each load may add to the household user are examined. However, the user’s comfort modeling, found in the literature, does not take into account the user’s comfort perceptions, and only uses the user-defined preferences for load on-time when it is registered in the SHC. The user’s comfort perceptions are dynamic and fluctuating, while the comfort preferences are fixed. Therefore, this paper proposes the modeling of a comfort function that takes into account the user’s perceptions using fuzzy logic. The proposed function is integrated into an SHC that uses PSO for scheduling residential loads, and aims at economy and user comfort as multiple objectives. The analysis and validation of the proposed function includes different scenarios related to economy–comfort, load shifting, consideration of energy tariffs, user preferences, and user perceptions. The results show that it is more beneficial to use the proposed comfort function method only when the user requires SHC to prioritize comfort at the expense of financial savings. Otherwise, it is more beneficial to use a comfort function that only considers the user’s comfort preferences and not their perceptions.

Prevenção de Colisão de um Manipulador SCARA utilizando Campos Potenciais Artificiais e Caminhos Probabilísticos / Collision Avoidance of a SCARA Manipulator using Potential Artificial Fields and Probabilistic Roadmap