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 2 a ) 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 generation of electric energy is an essential factor for society, both for economic and social development. Within this context, renewable sources have been gaining ground, such as photovoltaic systems. However, its efficiency presents non-linear characteristics due to thresholds in temperature and irradiance, mainly caused by climatic factors. This problem affects the load power supply, thus loosing effectiveness. To minimize this problem, it is necessarythe operation at the maximum power point (MPP), made by means of some systems, called Maximum Power Point Trackers (MPPT). This paper discusses the implementation of two intelligent MPPT algorithms: the fisrt one using a metaheuristic called Radial Movement Optimization (RMO), and the second one based on the cooperation of RMO, and a traditional MPPT, called Perturb and Observe (P&O).The results show that although providing stable outputs after finding the MPP, the metaheuristic presents a larger oscillation during the search procedure in comparison with P&O.

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