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This paper presents the identification of the inverse kinematics of a cylindrical manipulator using identification techniques of Least Squares (LS), Recursive Least Square (RLS), and a dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by RLS (RLSPSO). A helical trajectory in the cartesian space is used as input. The dynamic model is found through the Lagrange equation and the motion equations, which are used to calculate the torque values of each joint. The torques are calculated from the values of the inverse kinematics, identified by each algorithm and from the manipulator joint speeds and accelerations. The results obtained for the trajectories, speeds, accelerations, and torques of each joint are compared for each algorithm. The computational costs as well as the Multi-Correlation Coefficient ( R 2 ) are computed. The results demonstrated that the identification accuracy of RLSPSO is better than that of LS and PSO. This paper brings an improvement in RLS because it is a method with high complexity, so the proposed method (hybrid) aims to improve the computational cost and the results of the classic RLS.

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.

This paper proposes a robust control based on generalized predictive control (GPC) applied to the current control loop for a switched reluctance motor (SRM) drive. The proposed controller has two degrees of freedom where the setpoint tracking is decoupled from the load disturbance rejection at the nominal case. In addition a filter design is proposed in order to achieve good relationship among robustness, load disturbance rejection, and noise attenuation. Simulation and real experimental results are shown to illustrate the controller performance.

This paper presents the influence of the A. Considerations for the analytical method

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In this paper, a simple adaptive speed control for switched reluctance motor (SRM) is described and implemented using Digital Signal Processor (DSP). The controller structure design is based on PWM control scheme that have been used for the SRM current controller. To realize the speed control, the speed reference is calculated as function of current machine. From experimental results it is shown that the overall switched reluctance motor speed control gives good transient and steady state responses.

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