This paper is concerned with the design and implementation of a robust position backstepping tracking controller for a permanent magnet synchronous motor (PMSM). The information on the angular position and velocity, provided by a classical resolver-to-digital converter, additionally employs a phase lock loop (PLL) circuit. A backstepping control law is designed from the input-output linearization of the PMSM model, written in d-q coordinates. This controller is adapted via the online estimation of the unknown load torque and friction effects. A linear extended state observer is devised for this purpose, thus ensuring high closed-loop performance of the motor trajectory tracking task. An input-state stability analysis of the entire system is also provided. Cosimulation via the MATLAB/Simulink-PSIM package, including realistic measurement disturbances, is used to investigate the stability and accuracy of the proposed control algorithm. Experimental results are provided.

This paper presents a robust linear generalized proportional integral (GPI) control scheme for the output reference trajectory tracking task on a multilevel single-phase “buck” inverter. The scheme is shown to be robust with respect to arbitrary time-varying load current demands, acting as perturbation inputs to the inverter, and to significant converter parameter variations. The discrete time version of the control scheme is implemented on a field-programmable gate array hardware. Several laboratory robustness tests are performed on the controlled inverter with excellent results.

In this paper, we propose a fast online closed-loop identification method combined with an output-feedback controller of the generalized proportional integral (GPI) type for the control of an uncertain flexible robotic arm with unknown mass at the tip, including a Coulomb friction term in the motor dynamics. A fast nonasymptotic algebraic identification method developed in continuous time is used to identify the unknown system parameter and update the designed certainty equivalence GPI controller. In order to verify this method, several informative simulations and experiments are shown.

In this article, we propose a fast on-line closed loop identification method of continuous-time combined with an output feedback controller of the generalized proportional integral type (GPI), for the control of an uncertain flexible robotic arm with unknown mass at the tip, including a Coulomb friction term in the motor dynamics. A fast, non-asymptotic, algebraic identification method is used to identify the unknown system parameter and update the designed certainty equivalence GPI controller. In order to verify this method several informative experiments are shown.

Dynamic feedback control strategies are proposed for the asymptotic stabilization and asymptotic output tracking problems, associated with the operation of flexible joint manipulators. Smooth dynamical linearizing feedback controllers, as well as dynamical sliding mode regulators, are derived within the context of M. Fliess's (1989) generalized observability canonical form (GOCF). The GOCF is obtained by means of a state elimination procedure, carried out on the system of differential equations describing the manipulator dynamics. The remarkable feature of this new approach lies in the fact that a truly effective smoothing of the sliding mode controlled responses is possible while substantially reducing the chattering in the control input torque. Simulation examples are given that illustrate the performance of the proposed controllers.>

The authors propose a new class of adaptation algorithms for single- and multilayer perceptrons with discontinuous nonlinearities. The behavior of the proposed algorithms is shown on an application example and simulation results are included. The simulations were performed using the SIMNON package developed for purpose of simulation of nonlinear systems. The results can be used to control unknown dynamic systems using neural controllers. Indeed, many robust control algorithms utilize the inverse dynamics of the plant to be controlled. Thus, the proposed structures where the perceptrons are the inverse system model identifiers should constitute a part of the controller.>