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A new speed reference generator for electric vehicles with fixed transmission gear ratio powered by centralized electric motors is presented. On the basis of a contraction mapping algorithm and without requiring any a priori knowledge of the external conditions, a sequence of speed reference values is provided. They converge to a steady-state operating point with a safe tire longitudinal slip. CarSim simulations illustrate the effectiveness of the proposed approach, even in the presence of uncertain parameters and complex vehicle dynamics, which are neglected in the control design.

The heart rate can be effectively used as a measure of the exercise intensity during long duration cycle-ergometer exercises: precisely controlling the heart rate (HR) becomes crucial especially for athletes or patients with cardiovascular/obesity problems. The aim of this letter is to experimentally show how the nonlocal and nonswitching nonlinear control that has been recently proposed in the literature for the HR regulation in treadmill exercises can be effectively applied to cycle-ergometer exercises at constant cycling speed. The structure of the involved nonlinear model for the HR dynamics in cycle-ergometer exercises is mathematically inspired by the structure of a recently identified and experimentally validated nonlinear model for the HR dynamics in treadmill exercises: the role played by the treadmill speed is played here by the work load while the zero speed case for the treadmill exercise is here translated into the cycling operation under zero work load. Experimental results not only validate the aforementioned nonlinear model but also demonstrate the effectiveness--in terms of precise HR regulation--of an approach which simply generalizes to the nonlinear framework the classical proportional-integral control design. The possibility of online modifying the HR reference on the basis of the heart rate variability (HRV) is also suggested and experimentally motivated.

Permanent-magnet step motors offer several advantages such as high efficiency, high power density, high torque-to-inertia ratio, and excellent durability and serviceability, as well as the absence of external rotor excitation and windings. The nonuniformity in the developed torque due to the nonsinusoidal flux distribution in the airgap is, however, the major obstacle in achieving global high-precision position tracking. When the position reference profile is a periodic signal of known period, such an obstacle may be however overcome by using recent learning control techniques, which require neither high gains in the inner speed/position control loops nor resetting procedures. An experimental comparison of two different recently designed learning position controls (“adaptive” and “iterative”) is, for the first time, carried out with reference to the same low-speed robotic application. Benefits and drawbacks of the two learning approaches are analyzed in detail.

We address the tracking control problem for sensorless (nonsalient-pole surface) permanent magnet synchronous motors with unknown constant load torque. On the basis of stator current and voltage measurements only and without relying on nonrobust open loop integration of motor dynamics, a novel sixth order nonlinear adaptive control algorithm is designed, which guarantees, under persistency of excitation, local exponential rotor speed tracking. Simulation results illustrate the closed loop performance and show the effectiveness of the proposed solution even in the presence of current sensor noise and stator resistance variations.