Abstract
Adaptive tracking control of the speed of a very elastically attached circular load driven by a direct current motor accompanied with an adaptive conventional and a fractional-order Proportional Integral Derivative (PID) controller is studied. It refers to improving the closed-loop control system response of elastically coupled components of drivelines. The motor and the load mechatronic models and the block diagrams are constructed. Parameters of the PID controller in the model reference control are both constant, as well as vary in time. The adaptive control method is improved by the application of a new closed-loop control structure canceling error dynamics. A few competing control strategies are tested based on the application of two types low and high frequency stepwise increasing variations of loading torque and damping coefficient of motion. Moreover, the performance of the control strategies is verified by Integral Time-Weighted Absolute Error (ITAE) index, since their robustness is evaluated by applying a sine modulated triangle waves of selected electric parameters. Therefore, a dynamic forcing and parameter uncertainty is applied. Simulation results are compared for checking the proposed methods.
Highlights
Modeling of electric drives of machines and vehicles is currently one of the most explored fields of control engineering
A direct adaptive Fuzzy Logic Controller (FLC) studied in Reference [10] for precise control of the Direct Current (DC) motor speed is estimated from two levels, where the lower level uses a Mamdani fuzzy controller and the upper level is an inverse model based on a Takagi–Sugeno approach
Fractional-Order PID (FOPID) controller is proposed in Reference [9] for Brushless DC (BLDC) motor to achieve effective control of torque and speed
Summary
Modeling of electric drives of machines and vehicles is currently one of the most explored fields of control engineering. A direct adaptive Fuzzy Logic Controller (FLC) studied in Reference [10] for precise control of the DC motor speed is estimated from two levels, where the lower level uses a Mamdani fuzzy controller and the upper level is an inverse model based on a Takagi–Sugeno approach. In this method, the output is used to adapt the parameters of the FLC in the lower level. In Reference [13], a bio-inspired adaptive control strategy based on an improved differential evolution is proposed for the speed regulation of the DC motor subject to parametric uncertainties. A similar problem devoted to BLDC motors is related to a reduction of cogging torque [15]
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