To realize the high-performance load torque tracking of an electric dynamic load simulator system with random measurement noises and strong position disturbances, a PD-type iterative learning control (ILC) algorithm with adaptive learning gains is proposed in this paper. With the principle of system analyzing, a nonlinear discrete state-space model is established. The adaptive learning gains is used to suppress the effects of periodic disturbances and random measurement noises on the load torque tracking performance. A traditional PD feedback controller in parallel with the proposed ILC is designed to stabilize the system and render the ILC converge quickly. The convergence analysis of the proposed control method ensures the stability of the system. Compared with the fixed learning gains, the experiment results show that the proposed control method has better load torque tracking performance and can effectively suppress the adverse effects of periodic and aperiodic disturbances on tracking accuracy.

When the linear active disturbance rejection control (LADRC) is applied for the voltage-controlled inverter, the discrete period and the measurement noise limits the observer bandwidth, which affects the anti-disturbance performance of the system. This results in a poor ability to deal with the output voltage fluctuation under the load switch. In this paper, a novel LADRC strategy based on the known disturbance compensation is proposed for the voltage-controlled inverters. Firstly, the original LADRC scheme is designed. The dynamic performance and robustness of the system are analyzed by a root locus diagram, and the anti-disturbance ability is studied through amplitude-frequency characteristics. Then the partial model information and the load current are treated as the known disturbance and introduced to the linear extended state observer (LESO) to improve observation accuracy. The difference in anti-disturbance performance with the original scheme is compared and the stability of the LESO and LADRC is analyzed. Finally, the effectiveness of the proposed scheme is verified by the simulation and experimental results.