This paper presents an optimal and robust nonlinear control scheme to achieve trajectory tracking for disturbed nonlinear systems, which is applied for the control of power converters in DC microgrids. The state-feedback optimal controller synthesis is based on the solution of the Hamilton-Jacobi- Bellman (HJB) equation, which considers the minimization of a cost functional (performance index), resulting in an efficient control strategy. The proposed methodology is used to efficiently regulate the power flow from renewable resources into the utility grid, to supply energy to loads, and to storage energy. Simulation results are presented to assess the performance of the proposed controller for a case study DC microgrid, where its adequate operation depends on the DC bus voltage regulation; hence, for guaranteeing such voltage regulation through the converter (inverter) connected to the utility grid, the optimal control scheme is in addition combined with a super-twisting controller (a robust sliding mode-based control technique) to enhance the inverter control strategy robustness.

This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton-Jacobi-Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot.

In this article, a neural sliding-mode linearization controller is proposed to regulate the generated active and reactive power for each distributed energy resource in a microgrid. The developed controller is based on recurrent high-order neural network identification, trained online with an extended Kalman filter learning algorithm. Based on such neural identification, adequate models of the microgrid generation units are obtained even in the presence of grid disturbances, which helps the proposed controller to reject disturbances, to ensure stability, and to operate the renewable energy sources under different grid scenarios. The proposed microgrid is composed of a wind power system, a solar power system, a battery bank, and a load demand. In addition, the microgrid under study is interconnected to an IEEE nine-bus system. The whole system is simulated in real time using the Opal-RT (OP5600) simulator. Real-time simulation results illustrate the effectiveness of the proposed control scheme to achieve trajectory tracking of the distributed energy resources active and reactive power even in the presence of grid disturbances.

Power quality in electrical systems is a basic requirement that should be guaranteed. Nonetheless, the increase of power-electronics-based applications and nonlinear loads have induced harmonic distortion leading to a degradation of the power quality; therefore, effective methods to measure and suppress such distortion need to be developed. The main contributions of this paper are: the development of a novel fast-converging optimal estimator to determine on-line the harmonic components of a time-varying signal, which can be used for analysis and control purposes; the usage of the estimator to generate reference signals for an optimal control system in order to suppress the harmonics components; and the experimental validation of the proposed methodology as a feasible solution for industrial applications. The proposal considers practical assumptions such as the signal to be analyzed is corrupted with noise and contains time-varying harmonics (no a steady-state condition is assumed). As an application of the harmonic estimator, two experimental tests are carried out: firstly, the optimal determination of the harmonic components of the current waveform in a nonlinear load, and secondly, the usage of harmonics information to mitigate them through of an active power filter, which is controlled by using an optimal trajectory tracking control scheme.

This paper presents the design of a nonlinear optimal control strategy for a wind energy generation system based on a small scale wind turbine, a permanent magnet synchronous generator, a back-to-back power converter, and an LCL filter interconnected with the grid. The main control objectives in this energy system involve the trajectory tracking and regulation of different system variables as generator angular speed, DC-link voltage and reactive power. In order to fulfill these objectives, the application of optimal controllers based on the state-dependent coefficient factorization technique is proposed for both the generator-side converter and the grid-side converter. The advantages of the proposed control scheme are to achieve a wide system operating range, due to the controller design takes into account the system nonlinearities, and in addition, an efficient trajectory tracking of time-varying references generated by a maximum power point tracking algorithm, which uses a wind speed estimator. Also, an LCL filter is used to attenuate the harmonic content of the injected current to the utility grid to satisfy interconnection standards, which requires a THD below 5%. The effectiveness of the proposed controllers is verified via simulations developed in Matlab/Simulink, where the variables to be controlled reach the desired references that ensure proper operation, maximum power extraction, and fulfillment of interconnection standards.