This article proposes a continuous-control-set model-predictive control (CCS-MPC) designed explicitly for second-order dc/dc converters, such as the boost, buck, buck–boost, and noninverting buck–boost converters. These converters share a bilinear dynamic structure that allows designing a generalized control. In contradistinction to the conventional finite-control-set approach, the proposed control uses a pulsewidth modulation that allows a continuous control set with constant switching frequency. The modulation index's saturation is considered an inequality constraint in the optimization model. The proposed control guarantees stability in the sense of Lyapunov. This property is proven by using the passive structure of these converters. Experimental results on the four converters mentioned previously show the superior performance of the proposed control in terms of robustness, offset, computation time in the central processing unit, and, of course, stability.

This paper presents an adaptive control to stabilize the output voltage of a DC–DC boost converter that feeds an unknown constant power load (CPL). The proposed controller employs passivity-based control (PBC), which assigns a desired system energy to compensate for the negative impedance that may be generated by a CPL. A proportional-integral (PI) action that maintains a passive output is added to the PBC to impose the desired damping and enhance disturbance rejection behavior, thus forming a PI+PBC control. In addition, the proposed controller includes two estimators, i.e., immersion and invariance (I&I), and disturbance observer (DO), in order to estimate CPL and supply voltage for the converter, respectively. These observers become the proposed controller for an adaptive, sensorless PI+PBC control. Phase portrait analysis and experimental results have validated the robustness and effectiveness of the adaptive proposed control approach. These results show that the proposed controller adequately regulates the output voltage of the DC–DC boost converter under variations of the input voltage and CPL simultaneously.

DC microgrid (DCMG) is a promising technology for integrating distributed resources, such as solar generation and energy storage devices, that are intrinsically DC. Recently, model predictive control (MPC) is one of the control techniques that has been widely used in microgrid applications due to its advantages, such as transient response and flexibility to nonlinearity inclusion. MPC applications can be centralized, distributed, or decentralized based on the communication architecture. A major disadvantage of the centralized model predictive control (CMPC) is the high computational effort. This paper proposes a CMPC for DCMG stabilization that uses the admittance matrix of a reduced DCMG in the prediction equation and the one-step prediction horizon to decrease the computational effort. The proposed CMPC also replaces the hierarchical architecture primary and secondary controls, achieving voltage or power regulation. A hardware-in-the-loop (HIL) tool, known as RT-Box 2, has been used to emulate an 8-node DC microgrid with versatile buck–boost converters at the supply and power consumption nodes. The proposed predictive control exhibited better performance in comparison with the averaged voltage control in the HIL experiments.

The problem of the output voltage regulation in a DC-DC boost converter feeding a DC microgrid is addressed in this research via the passivity-based control theory with a proportional–integral action (PI-PBC). Two external input estimators were implemented in conjunction with the proposed controller to make it sensorless and adaptive. The first estimator corresponds to the immersion & invariance (I&I) approach applied to calculate the expected value of the DC load, which is modeled as an unknown DC current. The second estimator is based on the disturbance–observer (DO) approach, which reaches the value of the voltage input. The main advantage of both estimators is that these ensure exponential convergence under steady-state operating conditions, and their parametrization only requires the definition of an integral gain. A comparative analysis with simulations demonstrates that the proposed PI-PBC approach is effective in regulating/controlling the voltage profile in unknown DC loads as compared to the adaptive sliding mode controller. Experimental validations have demonstrated that the proposed PI-PBC approach, in conjunction with the I&I and the DO estimators, allowed regulation of the voltage output profile in the terminals of the DC load with asymptotic stability properties and fast convergence times (1.87 ms) and acceptably overshoots (6.1%) when the voltage input varies its magnitude (from 10 to 12 V and from 10 to 8 V) considering that the DC load changed with a square waveform between 1 and 2 A with 100 Hz.

This work presents the design process of an RT Box interface for control systems in microgrid applications. The control card allows implementing different controls and communication systems for microgrids in a physical environment, facilitating the development of robust control systems facing inherent adverse scenarios such as delays, loss of information packets, failures of partial or permanent communication and noise in signal overflows, among others. In addition, it permits the generation of programming resources and behavioral antecedents, helpful information for future users of the control card interface. The Plecs RT Box is a device that will enable a real-time simulation of different power electronics applications as can be a microgrid system. The built-in control card in this project is suitable as a complementary element of the RT Box, extending the capacity of this device to emulate a microgrid but testing real communication protocol between the microcontrollers that compose each of the distributed generation units (DGU). Tests were conducted to probe the communication protocols working correctly in a microgrid context, recreating real application scenarios.

Conventional model predictive control (MPC) of power converters has been widely found in many power electronics and motor drive applications. The performance of MPC strongly depends on the precision of the converter’s physical parameters, and a mismatch of them produces a control degradation, which leads to MPC suboptimal operation. Ensuring a precise estimation of the converter’s parameters is difficult because they continuously change during the operation process due to their operating point and aging. Recently, model-free predictive control (MF-PC) has been used in motor drives and power electronics converters, especially inverters and rectifiers, to deal with the predictive control method’s dependency model. However, MF-PC proposed for dc–dc converters is an open innovation scientific field. This article proposes an MF-PC designed for second-order dc–dc converters, such as the boost, buck, buck–boost, and noninverting buck–boost converters. The presented approach uses a Kalman filter to estimate the positive and negative inductor current slopes with high accuracy and a low computational cost. The experimental results show that the proposed method is robust against parameter and model changes compared to conventional model-based solutions.