Sensorless Adaptive Voltage Control for Classical DC-DC Converters Feeding Unknown Loads: A Generalized PI Passivity-Based Approach

Abstract

The problem of voltage regulation in unknown constant resistive loads is addressed in this paper from the nonlinear control point of view for second-order DC-DC converters. The converters’ topologies analyzed are: (i) buck converter, (ii) boost converter, (iii) buck-boost converter, and (iv) non-inverting buck-boost converter. The averaging modeling method is used to model these converters, representing all these converter topologies with a generalized port-Controlled Hamiltonian (PCH) representation. The PCH representation shows that the second-order DC-DC converters exhibit a general bilinear structure which permits to design of a passivity-based controller with PI actions that ensures the asymptotic stability in the sense of Lyapunov. A linear estimator based on an integral estimator that allows reducing the number of current sensors required in the control implementation stage is used to determine the value of the unknown resistive load. The main advantage of this load estimator is that it ensures exponential convergence to the estimated variable. Numerical simulations and experimental validations show that the PI passivity-based control allows voltage regulation with first-order behavior, while the classical PI controller produces oscillations in the controlled variable, significantly when the load varies.

Voltage Regulation in Second-Order Dc-Dc Converters Via the Inverse Optimal Control Design with Proportional-Integral Action

“…The objective of these simulations was to show the dynamic response of the converters. For the closed loop control, the inverse optimal control with PI action (IOC-PI) and the PI passivity-based control (PI-PBC) were implemented in order to compare two kinds of nonlinear control [25], [26]. The study was conducted with the values proposed by [26] with a switching frequency of 100 kHz.…”

“…These are connected to a resistive load, which is modeled as a conductance 𝐺 𝐿 . Variables and parameters in Figure 2 have the following interpretation: 𝐸 > 0 corresponds to the input voltage (in volts -V); 𝑖 > 0 represents the associated current of inductor L (in amperes -A); 𝑣 represents the output voltage associated to capacitor C (in volts -V); and 𝑢 ϵ [0 − 1] represents the input control variable applied to the forced commutation of the transistors (dimensionless quantity) [25]. To obtain the state equations of the converters (Figure 2), it is necessary to use Kirchhoff's current and voltage laws in the two states in which the converters operate (with Q on and Q off) [26].…”

“…Now, the objective is to obtain 𝑢 ̃. First, (25) and (26) are replaced in the dynamic equations of the converter obtaining (31) and (32):…”

“…In this context there are different kind of controls which have been implemented in both converters and inverters. Particularly, the direct current converter control involves a diversity of techniques [10] - [12], namely, direct pole placement [13]; voltage control mode [14]; current control mode [15], [16]; PID control [17]; classical half cycle posicast control [18]; hybrid posicast control [19]; sliding control mode [20]; fuzzy logic control [21], [22]; neural network-based control [23]; predictive model control [24]; state space modeling control and passivity-based control [25], [26]; and intelligent control, among others. These techniques allow achieving and/or satisfying the demands of problems in specific systems.…”

Comparative Performance Analysis of the DC-AC Converter Control System Based on Linear Robust or Nonlinear PCH Controllers and Reinforcement Learning Agent

“…Traditionally, PI-type controllers are used, which offer relatively good performance and parametric robustness, but only around static operating points established after the tuning of the PI-type controller [ 11 ]. Naturally, to obtain superior control performances, a series of modern types of controllers have been developed and implemented specifically for the control of the main elements of the microgrid described above, including adaptive controllers [ 12 ], robust controllers [ 13 , 14 , 15 , 16 , 17 ] in case of significant parametric variations, neuro-fuzzy controllers [ 18 ], as well as nonlinear controllers based on the Passivity theory, including nonlinear PCH [ 19 , 20 , 21 , 22 , 23 ].…”