A.G. Beccuti

Explicit Model Predictive Control of DC–DC Switched-Mode Power Supplies With Extended Kalman Filtering

This paper presents a sensorless explicit model predictive control scheme for the dc-dc boost converter. No direct inductor current measurement is needed as the coil current is derived either via a static approximation or, for improved accuracy, through an extended Kalman filter. The estimate is used in the chosen optimal control problem formulation which yields the optimal input by intrinsically accounting for duty cycle and current constraints. The optimization problem is explicitly presolved offline so that the online effort is reduced to a simple search in the resulting lookup table. No online optimization is required, greatly facilitating physical implementation and allowing for experimental validation on an integrated dc-dc converter through a fixed-point DSP.

Comparison of Hybrid Control Techniques for Buck and Boost DC-DC Converters

Five recent techniques from hybrid and optimal control are evaluated on two power electronics benchmark problems. The benchmarks involve a number of practically interesting operating scenarios for fixed-frequency synchronous dc-dc converters. The specifications are defined such that good performance can only be obtained if the switched and nonlinear nature of the problem is accounted for during the design phase. A nonlinear action is featured in all methods either intrinsically or as external logic. The designs are evaluated and compared on the same experimental platform. Experiments show that the proposed methods display high performances, while respecting circuit constraints, thus protecting the semiconductor devices. Moreover, the complexity of the controllers is compatible with the high-frequency requirements of the considered application.

Supervisory hybrid model predictive control for voltage stability of power networks

Emergency voltage control problems in electric power networks have stimulated the interest for the implementation of online optimal control techniques. Briefly stated, voltage instability stems from the attempt of load dynamics to restore power consumption beyond the capability of the transmission and generation system. Typically, this situation occurs after the outage of one or more components in the network, such that the system cannot satisfy the load demand with the given inputs at a physically sustainable voltage profile. For a particular network, a supervisory control strategy based on model predictive control is proposed, which provides at discrete time steps inputs and set-points to lower-layer primary controllers based on the predicted behavior of a model featuring hybrid dynamics of the loads and the generation system.

Hybrid Control Techniques for Switched-Mode DC-DC Converters Part I: The Step-Down Topology

Several recent techniques from hybrid and optimal control are evaluated on a power electronics benchmark problem. The benchmark involves a number of practically interesting operating scenarios for a fixed-frequency synchronous step-down DC-DC converter. The specifications are defined such that good performance only can be obtained if the switched and nonlinear nature of the problem is respected during the design phase.

A Lagrangian Decomposition Algorithm for Optimal Emergency Voltage Control

Voltage control problems typically involve large networks comprising diverse components extending over considerable areas and interconnecting different grids and operators. To deal with such systems, there has been over the past few years a steadily increasing interest in model predictive/optimal control techniques as a viable solution. In such a setting, it is essential to be able to coordinate the control actions among the various grids while preserving sensitive local system data that regional operators are often not willing to disclose. In view of these discordant factors, centralized and decentralized control schemes, respectively, yield advantages and drawbacks: the former require global system data to be accessible whereas the latter may prove to be difficult to effectively implement or might achieve suboptimal performance. The present paper therefore proposes a centralized control scheme that is solved however in a distributed fashion through a Lagrangian decomposition algorithm, thus reaping the benefits of both approaches: the control problem is global, therefore intrinsically more reliable and comprehensive, but only local information is employed to achieve the overall optimum control input. The computational delay associated with the additional iterations required by the algorithm is shown to be viable for the considered application and can furthermore be inherently accounted for within the proposed optimal control scheme.

A Decentralized Explicit Predictive Control Paradigm for Parallelized DC-DC Circuits

This paper extends previously introduced explicit optimal control methods for fixed frequency switched mode DC-DC converter circuits to the parallel synchronous step-down (i.e., buck) topology with N branches. Due to the computational complexity of modelling and controlling such potentially large multivariable systems a decentralized model predictive control scheme is employed allowing for the related optimal control problem to be efficiently formulated and solved offline for each local controller. Simulation results are provided to illustrate the outcome of the proposed approach.

Sensorless explicit model predictive control of the DC-DC buck converter with inductor current limitation

This paper investigates the digital control of DC- DC buck converters. It proposes a new explicit model predictive controller for a buck DC-DC converter. The controller features an inherent current limitation over a wide range of loads and supply voltages using no current sensor. It achieves good reference tracking and good rejection of line and load transients. The complexity of the controller is limited as it consists of a table of optimal state feedback gains that have been derived off-line and that are chosen according to the measured state. Feasibility is validated experimentally on a fixed point DSP.

Temporal Lagrangian decomposition of model predictive control for hybrid systems

Given a model predictive control (MPC) problem for a hybrid system in the mixed logical dynamical (MLD) framework, a temporal decomposition scheme is proposed that efficiently derives the control actions by performing Lagrangian decomposition on the prediction horizon. The algorithm translates the original optimal control problem into a temporal sequence of independent subproblems of smaller dimension. The solution of the Lagrangian problem yields a sequence of control actions for the full horizon that is approximate in nature due to the non-convexity of the hybrid optimal control problem formulation and the consequent duality gap. For cases, however, where the duality gap is sufficiently narrow, the approximate control law will yield almost the same closed-loop behavior as the one obtained from the original optimal controller, but with a considerably smaller computational burden. An example, for which a reduction of the computation time by an order of magnitude is achieved, illustrates the algorithm and confirms its effectiveness.

Optimal Control of the Buck dc-dc Converter Operating in Both the Continuous and Discontinuous Conduction Regimes

This paper extends the recently introduced approach for modelling and solving the optimal control problem of fixed frequency switch-mode dc-dc converters using hybrid system methodologies to the discontinuous conduction mode buck circuit topology. A simplified but comprehensive piecewise affine modelling scheme is employed capturing the hybrid nature of these circuits and allowing for the optimal control problem to be efficiently formulated and solved off-line. Simulation results are provided to illustrate the outcome of the proposed approach

A distributed solution approach to centralized emergency voltage control

Voltage control and stability issues typically involve large networks with structurally diverse components extending over considerable areas and interconnecting different system grids and operators. In such a setting it is of the utmost importance to be able to effectively coordinate the control actions among the various operators whilst preserving sensitive local system data that regional operators are not willing to disclose. In view of these two discording factors centralized and decentralized control schemes respectively yield advantages and drawbacks, as the former require the global system data to be accessible whereas the latter may prove to be difficult to effectively implement if realistic performance requirements and constraints are imposed. The present paper therefore proposes a centralized control scheme that is solved however in a distributed fashion, thus reaping the benefits of both approaches: the control problem is global, therefore intrinsically more reliable and comprehensive, but only local information is employed to achieve the overall optimum control input