Predictive control of power converters: Designs with guaranteed performance

© 2005-2012 IEEE. In this work, a cost function design based on Lyapunov stability concepts for finite control set model predictive control is proposed. This predictive controller design allows one to characterize the performance of the controlled converter, while providing sufficient conditions for local stability for a class of power converters. Simulation and experimental results on a buck dc-dc converter and a two-level dc-ac inverter are conducted to validate the effectiveness of our proposal

Frequency-Shaped Second-Order Sliding Mode Control for Smart Suspension Systems

© 2018 IEEE. Design of a frequency-shaped second-order sliding mode (FS2SM) controller is demonstrated by means of exploiting second-order low-pass filter (LPF) to model the dynamic sliding surface to shape the frequency characteristics of the equivalent dynamics. The proposed technique is numerically verified in the simulation of a half-car model (HCM) with inbuilt active hydraulically interconnected suspension (HIS) system. The closed-loop performances confirm that inclusion of an appropriate filter in the control scheme allows not only to reduce the roll angle but also its spectrum can be shaped

Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

© 2017 IEEE. This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear d-q reference frame voltage-current model and linearized power flow approximations. This allows the optimal power flows to be solved as a convex optimization problem, for which fast and robust solvers exist. The proposed method does not assume that real and reactive power flows are decoupled, allowing line losses, voltage constraints, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real-time MPC implementation

Sequential Phase-Shifted Model Predictive Control for multicell power converters

© 2017 IEEE. This paper proposes a sequential Phase-Shifted Model Predictive Control (PS-MPC) strategy for multicell power converters. The key novelty of this proposal lies in the way the predictive control strategy is formulated to fully exploit a phase-shifted pulse width modulation (PS-PWM) stage. Normally, when using a linear controller along with a standard PS-PWM stage, the modulator receives the same duty cycle for all the internal carriers. In contrast, by means of an appropriate choice of synchronized models for each carrier, the proposed predictive controller obtains independent optimal duty cycles for each carrier in a sequential manner. This allows one to formulate the optimal control problem to govern not only the output current but also the internal floating capacitor voltages, similarly to the finite-control-set MPC (FCS-MPC) case. As a result, the proposed sequential PS-MPC can attain a faster floating voltage balancing dynamic when compared to a standard PS-PWM implementation. Moreover, it generates a fixed-spectrum in the steady state with a constant commutation rate, which outperforms a standard FCS-MPC strategy. Simulation results of the proposed sequential PS-MPC strategy governing a single-phase four-level flying capacitor converter are presented to verify its dynamic and steady-state performance

Quadratic Model Predictive Control Including Input Cardinality Constraints

© 2017 IEEE. This note addresses the problem of feedback control with a constrained number of active inputs. This problem is known as sparse control. Specifically, we describe a novel quadratic model predictive control strategy that guarantees sparsity by bounding directly the l0-norm of the control input vector at each control horizon instant. Besides this sparsity constraint, bounded constraints are also imposed on both control input and system state. Under this scenario, we provide sufficient conditions for guaranteeing practical stability of the closed-loop. We transform the combinatorial optimization problem into an equivalent optimization problem that does not consider relaxation in the cardinality constraints. The equivalent optimization problem can be solved utilizing standard nonlinear programming toolboxes that provides the input control sequence corresponding to the global optimum

Model Predictive Switching Pattern Control for Current-Source Converters with Space-Vector-Based Selective Harmonic Elimination

© 2017 IEEE. This paper presents a model predictive switching pattern control (MPSPC) for a current-source converter (CSC), which achieves superb low-order harmonics elimination performance in steady state and improved transient responses. Based on a proposed space-vector-based selective harmonic elimination (SHE) method and prediction of load current at the next sampling instant, MPSPC prefers to following a precalculated SHE-pulse width modulation (PWM) pattern in steady state, and governing the CSC through a model predictive control (MPC) approach during transients. In comparison with existing schemes, the advantages of MPSPC are threefold: First, quantization error, introduced by a constant sampling frequency in MPC and degrading steady-state low-order harmonic elimination, is mitigated in the proposed scheme. Second, there is no weighting factor in the cost function, as used in existing schemes. Finally, MPSPC is totally realized based on one-step prediction, which simplifies the structure of the scheme. Both simulation and experimental results verify the steady state and dynamic performance of MPSPC with different SHE-PWM patterns

An EM-based identification algorithm for a class of hybrid systems with application to power electronics

In this paper we present an identification algorithm for a class of continuous-time hybrid systems. In such systems, both continuous-time and discrete-time dynamics are involved. We apply the expectation-maximisation algorithm to obtain the maximum likelihood estimate of the parameters of a discrete-time model expressed in incremental form. The main advantage of this approach is that the continuous-time parameters can directly be recovered. The technique is particularly well suited to fast-sampling rates. As an application, we focus on a standard identification problem in power electronics. In this field, our proposed algorithm is of importance since accurate modelling of power converters is required in high- performance applications and for fault diagnosis. As an illustrative example, and to verify the performance of our proposed algorithm, we apply our results to a flying capacitor multicell converter. © 2014 © 2014 Taylor & Francis

Energy cost optimization in microgrids using model predictive control and mixed integer linear programming

© 2019 IEEE. This paper presents a model predictive control (MPC) approach based on the mixed integer linear programming (MILP) to develop an optimal power management strategy (PMS) for minimizing the electricity bill of commercial buildings in a domestic on-grid system. The optimal PMS is first formulated as a MILP-MPC with time-varying constraints. The constraints are then linearized at each sampling time so that a receding horizon principle can be used to determine the control input applied to the plant and update the model. The time-varying efficiency of power electronic converters is evaluated for each time interval and assumed to be persistent for the prediction time horizon. The numerical results show that the proposed MILP-MPC strategy with variable efficiency is effective in utilizing photovoltaic power generation to save the cost on electricity for buildings

Delta-connected cascaded H-bridge multilevel photovoltaic converters

© 2015 IEEE. Multilevel cascaded H-bridge converters are becoming popular for next generation large-scale photovoltaic power converters. However, the power generation levels in the three phases can be significantly unequal, especially in a large plant, owing to the non-uniform irradiance levels and/or ambient temperatures. This paper proposes the delta-connected cascaded H-bridge converter for large-scale photovoltaic farms. Compared to the existing star connection, the delta connection reduces the converter overrating required. Experimental results obtained from a 430 V, 10 kW, three-phase, seven-level, delta connected cascaded H-bridge converter prototype are provided to demonstrate the superiority of the delta connection