Modular Model Predictive Control Research Articles

Model predictive control of modular multilevel converter based on fixed switch state set for offshore wind power

AbstractThis paper aims at the problems of the traditional modular model predictive control (MPC) of modular multilevel converter (MMC) with huge computational complexity and high equivalent switching frequency. In this paper, a model predictive control method based on fixed switch state set (FSS‐MPC) is proposed. In this method, two cost functions are used to control phase current and circulating current. Meanwhile, the number of the upper and lower on‐state sub‐modules (SM) is obtained by cost functions. In order to obtain the optimal switching sequence of SM, FSS‐MPC adopts a SM capacitive voltage balancing strategy which has low sorting frequency. Under the control of the FSS‐MPC, the amount of switch combination and sorting computation are greatly reduced. In addition, the switching frequency is extremely low. Finally, to verify the effectiveness and feasibility of the proposed control strategy, a simulation system of 11‐level MMC for offshore wind power has built on the MATLAB/Simulink platform.

Modular Model-Predictive Control With Regulated Third-Harmonic Injection for Zero-Sequence Stabilized Inverter

A modular model-predictive control (MPC) method for a nonisolated three-phase <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LCL$</tex-math></inline-formula> grid-connected converter is proposed with zero-sequence voltage control and regulated third-harmonic injection capabilities. The control method is proposed with the combination of a modified three-phase <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LCL$</tex-math></inline-formula> converter topology to stabilize the common-mode voltage. The control mechanism consists of two layers: 1) the central-level control of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dq$</tex-math></inline-formula> -sequence output current control cascaded with 2) the local-level control of zero-sequence voltage three-phase inductor current/capacitor voltage MPC. No grid-side inductance value is needed for the two-layer cascaded control structure, which both improves the dynamic performance from the inner MPC loop and avoids the MPC parametric error caused by the uncertainty of grid-side inductance. With the zero-sequence MPC, the common-mode voltage and leakage current can be attenuated without a bulky isolation transformer. A regulated third-harmonic injection (RTHI) method is designed based on the zero-sequence voltage control and the proposed modified converter topology to improve the dc bus utilization and avoid the overmodulation issue. Compared with the conventional third-harmonic injection methods, no extra harmonics are injected to the grid, and there is no need for a specialized grid total harmonic distortion optimization algorithm with the proposed zero-sequence-controlled RTHI. The stability and robustness of the proposed RTHI method are also improved with the zero-sequence MPC regulation. In addition, the modular concept of the local MPC module enables the extensibility for the power converter control with a flexible number of phases. The dynamic performance, leakage current attenuation, dc bus utilization, and grid-connection power quality of the proposed MPC method with RTHI have been validated experimentally.

Optimal Tracking and Resonance Damping Design of Cascaded Modular Model Predictive Control for a Common-Mode Stabilized Grid-Tied $LCL$ Inverter

A cascaded modular model predictive control (MMPC) method is designed for a modified nonisolated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LCL$</tex-math></inline-formula> grid-connected inverters to provide resonance damping, improved dynamic performance, and leakage current attenuation capabilities. The continuous control set model predictive control strategy is applied for the proposed method. The active damping function of the inner loop MMPC is analyzed in detail to illustrate the mechanism of improving the system dynamic performance. The cascaded MMPC method is compared with the conventional proportional-integral (PI) control methods with/without a notch filter to show the merits in resonance damping and dynamic response. The optimal control parameters design procedure is elucidated with the tuning mechanism of the MMPC weighing factor and PI gain. With the proposed optimal MMPC design method, the dynamic performance of rising time and overshoot are improved compared to the conventional PI control methods with/without the notch filter. The simulation and experimental results verified the proposed control design method.

Modular Model Predictive Control upon an Existing Controller

The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem; the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples.

Preview predictive control layer design based upon known wind turbine blade‐pitch controllers

AbstractThe use of upstream wind measurements has motivated the development of blade‐pitch preview controllers for improving rotor speed tracking and structural load reduction beyond that achievable via conventional feedback control. Such preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, alter the closed‐loop dynamics of the existing blade‐pitch feedback control system. This can result in a deterioration of the robustness properties and performance of the existing feedback control system. Furthermore, performance gains from utilising the upcoming real‐time measurements cannot be easily distinguished from the feedback control, making it difficult to formulate a clear business case for the use of preview control. Therefore, the aim of this work is to formulate a modular MPC layer on top of a given output‐feedback blade‐pitch controller, with a view to retaining the closed‐loop robustness and frequency‐domain performance of the latter. The separate nature of the proposed controller structure enables clear and transparent quantification of the benefits gained by using preview control, beyond that of the underlying feedback controller. This is illustrated by results obtained from high‐fidelity closed‐loop turbine simulations, showing the proposed control scheme incorporating knowledge of the oncoming wind and constraints achieved significant 43% and 30% reductions in the rotor speed and flap‐wise blade moment standard deviations, respectively. Additionally, the chance of constraint violations on the rotor speed decreased remarkably from 2.15% to 0.01%, compared to the nominal controller. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Nonlinear model predictive control for a heating and cooling system of a low-energy office building

Model predictive control (MPC) is highly suitable for building heating and cooling control because it exploits disturbance predictions, obeys constraints, and enables optimal building operation in terms of user comfort and energy efficiency. This work presents a highly efficient nonlinear modular MPC concept. It optimally controls both, heating and cooling activities in a low-energy office building simultaneously. Relevant system nonlinearities are considered through a nonlinear prediction model, an LTI MPC optimization step, and an efficient mixed-integer mapping to setpoint temperatures in the building. The involved optimization problems are efficiently solvable and enable realtime control, and the controller structure allows for retrofitting and can directly be incorporated into the existing building control infrastructure. A clear formulation of thermal user comfort and energy efficiency allows straightforward tuning. Excellent control performance and robustness are observed in detailed co-simulation studies, significantly outperforming a classical rule-based reference control law. Possible approaches to analyze robust stability of the controlled system are discussed and related to results of robust TS-fuzzy system analysis.

Randomized Summer Camp Crossover Trial in 5- to 9-Year-Old Children: Outpatient Wearable Artificial Pancreas Is Feasible and Safe.

The Pediatric Artificial Pancreas (PedArPan) project tested a children-specific version of the modular model predictive control (MMPC) algorithm in 5- to 9-year-old children during a camp. A total of 30 children, 5- to 9-years old, with type 1 diabetes completed an outpatient, open-label, randomized, crossover trial. Three days with an artificial pancreas (AP) were compared with three days of parent-managed sensor-augmented pump (SAP). Overnight time-in-hypoglycemia was reduced with the AP versus SAP, median (25(th)-75(th) percentiles): 0.0% (0.0-2.2) vs. 2.2% (0.0-12.3) (P = 0.002), without a significant change of time-in-target, mean: 56.0% (SD 22.5) vs. 59.7% (21.2) (P = 0.430), but with increased mean glucose 173 mg/dL (36) vs. 150 mg/dL (39) (P = 0.002). Overall, the AP granted a threefold reduction of time-in-hypoglycemia (P < 0.001) at the cost of decreased time-in-target, 56.8% (13.5) vs. 63.1% (11.0) (P = 0.022) and increased mean glucose 169 mg/dL (23) vs. 147 mg/dL (23) (P < 0.001). This trial, the first outpatient single-hormone AP trial in a population of this age, shows feasibility and safety of MMPC in young children. Algorithm retuning will be performed to improve efficacy.

Abstracts from ATTD 20158th International Conference on Advanced Technologies &amp; Treatments for DiabetesParis, France—February 18–21, 2015

Abstracts from ATTD 20158th International Conference on Advanced Technologies &amp; Treatments for DiabetesParis, France—February 18–21, 2015