Two-layer Model Predictive Control Research Articles

Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS.

Yaw Stability Control of Unmanned Emergency Supplies Transportation Vehicle Considering Two-Layer Model Predictive Control

The transportation of emergency supplies is characterized by real-time, urgent, and non-contact, which constitute the basic guarantee for emergency rescue and disposal. To improve the yaw stability of the four-wheel-drive unmanned emergency supplies transportation vehicle (ESTV) during operation, a two-layer model predictive controller (MPC) method based on a Kalman filter is proposed in this paper. Firstly, the dynamics model of the ESTV is established. Secondly, the improved Sage–Husa adaptive extended Kalman filter (SHAEKF) is used to decrease the impact of noise on the ESTV system. Thirdly, a two-layer MPC is designed for the yaw stability control of the ESTV. The upper-layer controller solves the yaw moment and the front wheel steering angle of the ESTV. The lower-layer controller optimizes the torque distribution of the four tires of the ESTV to ensure the self-stabilization of the ESTV operation. Finally, analysis and verification are carried out. The simulation results have verified that the improved SHAEKF can decrease the state estimation error by more than 78% and achieve the noise reduction of the ESTV state. Under extreme conditions of high velocity and low adhesion, the average relative error is within 6.77%. The proposed control method can effectively prevent the instability of the ESTV and maintain good yaw stability.

Automation of superconducting cavity cooldown process using two-layer surrogate model and model predictive control method

Superconducting cavity is the key equipment of the superconducting accelerator, which provides higher acceleration voltage and higher frequency power per unit length, and saves equipment space. Superconducting cavities need to be gradually cooled from ambient temperature (300 K) to the superconducting temperature (4.2 K or below) during the test and operation. The temperature difference on the cavity must be strictly limited during the cooldown process to prevent excessive thermal stress on the surface of the superconducting cavity. Since this cooldown process for the superconducting cavity is a typical large hysteresis, non-linear process that is difficult to control automatically using decoupled proportion integral derivative (PID) methods directly, a less efficient manual control scheme is normally adopted. In this paper, 3D numerical simulation, 1D pipe and 0D tank model with artificial neural network (ANN) were combined to generate a two-layer surrogate model that can balance computational accuracy and speed, to improve the automation and cooling efficiency of the superconducting cavity cooldown process. In order to achieve automatic control of the cooling procedure for the superconducting cavity, a model predictive control (MPC) approach was also built on the basis of this two-layer surrogate model. According to the results of the experiment test, the improved method could realize a quick and smooth cooldown process of the superconducting cavity, during which the temperature difference on the cavity could satisfy the requirements. Additionally, the improved automatic cooldown method was more adaptable and saved 29 % more time than the original manual control method. The foundation for a more intelligent automated control of future large cryogenic systems or other system with the large hysteresis, non-linear properties, was laid.

Fast two-layer nonlinear economic predictive control using machine learning for ORC systems with non-Gaussian disturbances

This study proposes a machine learning-based generalized correntropy two-layer nonlinear economic model predictive control (ML-GC-TL-NEMPC) algorithm to improve the computational speed for Organic Rankine Cycle (ORC) systems with non-Gaussian disturbances. In the upper layer, the ratio of the net output power to the total heat transfer area is used as the economic performance index to optimize the optimal reference trajectory. In the lower layer, the generalized correntropy-based MPC (GC-MPC) is used to track the optimal trajectory. To quickly obtain an optimal solution, a Deep Neural Network (DNN) neural network controller is combined with kernel support vector machine (SVM). It learns the possible optimal solution via off-line training. Then it can be quickly implemented on on-line applications, so it can save the optimal calculation time. The proposed ML-GC-TL-NEMPC is applied to ORC. Its computation and performance are faster and better than the traditional two-layer EMPC and single-layer EMPC.

Enhanced reinforcement learning in two-layer economic model predictive control for operation optimization in dynamic environment

Economic model predictive control (EMPC) has attracted an abundance of interest in both academic and industrial communities in recent years because it is able to increase the economic profits of dynamic systems. For greater computational efficiency, two-layer EMPC schemes are applied in some complex process control. However, the performance of two-layer EMPC is significantly influenced by the accuracy of the chosen process model. Reinforcement learning (RL) has been studied as a model-free strategy of model-based control approaches, but its safety and stability remain a concern. In order to estimate the model parameters of nonlinear dynamic systems in real time, this work introduces a unique scheme for merging two-layer EMPC and RL. In this scheme, the two-layer EMPC technique maintains closed-loop stability and recursive feasibility while operating the closed-loop dynamic system optimally. And the RL agent continually compares the observed states to the predictions made by the EMPC and modifies the time-varying parameters as necessary. The usability of the proposed scheme is shown on a chemical process of ethylene oxide production in dynamic environment. This work enables online and continuous control, optimization, and model correction, and makes process production optimization more feasible and profitable.

Energy Management Strategies for Series-Parallel Hybrid Electric Vehicles Considering Fuel Efficiency and Degradation of Lithium-Ion Batteries

<div>Lithium-ion batteries are the most crucial component of hybrid electric vehicles (HEVs) with respect to cost and performance. In this article, a new energy management strategy (EMS) is developed that improves fuel efficiency (FE) and suppresses the degradation of the battery. A hybridized two-layer algorithm that combines multi-objective nonlinear model predictive control (NMPC) with a rule-based (RB) algorithm is proposed as a new EMS that is called RB-NMPC. The RB-NMPC is designed to optimize the torque split between the engine and electric motors while maintaining the maximum and minimum constraints of each component. The proposed EMS is incorporated into control-oriented vehicle models, and their performances are analyzed for different driving cycles by comparing with RB, dynamic programming (DP), and NMPC. In addition, the RB-NMPC algorithm is applied for two different powertrain configurations of HEV, P0P2 and P1P2 configurations for both an Urban Dynamometer Driving Schedule (UDDS) and a Highway Fuel Economy Test (HWFET). For P0P2, the results show that RB-NMPC outperforms other methods for UDDS with an FE that is 4.7% higher than that of RB and is the closest to that of DP, which is an optimal standard that is limited for real-time application due to its complexity among others. The capacity loss of the battery using RB-NMPC is 19.1% less than that using DP when applied to the UDDS. The FE of P1P2 is higher than that of P0P2, but the similar capacity fade is comparable. RB-NMPC shows the lowest capacity loss for both P0P2 and P1P2 configurations. Parallel comparisons are performed for the HWFET. For the HWFET, the FEs of P0P2 and P1P2 are similar. However, the capacity fades by RB-NMPC are 16.3% and 67.0% reduced compared to that by DP for P0P2 and P1P2, respectively. Finally, to verify the effectiveness of the RB-NMPC in reducing battery aging, the currents from DP and RB-NMPC EMSs are applied to pouch-type lithium-ion batteries and tested for multiple UDDSs using a battery test station. The results demonstrate that the RB-NMPC can effectively reduce battery aging.</div>

A novel virtual inertia control strategy for frequency regulation of islanded microgrid using two-layer multiple model predictive control

This paper investigates the frequency performance problem of microgrids (MGs) integrated with renewable employing an energy storage system (ESS) equipped with virtual inertial control (VIC) support. To tackle the uncertainties related to the system operation, a two-layer multiple model predictive control (TLMMPC) method, consisting of nominal and ancillary MMPCs, is proposed to submit effective control signals to the ESS for improving system frequency performance. The ancillary MMPC generates the control commands for the VIC-based ESS utilizing the signals provided by the nominal MMPC and the frequency deviation signal of the actual system considering uncertainties and operating constraints. The control commands are generated to attain the minimum value of frequency response error with the least control endeavor while considering various operational and physical limitations. The TLMMPC method has the capability to work with different state of charge (SoC) levels to obtain the desired SoC and highest efficiency from the ESS and preserve the ESS’s longevity. The dynamic performance of the proposed TLMMPC technique is investigated on an islanded MG and compared to model predictive control (MPC), fractional-order MPC, and tilt-integral-derivative controllers under different scenarios. The results validate that the proposed TLMMPC technique significantly improves the system frequency response from viewpoints of settling time, peak overshoot, and undershoot and obtains the most efficient ESS compared to the other methods.

Building energy management and Electric Vehicle charging considering battery degradation and random vehicles’ arrivals and departures

We study the energy management and Electric Vehicle (EV) charging optimization problem for a smart building integrating Renewable Energy Source (RES) considering battery degradation cost and random EVs’ arrivals/departures. Our design employs a general non-linear degradation cost model for lithium battery in which the degradation cost is a function of the Depth of Discharge (DoD). We develop a two-layer model predictive control (MPC) based framework to effectively deal with the uncertainties in EVs’ arrivals/departures and erroneous prediction of electricity load, RES energy, and energy price. Specifically, the upper layer with a longer optimization horizon aims to plan the energy for the EVs and ESS so that the total energy usage and battery degradation cost is minimized while satisfying the required energy levels of EVs before their departures. The lower layer optimizes the charging/discharging operations of the EVs and ESS over a shorter horizon following the energy planned by the upper layer. In extensive numerical studies, we study the impacts of different parameters on the achievable costs. Moreover, the proposed design is compared with different baselines using different cost models in the two layers and the one-layer MPC-based framework to demonstrate the efficacy of our proposed framework.

Optimization Control of Thermal Efficiency for Cement Raw Meal Pre-Decomposition Based on Two-Layer Structure Model Predictive Control

A two-layer structure model predictive control (MPC) for the low thermal efficiency of cement raw material pre-decomposition is proposed. In the upper layer, an outlet temperature of the precalciner automatic setting scheme is formulated with craft constraints by taking advantage of the energy flow and thermal energy conversion models. In the lower layer, the stable control of the precalciner is achieved using a model predictive control algorithm. Finally, extensive simulation results verify the proposed two-layer MPC approach with excellent performance in energy saving and consumption reduction of raw meal pre-decomposition link.

A Two-Layer Model Predictive Path-Tracking Control With Curvature Adaptive Method for High-Speed Autonomous Driving

A Two-Layer Model Predictive Path-Tracking Control With Curvature Adaptive Method for High-Speed Autonomous Driving

Two-layer Coalitional Model Predictive Control for Parabolic-Trough Collector Fields*

Coalitional control partitions a system into multiple clusters or coalitions that solve independent local subproblems in parallel. This paper presents a two-layer coalitional model predictive control approach for regulation in constrained-coupled subsystems. We formulate a resource allocation mechanism to distribute the coupled constraint so that the global control problem can be solved in a decentralized manner, guaranteeing the satisfaction of the common constraint. In particular, a top layer will calculate the system's partition according to a given criterion and supervise the shared resource allocation. In turn, the lower control layer will calculate the local optimization problems for every coalition in a decentralized fashion, according to the available shared resource determined by the upper layer. This strategy is applied to regulate the outlet temperature of parabolic-trough solar collector fields, which are composed of a set of loops that remain coupled through a global shared resource constraint.

Applications of Advanced Process Control Techniques to an Italian Water Distribution Network

In this paper an application of Advanced Process Control techniques to an Italian water distribution network is presented. Several in-depth hydraulic studies had previously been conducted in order to perform hardware modifications through sectorization procedures. In order to further improve performance, both pressure management of the water distribution system and optimization of pump scheduling problems have been addressed. Net pressure has been minimized through two-layer Model Predictive Control techniques, while advanced logics have been designed for the pumps scheduling. The developed Advanced Process Control system has been successfully installed allowing to reduce water losses and to achieve a significant reduction of operational costs in terms of electric energy and fault rates.

Hierarchical Model Predictive Control for On-Line High-Speed Railway Delay Management and Train Control in a Dynamic Operations Environment

In practice, the operation of high-speed trains is often affected by adverse weather conditions or equipment failures, which result in delays and even cancellations of train services. In this article, a novel two-layer hierarchical model predictive control (MPC) model is proposed for on-line high-speed railway delay management and train control for minimizing train delays and cancellations. The upper layer manages the global objectives of the train operation, that is, minimizing the total train delays and providing guidance for the speed control in the lower layer. The objectives of the lower layer are to satisfy the running time requirements given by the upper layer and to save energy at the same time. The optimization problems in both levels of the hierarchical MPC framework are formulated as small-scale mixed integer linear programming problems, which can be solved efficiently by existing solvers. Particularly, the train control problem is solved in a distributed way for each train. Simulation analysis based on the real-life data of the Beijing–Shanghai high-speed railway shows that the proposed hierarchical MPC framework can meet the real-time requirements and reduce train delays effectively when compared with widely accepted strategies, for example, first-scheduled-first-serve and first-come-first-serve. Moreover, the proposed hierarchical MPC framework also provides good robustness performance for different disturbance scenarios.

QTSL: A Multilayer Control Framework for Managing Capacity, Temperature, Stress, and Losses in Hybrid Balancing Systems

This work deals with the design and validation of a control strategy for hybrid balancing systems (HBSs), an emerging concept that joins battery equalization and hybridization with supercapacitors (SCs) in the same system. To control this system, we propose a two-layer model predictive control (MPC) framework. The first layer determines the optimal state-of-charge (SoC) reference for the SCs considering long load forecasts and simple pack-level battery models. The second MPC layer tracks this reference and performs charge and temperature equalization, employing more complex module-level battery models and short load forecasts. This division of control tasks into two layers, running at different time scales and model complexities, enables us to reduce computational effort with a small loss of control performance. Experimental validation in a small-scale laboratory prototype demonstrates the effectiveness of the proposed approach in reducing charge, temperature, and stress in the battery pack.

Model Predictive Supervisory Control for Integrated Emission Management of Diesel Engines

In this work, a predictive supervisory controller is presented that optimizes the interaction between a diesel engine and its aftertreatment system (ATS). The fuel consumption is minimized while respecting an upper bound on the emitted tailpipe NOx mass. This is achieved by optimally balancing the fuel consumption, the engine-out NOx emissions, and the ATS heating. The proposed predictive supervisory controller employs a two-layer model predictive control structure and solves the optimal control problem using a direct method. Through experimental validation, the resulting controller was shown to reduce the fuel consumption by 1.1% at equivalent tailpipe NOx emissions for the nonroad transient cycle when compared to the operation with a fixed engine calibration. Further, the controller’s robustness to different missions, initial ATS temperatures, NOx limits, and mispredictions was demonstrated.

Module-Based Active Equalization for Battery Packs: A Two-Layer Model Predictive Control Strategy

High-performance and safe operation of a serially connected lithium-ion battery pack in the electric vehicle necessitates effective cell equalization to maintain the state-of-charge of each cell at the same level. In this work, an improved module-based cell-to-pack-to-cell (CPC) equalization system is developed, where the module-level (ML)/cell-to-module-to-cell (CMC) equalizers are utilized for equalization of the battery modules/cells in each module. Compared with the conventional CPC balancing system, it has the advantages of simple modular structure and convenient maintainability. Then, a two-layer model predictive control (MPC) strategy is proposed, in which the ML equalizers are controlled by the top-layer MPC and the controlled CMC equalizing currents in each module are designed by the bottom-layer MPC algorithms in parallel. Its computational complexity is much less than the centralized MPC, which makes it more feasible for real-time cell equalization implementation in practical applications. A rigorous mathematical convergence proof of the proposed equalization control strategy is provided based on the Lyapunov stability theorem. Finally, extensive results are provided to verify the proposed improved module-based CPC equalization system and the two-layer MPC-based equalization control approach with excellent equalization performance being demonstrated.

Reliable Dispatch of Active Distribution Networks via a Two-Layer Grid-Aware Model Predictive Control: Theory and Experimental Validation

Dispatching active distribution networks (ADNs) is an energy-intensive application that, if implemented via battery-energy storage systems (BESSs), can require a large capacity of these assets in order to fully balance the uncertainties caused by the stochastic demand and generation. The insufficient capacity of the BESSs often leads to their state-of-charge (SOC) saturation; this results in unreliable dispatch tracking. In this work, we propose and experimentally validate a real-time control scheme that achieves a highly-reliable dispatching of ADNs and ensures that the BESSs’ SOC is not saturated during the daily operation. Our proposed scheme uses a two-layer model predictive control (MPC). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">upper-layer</i> MPC, running every 5 minutes, optimizes the BESSs’ SOC trajectories while minimizing the tracking error, considering the prosumption forecast of the whole day. Then, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lower layer</i> MPC, running every 30 seconds, takes the BESSs’ SOC trajectories as constraints while achieving a high-resolution tracking of the dispatch plan over the current 5-minute time horizon. Both layers account for the grid constraints by using the augmented relaxed optimal power-flow (AR-OPF) model; an exact convex relaxation of the original AC-OPF and used in this paper (for the first time in the literature) to solve a real-time constrained control problem for ADNs. Our proposed framework was experimentally validated using a 1.5 MVA/2.5 MWh BESS connected to an actual 24-node medium-voltage (MV) ADN that, in Aigle, Switzerland, hosted an uncontrollable 3.2 MWp distributed photovoltaic generation, 3.4 MVA hydro-power generations, and a 2.8 MW base demand.

Steady-state sequence optimization with incremental input constraints in two-layer model predictive control

Steady-state optimization is of vital importance in two-layer model predictive control for bringing better steady-state and dynamic performance. However, the global optimality of steady-state sequences provided by local steady-state optimization cannot be guaranteed. Therefore, a new steady-state sequence optimization approach is proposed in the paper, to improve the global optimality of steady-state sequences. First, the non-global optimality of local steady-state sequences is discussed using an example. Subsequently, aiming at improving the global optimality, a novel sequence optimization strategy designed for steady-state optimization is proposed. Its basic formulation is given and the lower bound of the introduced parameter is analyzed. Then, the relation and difference between the proposed steady-state sequence optimization and the existing global steady-state optimization and local steady-state optimization are discussed. Finally, the steady-state performance, dynamic performance, and computational burden of the proposed approach are studied. The proposed approach provides engineers a brand-new way to realize steady-state optimization and effectively improves the global optimality of calculated steady-state sequences. Extensive simulations verify the effectiveness and reliability of the proposed method.

Automated Vehicle’s Overtaking Maneuver with Yielding to Oncoming Vehicles in Urban Area Based on Model Predictive Control

The rapid development of automated driving technology has brought many emerging technologies. The collision avoidance (CA) function by braking and/or steering maneuver of advanced driver assistance systems (ADAS), which contributes to the improvement of the safety of automated vehicles, has been playing an important role in recent modern passenger cars and commercial vehicles. When an automated vehicle needs to avoid multiple obstacles at the same time, consuming travel time and safety assurance of CA need to be carefully considered especially in the case related to unpredictable motion of obstacles. This paper proposes a feasible solution to this situation by controlling speed and the steering wheel angle. The proposed motion re-planning based on post-encroachment time (PET) provides a judgment of a function which calculates the possibility of unavoidable road accidents. Then the path re-planning layer of a novel two-layer model predictive control (TL-MPC) will re-plan a local trajectory and give a reference acceleration. Finally, the path tracking layer outputs steering wheel angle to follow the trajectory under the premise of ensuring safety constraints. The proposed control system is evaluated by co-simulations of MATLAB/Simulink and CarSim software. The results show that for various conditions of post-encroachment time, the ego vehicle adopting the proposed strategy will conduct reasonable behavior re-planning and consequently successfully avoid obstacles.

Interleaved Model Predictive Control for Three-Level Neutral-Point-Clamped Dual Three-Phase PMSM Drives With Low Switching Frequencies

In this article, the control strategy is studied for the neutral-point-clamped three-level inverter-fed dual three-phase permanent-magnet synchronous motor (PMSM) drive with low switching frequencies. An interleaved finite-control-set model predictive control (MPC) scheme is proposed, where a two-layer MPC is designed to solve the multiobjective optimization problem. The two sets of windings in PMSM are sampled and controlled in an interleaved way so that the control delay and the prediction horizon of the drive system are reduced by half. Moreover, the proposed interleaved control scheme increases the equivalent sampling and control frequency from the perspective of the whole drive system, and thus provides better steady-state performance and dynamic performance. With the switching states of one inverter remaining unchanged, the cross traversal of vector candidate sets between two sets of windings is avoided, and the computational burden can be reduced effectively. Experimental results are given to verify the validity and effectiveness of the proposed interleaved control scheme.