Optimization-based Model Predictive Control Research Articles

Predictive Energy Management of a Building-Integrated Microgrid: A Case Study

The efficient integration of distributed energy resources (DERs) in buildings is a challenge that can be addressed through the deployment of multienergy microgrids (MGs). In this context, the Interreg SUDOE project IMPROVEMENT was launched at the end of the year 2019 with the aim of developing efficient solutions allowing public buildings with critical loads to be turned into net-zero-energy buildings (nZEBs). The work presented in this paper deals with the development of a predictive energy management system (PEMS) for the management of thermal resources and users’ thermal comfort in public buildings. Optimization-based/optimization-free model predictive control (MPC) algorithms are presented and validated in simulations using data collected in a public building equipped with a multienergy MG. Models of the thermal MG components were developed. The strategy currently used in the building relies on proportional–integral–derivative (PID) and rule-based (RB) controllers. The interconnection between the thermal part and the electrical part of the building-integrated MG is managed by taking advantage of the solar photovoltaic (PV) power generation surplus. The optimization-based MPC EMS has the best performance but is rather computationally expensive. The optimization-free MPC EMS is slightly less efficient but has a significantly reduced computational cost, making it the best solution for in situ implementation.

Distributionally Robust Optimization Based Model Predictive Control for Stochastic Mixed Traffic Flow

Distributionally Robust Optimization Based Model Predictive Control for Stochastic Mixed Traffic Flow

PSO-Based Model Predictive Control for Load Frequency Regulation with Wind Turbines

With the high penetration of wind turbines, many issues need to be addressed in relation to load frequency control (LFC) to ensure the stable operation of power grids. The particle swarm optimization-based model predictive control (PSO-MPC) approach is presented to address this issue in the context of LFC with the participation of wind turbines. The classical MPC model was modified to incorporate the particle swarm optimization algorithm for the power generation model to regulate the system frequency. In addition to addressing the unpredictability of wind turbine generation, the presented PSO-MPC strategy not only addresses the randomness of wind turbine generation, but also reduces the computation burden of traditional MPC. The simulation results validate the effectiveness and feasibility of the PSO-MPC approach as compared with other state-of-the-art strategies.

A hybrid constrained Particle Swarm Optimization-Model Predictive Control (CPSO-MPC) algorithm for storage energy management optimization problem in micro-grid

This paper investigates energy management systems in micro-grid using an optimization-based approach, optimizing the operating cost related to the energy purchased from the utility grid, the operation cost of the energy storage system, and revenue from the selling of energy to the utility grid. This research uses a constrained Particle Swarm Optimization-Based Model Predictive Control (CPSO-MPC) and a Linear Program-Based Optimization approach to solve the constrained optimization problem formulated in micro-grid energy management. Due to the absence of constraint management strategies in the traditional PSO algorithm, it is incapable of solving constrained optimization problems. Hence, to overcome this drawback, an intuitive approach known as Deb’s rule is applied to handle the constraints. The simulation results show the modified particle swarm optimization’s effective performance embedded in the model predictive control algorithm compared to the linear programming algorithm.

Hierarchical Optimization-Based Model Predictive Control for a Class of Discrete Fuzzy Large-Scale Systems Considering Time-Varying Delays and Disturbances

In this manuscript, model predictive control for class of discrete fuzzy large-scale systems subjected to bounded time-varying delay and disturbances is studied. The considered method is Razumikhin for time-varying delay large-scale systems, in which it includes a Lyapunov function associated with the original non-augmented state space of system dynamics in comparison with the Krasovskii method. As a rule, the Razumikhin method has a perfect potential to avoid the inherent complexity of the Krasovskii method especially in the presence of large delays and disturbances. The considered large-scale system in this manuscript is decomposed into several subsystems, each of which is represented by a fuzzy Takagi–Sugeno (T-S) model and the interconnection between any two subsystems is considered. Because the main section of the model predictive control is optimization, the hierarchical scheme is performed for the optimization problem. Furthermore, persistent disturbances are considered that robust positive invariance and input-to-state stability under such circumstances are studied. The linear matrix inequalities (LMIs) method is performed for our computations. So the closed-loop large-scale system is asymptotically stable. Ultimately, by two examples, the effectiveness of the proposed method is illustrated, and a comparison with other papers is made by remarks.

Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch

Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency variable instead of the local voltage for adjusting loads, which has led to obstacles in operating DC microgrids in the context of increasingly rising power electronic loads. Moreover, the consideration of voltage-dependent demand response and optimal battery dispatch has posed challenges for the traditional planning methods, such as stochastic programming, because of nonlinear constraints. Considering these facts, this paper proposes a model predictive control-based integrated voltage-based demand response and batteries’ optimal dispatch operation for minimizing the entire DC microgrid’s operating cost. In the proposed model predictive control approach, the binary decisions about voltage-dependent demand response and charging or discharging status of storage batteries are determined using a deep-Q network-based reinforcement learning method to handle uncertainties in various operating conditions (e.g., AC grid-connect faults and DC sources variations). It also helps to improve the DC microgrid operation efficiency in the two aspects: continuously avoiding load shedding or shifting and reducing the batteries’ charge and discharge cycles to prolong their service life. Finally, the proposed method is validated by comparing to the stochastic programming-based model predictive control method. Simulation results show that the proposed method obtains convergence with approximately 41.95% smaller operating cost than the stochastic optimization-based model predictive control method.

Carbon Footprint Analysis of a Laboratory Plate Heat Exchanger Control

More than 100 countries worldwide have pledged carbon neutrality by 2050 to reduce the negative environmental impacts of carbon emissions. To reach this goal, every part of the industry must prepare to transform and take precautions. Heat exchangers are one of the most common parts of industrial production. The industrial operation of heat exchangers still offers lots of opportunities to minimize their carbon footprints. This goal can be achieved by their optimized operation. This work investigates the operation of a laboratory plate heat exchanger controlled by a proportional-integral-derivative (PID) controller and by an optimization-based model predictive controller (MPC). The analysis is performed using both, the simulations, and the laboratory experiments. The carbon footprint and energy consumption of the heat exchanger are analysed and compared. The paper investigates whether the optimization-based approach subject to the constraints on manipulated variables and controlled variables, considering economic criteria, simultaneously, reduces the carbon footprint of a laboratory heat exchanger control.

Online predictive connected and automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions

For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic control devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project.

Ethical Decision-Making Platform in Autonomous Vehicles With Lexicographic Optimization Based Model Predictive Controller

Ethical decision-making during inevitable crashes, especially when humans involved, has become a big and sensitive roadblock for future mass adoption of autonomous vehicles. Towards addressing this challenge, this paper proposes a predictive control framework for ethical decision-making in autonomous driving using rational ethics. For flexibly implementing of ethical rules, the Lexicographic Optimization-based model predictive controller (LO-MPC) has been designed, in which obstacles and constraints are prioritized. Simulation environment is set up in PreScan, with different edge cases. The results show that the proposed LO-MPC approach has the capability to deal with the ethical decision-making during inevitable crashes by avoiding the obstacles with the assumed priority orders compared with traditional decision-making algorithm.

A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions

Motivated by connected and automated vehicle (CAV) technologies, this paper proposes a data-driven optimization-based Model Predictive Control (MPC) modeling framework for the Cooperative Adaptive Cruise Control (CACC) of a string of CAVs under uncertain traffic conditions. The proposed data-driven optimization-based MPC modeling framework aims to improve the stability, robustness, and safety of longitudinal cooperative automated driving involving a string of CAVs under uncertain traffic conditions using Vehicle-to-Vehicle (V2V) data. Based on an online learning-based driving dynamics prediction model, we predict the uncertain driving states of the vehicles preceding the controlled CAVs. With the predicted driving states of the preceding vehicles, we solve a constrained Finite-Horizon Optimal Control problem to predict the uncertain driving states of the controlled CAVs. To obtain the optimal acceleration or deceleration commands for the CAVs under uncertainties, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e. a special case of data-driven optimization models under moment bounds) with a Distributionally Robust Chance Constraint (DRCC). The predicted uncertain driving states of the immediately preceding vehicles and the controlled CAVs will be utilized in the safety constraint and the reference driving states of the DRSO-DRCC model. To solve the minimax program of the DRSO-DRCC model, we reformulate the relaxed dual problem as a Semidefinite Program (SDP) of the original DRSO-DRCC model based on the strong duality theory and the Semidefinite Relaxation technique. In addition, we propose two methods for solving the relaxed SDP problem. We use Next Generation Simulation (NGSIM) data to demonstrate the proposed model in numerical experiments. The experimental results and analyses demonstrate that the proposed model can obtain string-stable, robust, and safe longitudinal cooperative automated driving control of CAVs by proper settings, including the driving-dynamics prediction model, prediction horizon lengths, and time headways. Computational analyses are conducted to validate the efficiency of the proposed methods for solving the DRSO-DRCC model for real-time automated driving applications within proper settings.

Two-Stage Optimization-Based Model Predictive Control of 5L-ANPC Converter-Fed PMSM Drives

Balancing the dc-link voltage and three flying-capacitor (FC) voltages while maintaining very fast control of the motor is always a challenging task in five-level active neutral-point-clamped (5L-ANPC) converter-fed permanent-magnet synchronous motor drives, especially when the FC is small. Besides, the fundamental-frequency operation of the low-frequency cell (LFC) is also required to reduce the losses of the converter. This article proposed a two-stage optimization-based model predictive control (MPC) scheme to achieve these goals. In the first stage, the switching states of the LFC are selected based on the sign of the desired output voltage through a deadbeat approach for its fundamental-frequency operation; in the second stage, the optimal duty cycles for the high-frequency cell are calculated by the multiple-vector MPC and generated by phase-shifted pulsewidth modulation for its fixed-switching frequency operation. The FC and dc-link capacitor voltage balance can be managed by the flexibility of the inherent redundancy in the 5L-ANPC converter. Instead of 512, only six switching vectors must be considered, which significantly simplifies the computational burden of MPC. A thorough experimental evaluation has been conducted to validate the effectiveness of the proposed MPC method.

Online optimization based model predictive control on two wheel Segway system

The current work explores the modelling, control and realistic execution of nonlinear segway system in both simulation andreal-time. The Segway vehiclehas been consideredas a physical system that is nonlinear and unstable. Equations of motions have been derivedusing Lagrangian dynamics by considering kinetic energy and potential energy. Then, the nonlinear differential equations have linearised using Taylor series expansion of functions to acquire a state space model of the system and convert it into two transfer functions that corresponds to tilt angle and yaw angle. These are the controlparametersto stabilize the Segway system. Effective simulation analysis of nonlinear controlis madeup with PID, GA Tuned PID and Model Predictive controller (MPC) byMATLAB. And also, an electro-mechanical model of Segway vehiclehas developedforreal-timecontrol analysis. It is autonomously sensed and actuated through embedded processor tomeetthe desired response. An online optimization techniquehas involvedsolving dynamic optimization problems in MPC for superior performance. This innovative realisation paves the approach for the young researchers to consider the essence of analysis in nonlinear applications.

Encrypted MPC based on ADMM real-time iterations

Encrypted control enables confidential controller evaluations in cloud-based or networked control systems. Technically, an encrypted controller is a modified control algorithm that is capable of computing encrypted control actions based on encrypted system states without intermediate decryption. The realization of such controllers, e.g., using homomorphic encryption, is non-trivial. Nevertheless, even optimization-based model predictive control (MPC) has already been implemented in an encrypted fashion. However, the existing schemes either require an explicit solution of the parametric optimal control problem (OCP) or they can only consider input constraints. In this paper, we present a novel encrypted MPC that allows to include state and input constraints without the requirement of an explicit solution of the OCP. The approach builds on the encrypted implementation of a single iteration of the alternating direction method of multipliers (ADMM) per time step, i.e., ADMM real-time iterations.

Multi-Time-Scale Coordinated Operation of a Combined System with Wind-Solar-Thermal-Hydro Power and Battery Units

The grid connection of intermittent energy sources such as wind power and photovoltaic power generation brings new challenges for the economic and safe operation of renewable power systems. To address these challenges, a multi-time-scale active power coordinated operation method, consisting of day-ahead scheduling, hour-level rolling corrective scheduling, and real-time corrective scheduling, is proposed for the combined operation of wind-photovoltaic-thermal-hydro power and battery (WPTHB) to handle renewable power fluctuations. In day-ahead scheduling, the optimal power outputs of thermal power units, hydro-pumped storage units, and batteries are solved with the purpose of minimizing the total power generation cost. In hour-level rolling corrective scheduling, the power output plan of thermal power units and pumped storage units is modified to minimize the correction cost based on the on-off state of thermal power units determined in day-ahead scheduling. In real-time corrective scheduling stage, the feedback correction and rolling optimization-based model predictive control algorithm is adopted to modify the power output of thermal power units, hydro-pumped storage units, and batteries optimized in hour-level rolling correction scheduling, so as to ensure the economy of the correction plan and the static security of system operation. Finally, simulation results demonstrated that the proposed method can accurately track system power fluctuations, and ensure the economic and security operation of a multi-energy-generation system.

Agile manoeuvres using model predictive control

Abstract This paper shows how model predictive control (MPC) can be used to perform agile manoeuvres in a pendulum-on-a-cart system, which is an abstraction of many mechanical systems commonly used in the industry, such as cranes. Specifically, the problem of moving a cart on which a pendulum is mounted using a free joint is rapidly moved from one position to another one while mitigating the swings of the pendulum. To achieve this goal, an optimization-based MPC strategy is employed that selects the control moves while minimizing the chosen cost function and, simultaneously, enforcing constraint satisfaction. As the controlled system is nonlinear, two options are considered. The first one solves the nonlinear MPC problem in an approximate fashion using the so-called random shooting approach. The second method is based on the first one approximating the nonlinear system by a linear one, followed by applying convex MPC techniques. The performance of both strategies was compared by means of real-time experiments.

Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller

This article presents a new energy management system (EMS) for a variety of energy flexibility conversion, routing and storage options in buildings. The EMS uses an efficient nonlinear optimization-based model-predictive control (MPC) method, which achieves low computational complexity by utilizing successive linear programming (SLP) for continuous approximations of discrete (two-level) control problems. Whole-year simulation runs demonstrate that the method is applicable to a residential building system that has multiple energy generation, conversion and storage units with significant nonlinear interactions. Both qualitative and quantitative comparison of the simulation results with a rule-based reference control showed strong dependencies between cost and CO2 emission flexibility goals, energy selling prices and forecasting accuracy. This study shows that significant cost savings can be obtained by taking advantage of energy price fluctuations, increasing the average coefficient of performance (COP) of the heating system, and reducing passive losses in heat storage. In the simulated case study the EMS was able to improve the average COP of a heating system from 2.20 to 2.43–2.74, depending on energy cost assumptions, when compared against a rule-based control (RBC). With a performance bound of perfect forecasting the EMS was able to improve net economic outcome by 38–168%, or by 21–75% of the cost of imported electricity.

Neural-Dynamic Optimization-Based Model Predictive Control for Tracking and Formation of Nonholonomic Multirobot Systems.

In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for the multiple nonholonomic mobile robots formation. First, a model-based monocular vision method is developed to obtain the location information of the leader. Then, a separation-bearing-orientation scheme (SBOS) control strategy is proposed. During the formation motion, the leader robot is controlled to track the desired trajectory and the desired leader-follower relationship can be maintained through the SBOS method. Finally, the model predictive control (MPC) is utilized to maintain the desired leader-follower relationship. To solve the MPC generated constrained quadratic programming problem, the neural-dynamic optimization approach is used to search for the global optimal solution. Compared to other existing formation control approaches, the proposed solution is that the NMPC scheme exploit prime-dual neural network for online optimization. Finally, by using several actual mobile robots, the effectiveness of the proposed approach has been verified through the experimental studies.

Identification of the cooling system of a large business center

This paper deals with the development of a control oriented model for a real cooling station serving a large business/commercial area with about 65,000 square meters of spaces and 3,000 permanent workplaces. The model is derived by focusing on one of the subsystems at a time, eventually simulating the physical closed-loop present in the plant. Since first principle equations would be impractical in view of the dimension and uncertainty of the system, the model largely relies on black-box relations estimated from the available measurements. The final model can be used for the design of an optimization-based model predictive control algorithm.

Hierarchical Voltage Control of Weak Subtransmission Networks With High Penetration of Wind Power

In this paper, we propose a novel coordinated control framework to handle negative voltage impacts caused by wind power in a weak subtransmission system. The designed controller addresses the problem of optimal coordination of controls with different response time and control characteristics. In our control design, the traditional voltage regulators (TVRs) such as on-load tap changers and capacitors are primarily used to regulate voltages in the network. If the control actions of TVRs cannot meet the control requirements, an event-triggered load-side aggregate controller will be activated immediately to help regulate voltages. The control schedules of TVRs are calculated through a multitime-step optimization-based model-predictive control method and are updated with the help of wind power predictions. The load-side controller is realized by controlling active power consumptions of load aggregators through an optimal distributed algorithm that relies on local information exchanges between neighboring load aggregators. The effectiveness of the proposed control scheme is tested through a case study on a modified IEEE 30-bus test system with high penetration of wind power.

New results on semi-explicit and almost explicit MPC algorithms

Abstract New results on a particular type of state-dependent parameterization for model predictive control (MPC) are presented. Based on such parameterizations efficient MPC algorithms can be formulated, which combine the advantages of explicit and online optimization-based MPC. The new results comprise an offline stability check for the parameterizations to decide if a closed-loop MPC scheme applying the parameterizations is asymptotically stabilizing. Second, a novel way of computing the parameterizations with improved scalability in the state space dimension is included. Furthermore, new results and simplifications of almost explicit MPC schemes based on univariate parameterizations are contributed. The results are presented in a common framework and are illustrated in numerical examples including an almost explicit controller for an eight-dimensional spring-damper system.