Grey-Box RC Building Models for Intelligent Management of Large-Scale Energy Flexibility: From Mass Modeling to Decentralized Digital Twins

Model predictive controls for residential buildings with heat pumps: Experimentally validated archetypes to simplify the large-scale application

An application of economic model predictive control to inventory management in hospitals

This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations.

This paper investigates the application of Model Predictive Control (MPC) to fast systems such as Autonomous Ground Vehicles (AGV) or mobile robots. The control of Autonomous ground vehicles (AGV) is challenging because of nonholonomic constraints, uncertainties, speed, accuracy of controls and the vehicle's terrain of operation. Two nonlinear models: a car-like model and a bicycle model are considered. A Nonlinear MPC (NMPC) was developed. A trajectory tracking performance index for both models was studied. After thorough and extensive simulation, it is observed that both models are applicable in the context of NMPC and the constraints on model variables were adequately respected. The trajectories were successfully tracked and thus clearly indicate the efficiency and effectiveness of the MPC technique. In order to improve on speed and reduce the computational effort required for the optimization problem, a Linear MPC (LMPC) was implemented with both models. This is possible by successive linearization along the reference trajectory and formulating a quadratic optimization problem which is solved by implementing an interior-point quadratic programming algorithm. For both AGV models, analysis concerning the reduced computational efforts is presented in order to

In this paper, we present our first results from an industrial application of model predictive control (MPC) with real-time steady-state target optimization (RTO) for control of an industrial spray dryer that produces enriched milk powder. The MPC algorithm is based on a continuous-time transfer function model identified from data and states estimated by a time-varying Kalman filter. The RTO layer utilizes the same linear model and a nonlinear economic objective function for calculation of the economically optimized targets. We demonstrate, by industrial application of the MPC, that this method provides significantly better control of the residual moisture content, increases the throughput and decreases the energy consumption compared to conventional PI-control. The MPC operates the spray dryer closer to the residual moisture constraint of the powder product. Thus, the same amount of feed produces more powder product by increasing the average water content. The value of this is 186,000 €/year. In addition, the energy savings account to 6,900 €/year.

This paper addresses the practical applicability of dual mode infinite horizon model predictive control (MPC) strategies for a variety of stationary and mobile transmission power control problems that arise naturally in wireless sensor network (WSN) systems. The key performance requirement is to maximise battery lifetime while also preserving sufficient Quality of Service (QoS) among all users. This is achieved by implementing a closed-loop MPC mechanism within a nominal state-space tracking error based dynamic model that uses the received signal strength related to the signal-to-interference-plus-noise ratio (SINR) as a state feedback signal. The resulting controller is compared with a number of proven strategies and is experimentally validated using an IEEE 802.15.4 WSN test bed and across a number of different test scenarios.

Model Predictive Control In Solar Trough Plants: A Review

The emergency control of Menglou~Qifang inverted siphon, which is about 72 km long, is the key to the safety of the Northern Hubei Water Transfer Project. Given the complicated layout of this project, traditional emergency control method has been challenged with the fast hydraulic transient characteristics of pressurized flow. This paper describes the application of model predictive control (MPC), a popular automatic control algorithm advanced in explicitly accounting for various constraints and optimizing control operation, in emergency condition. For the fast prediction to the pipe-canal combination system, a linear model for large-scale inverted siphon proposed by the latest research and the integrator-delay (ID) model for open canals are used. Simulation results show that the proposed MPC algorithm has promising performance on guaranteeing the safety of the project when there are sudden flow obstruction incidents of varying degrees downstream. Compared with control groups, the peak pressure can be reduced by 17.2 m by MPC under the most critical scenario, albeit with more complicated gates operations and more water release (up to 9.75 × 104 m3). Based on the linear model for long inverted siphon, this work highlights the applicability of MPC in the emergency control of large-scale pipe-canal combination system.

In this paper, a centralized model predictive control (MPC) is applied on a group of interconnected microgrids (MGs) with the main grid. The objective is to maximize the benefits for all the elements constituting the MGs in addition to the benefits of the main grid. The application of MPC in our study needs a forecasting information about energy prices, production power, and loads. The algorithm is tested on five interconnected MGs connected to the main grid. Results have shown the performance of the proposed algorithm, especially for the benefits of MG owners, the coordination between MGs while respecting of the constraints related to each one of them.

Model predictive control (MPC) has been extensively studied in academia and widely accepted in industry. This research has focused on the novel formulation of model predictive controllers for systems that can be decomposed according to their nonlinearity properties and several novel MPC applications including bioreactors modeled by population balance equations (PBE), gas pipeline networks, and cryogenic distillation columns. Two applications from air separation industries are studied. A representative gas pipeline network is modeled based on first principles. The full-order model is ill-conditioned, and reduced-order models are constructed using time-scale decomposition arguments. A linear model predictive control (LMPC) strategy is then developed based on the reduced-order model. The second application is a cryogenic distillation column. A low-order dynamic model based on nonlinear wave theory is developed by tracking the movement of the wave front. The low-order model is compared to a first-principles model developed with the commercial simulator HYSYS.Plant. On-line model adaptation is proposed to overcome the most restrictive modeling assumption. Extensions for multiple column modeling and nonlinear model predictive control (NMPC) also are discussed. The third application is a continuous yeast bioreactor. The autonomous oscillations phenomenon is modeled by coupling PBE model of the cell mass distribution to the rate limiting substrate mass balance. A controller design model is obtained by linearizing and temporally discretizing the ODES derived from spatial discretization of the PBE model. The MPC controller regulate the discretized cell number distribution by manipulating the dilution rate and the feed substrate concentration. A novel plant-wide control strategy is developed based on integration of LMPC and NMPC. It is motivated by the fact that most plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems. LMPCs and NMPCs are applied to the respective subsystems. A sequential solution algorithm is developed to minimize the amount of unknown information in the MPC design. Three coordination approaches are developed to reduce the amount of information unavailable due to the sequential MPC solution of the coupled subsystems and applied to a reaction/separation process. Furthermore, a multi-rate approach is developed to exploit time-scale differences in the subsystems.

This manuscript introduces the application of Model Predictive Control (MPC) for high force control precision in a real industrial electro-hydraulic servo system (EHSS). Moreover, it presents a fractional order control (FOC) and conventional controllers (CC) based on genetic algorithm (GA). The GA technique has been used to tune the parameters of FOC and CC approach. In order to verify the ability of the proposed controller applied to the hydraulic press machines emulator using EHSS, a hardware implementation of a test press system is also suggested and setup to be used in this research. As a result, the study has been investigated using a simulation model then verified via the experimental implementation. In fact, the EHSS plays an important role in many industrial applications, especially in flight simulators, aircraft landing gear system, material testing machine and hydraulic press machines for which the high accuracy and fast response of the force or pressure control are exceedingly necessary. Real-time experiments on the EHSS are carried out to evaluate the proposed control approach in a large system parameters variation of working environments. Considerable improvement in the performance generated by the designed MPC controller is compared with the traditional and fractional order controllers. Moreover, the results show that the performance criteria in terms of settling, rise times, system overshoots, system parameters variation and applying different test signals are good values in case of applying MPC over using FOC and CC in this study. As a general conclusion, one can conclude that the MPC has the priority of applying it in the field of the industrial EHSS. The obtained results are promising in the field of mechatronic.

A resolver is an angular position sensor used in applications that demands robustness and reliability, such as electric vehicles. However, getting the angular position from resolver output signals is a difficult task, and many algorithms were proposed to achieve that task. This paper describes the application of model predictive control (MPC) to get the angular position from resolver signals. Synchronous demodulation is used to get the envelopes of the resolver outputs and get the estimation error. The structure of the conventional model predictive controller was modified to be used as an angle tracking observer (ATO). Simulations show the performance of the proposed approach. According to the bibliographic review of the authors, it is the first time that model predictive control is applied as an observer to get the angular position from resolver signals.

This paper describes the application of model predictive control (MPC) to current control system of permanent magnet synchronous motor (PMSM). Proposed current control system considers switching states of inverter as controller output, while conventional one regards the inverter as ideal amplifier. The problem with deciding the switching state to output is formulated based on MPC, which uses the mathematical model of PMSM for prediction of future motor current and the objective function deciding the optimal switching state. This function is based on not only minimization of the error between the predicted motor current and the current reference, but actual requirements for current control system such as reduction of inverter switching times. Simulation results show that current transient response improves by proposed system, especially at the region where voltage saturation occurs.

In this paper, an application of Model Predictive Control (MPC) to physical human-machine interaction is presented. In particular, the study focuses on the development of a mechatronic device able to apply a well-controlled mechanical impulsive force on the human body, for clinical investigation of postural control. The need for high accuracy and repeatability led to the MPC design, which is able to manage the non-linearities related to the human-machine interaction. The hardware architecture design of the prototype, the development of the control system (based on motor current saturation) and its optimization are presented. The results of experimental trials carried out in the laboratory and on healthy subjects show that the MPC algorithm is able to provide the accuracy and robustness requested by the application.KeywordsModel Predictive ControlLinear electric actuatorHuman-machine interactionForce controlSpeed controlAutomated posturography

Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives