Supervisory Model Predictive Control Research Articles

On the simplification of the internal nonlinear robot models for the MPC-based visual servoing

Visual servoing enables tracking and grasping of static and moving objects and locating regions of interest. Its implementation requires the coordination of low-level control tasks with high-level supervision and planning. The use of the PID-based manipulation control addresses only the dynamical response and requires the use of optimization-based techniques at the supervisory level. On the contrary, model predictive control (MPC) simplifies the design as it combines both tasks within a single algorithm. However, successful MPC implementation depends on the quality of the internal model utilized by the algorithm. Robots are intrinsically and significantly nonlinear control objects, so their models are inherently complex. This leads to computational problems. Standard solutions are to shorten the MPC horizons or to parallelize calculations. The approach suggested here returns to the problem roots, i.e. to the model. This study proposes simplified models that allow the use of the MPC. General Hammerstein–Wiener-like model is applied to an arm, which is further simplified into the linear dynamics used as the predictive supervisory control over the torque control. The head uses linear dynamics used by the supervisory MPC working with the base servomotors. It is shown that they are computationally efficient and sufficiently accurate. It is shown that the linear MPC controllers, which use the suggested simplified dynamic robot models, can successfully support visual servoing with accurate control. They are compared with standard PID-based structures and show their superiority. The proposed approach is successfully validated using Matlab and Gazebo robot simulator and is ultimately confirmed by experiments on a real robot.

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.

Energy Balance in a Standalone PV Battery Hybrid Generation System on Solar-Powered Aircraft Using the Model Predictive Control Method

This paper proposes a battery state of charge (SOC)-based energy management strategy using hierarchical distributed model predictive control (HDMPC) for a standalone microgrid on solar-powered long-endurance aircraft. The microgrid was innovatively designed as a two-layer structure in which the first layer consists of a photovoltaic generation and battery storage system named the PV battery module (PBM). The second layer, named the microgrid subsystem (MGSS), consists of several PBMs, each of which supplies power to a specific DC load on the aircraft. The control system is divided into two levels: the grid-level model predictive control (MPC) and the converter-level MPC. The grid-level MPC adopts a distributed model predictive control strategy to obtain the reference power of each module. The converter-level MPC calculates the control variables of converters using a supervisory model predictive control (SMPC) strategy. The new microgrid structure and the proposed control strategy have improved the reliability of the energy system and increased its energy utilization rate.

Reducing Air Pollutant Emissions and Optimizing Fuel Economy by Controlling Torque and Catalyst warm-up in mild HEV via Cascaded MPC

In this paper, Cascaded MPC (Model Predictive Control) including Supervisory MPC, Heating MPC and Torque MPC is applied to mild HEVs (Hybrid Electric Vehicles) in order to improve fuel economy and reduce NOx emissions. There is a difference in the timescale between Heaitng MPC and Torque MPC, which are connected via the battery. Supervisory MPC treats the power and temperature systems comprehensively and calculates reference values for NOx emissions to be tracked in Heating MPC. In Heating MPC, the optimal amount of heating from the battery to the catalyst in the exhaust gas after-treatment system is determined. The amount of heating must be determined in a way that the catalyst can optimally purify NOx emitted from the engine. At Torque MPC, the optimal torque distribution for the engine and motor is determined. Finally, the effectiveness of our proposed method is demonstrated by simulations.

Supervisory model predictive control for combined electrical and thermal supply with multiple sources and storages

We present the design, implementation, and experimental validation of a supervisory model predictive control approach for the simultaneous optimization of thermal and electrical building energy supplies. The system comprises a heat pump, a photovoltaic system, an electric battery, and a thermal storage tank. The system uses a phase change slurry as both the heat storage and heat transfer medium, which, beyond an increased storage capacity, results in an improved performance of the heat pump and an increased load-shifting potential. In contrast to approaches that use volumetric flow rates as inputs, the supervisory model predictive control used here determines optimal energy flows. As a result, the online optimization problems do not require impractical computational power in spite of the system complexity. We show the control can be implemented on low-cost embedded hardware and validate it with an experimental demonstrator comprising an installation of the complete supply system, including all hydraulic and all electrical components. Experimental results demonstrate the feasibility of both, the heat pump based heating system with a phase change slurry, and the optimal control approach. The main control objectives, i.e., thermal comfort and maximum self-consumption of solar energy, can be met. In addition, the system and its controller provide a load shifting potential.

Design of Supervisory Model Predictive Control for Building HVAC System With Consideration of Peak-Load Shaving and Thermal Comfort

This paper proposes a design of a supervisory model predictive controller for a heating-ventilation-air-conditioning (HVAC) control system. The control objective is to minimize the operating cost and take into account of electrical load shaving and thermal comfort of users. To ensure that thermal comfort is well regulated, we utilize the Predicted Mean Vote (PMV) as an indicator and determine an acceptable bound of a desired set-point temperature. The control design consists of two layers, namely, a supervisory control (SC) layer and a model predictive control (MPC) layer. For the SC layer, we explore a configuration for the SC layer including the choice of predesign controller, the analysis of steady-state response, and the analysis of the possible range of the set-point temperature. We incorporate the effect of set-point temperature, air velocity, outside air temperature, heat load inside zone onto the HVAC electrical power. Then, we search for an optimal profile of the set-point temperature that minimizes a weighted sum of a total operating cost (TOC) and a thermal comfort cost (TCC). Moreover, exploration of trade-off between TOC and TCC helps us to achieve both control objectives efficiently. For the MPC layer, we formulate the control design with the objective of tracking the optimal set-point temperature and minimizing the control inputs. We apply the proposed control design to a complex dynamical model of HVAC system with volumetric flow rate, electrical power of heat exchanger, and removed moisture as control variables. When the allowable PMV of the zone is specified within 0.5, the TOC of the proposed supervisory MPC is reduced by 12.46% compared with that of the nominal operation. The maximum electricity demand of HVAC system is reduced by 13.43% and the electrical power profile is smoothly shaved under the proposed control scheme. Furthermore, the zone relative humidity is well regulated which corresponds to the assumption in the supervisory control.

An Optimization-Based Supervisory Control and Coordination Approach for Solar-Load Balancing in Building Energy Management

This work considers the problem of reducing the cost of electricity to a grid-connected commercial building that integrates on-site solar energy generation, while at the same time reducing the impact of the building loads on the grid. This is achieved through local management of the building’s energy generation-load balance in an effort to increase the feasibility of wide-scale deployment and integration of solar power generation into commercial buildings. To realize this goal, a simulated building model that accounts for on-site solar energy generation, battery storage, electrical vehicle (EV) charging, controllable lighting, and air conditioning is considered, and a supervisory model predictive control (MPC) system is developed to coordinate the building’s generation, loads and storage systems. The main aim of this optimization-based approach is to find a reasonable solution that minimizes the economic cost to the electricity user, while at the same time reducing the impact of the building loads on the grid. To assess this goal, three objective functions are selected, including the peak building load, the net building energy use, and a weighted sum of both the peak load and net energy use. Based on these objective functions, three MPC systems are implemented on the simulated building under scenarios with varying degrees of weather forecasting accuracy. The peak demand, energy cost, and electricity cost are compared for various forecast scenarios for each MPC system formulation, and evaluated in relation to a rules-based control scheme. The MPC systems tested the rules-based scheme based on simulations of a month-long electricity consumption. The performance differences between the individual MPC system formulations are discussed in the context of weather forecasting accuracy, operational costs, and how these impact the potential of on-site solar generation and potential wide-spread solar penetration.

Supervisory predictive control for wheel slip prevention and tracking of desired speed profile in electric trains

This article presents a supervisory model predictive control system to track the desired speed profile and simultaneously prevent the wheels from slipping in acceleration mode of electrical trains. The proposed control strategy employs field-oriented control (FOC) to control the angular speed of the wheel. Model predictive control (MPC) is used to control the longitudinal velocity of the train to track the desired speed profile and prevent the wheels from slipping by generating the desired angular velocity for the FOC. Since, it is not possible to control the longitudinal velocity and slip ratio independently, a fuzzy supervisor system is proposed to control the train dynamics at the appropriate operating point. A method is presented to estimate train longitudinal velocity and the adhesion coefficient between the wheels and rail surface. These components are vital to implement the proposed method in a real train control system. The closed loop stability of the control system has been studied. Simulations were run under different friction coefficients corresponding to real train parameters to verify the effectiveness of the proposed re-adhesion control system. The simulation results have been compared with the results of other researches to show the feasibility and validity of the presented approach.

Non-Linear Tank Level Control for Industrial Applications

Tank level control is ubiquitous in industry. The focus of this paper is on accurate liquid level control in single tank systems which can be actuated continuously and modulation of the level setpoint is also required, for example in cascade control loops or supervisory Model Predictive Control (MPC) applications. To avoid common problems encountered when using fixed gain or adaptive/gain scheduled schemes, an accurate technique based around feedback linearization and Proportional Integral (PI) control is introduced. This simple controller can maintain linear performance over the full operating range of a uniform tank. As will be demonstrated, the implementation overhead compared to a regular PI controller is negligible, making it ideal for industrial implementation. Implementation details and parameter identification for adaptive implementation are discussed. Simulations coupled with experimental results using a large-scale laboratory level control system using commercial industrial control equipment validate the approach, and illustrate its effectiveness for both level tracking and disturbance rejection.

Supervisory Model Predictive Control for Optimal Operation of a Greenhouse Indoor Environment Coping With Food-Energy-Water Nexus

This paper presents a greenhouse indoor environment controller based on model predictive control (MPC), which can be integrated into existing greenhouse regulatory systems to optimally maintain critical climatic variables, including artificial lighting levels, CO2 rate, indoor temperature and humidity level within acceptable limits. The MPC based optimization problem aims to maximize the rate of crop photosynthesis while optimizing the use of the available water and energy sources, taking into account the unpredictability and intermittent nature of renewable energies and external atmospheric conditions. This would facilitate the management of greenhouses by anticipating control actions for a better quality of crops production. For that, the mathematical formulation of the optimal control problem is presented, and the numerical results related to the application of the MPC to case studies are analyzed integrating the effects of greenhouse structural considerations and the influence of climate data on its operation.

Closed-Loop Identification for Model Predictive Control of HVAC Systems: From Input Design to Controller Synthesis

Heating, ventilation, and air conditioning (HVAC) systems are responsible for maintaining occupants’ thermal comfort and share a large portion of the overall building energy use. Hence, it is of great interest to improve the performance of HVAC control systems and thus the building energy efficiency. Model predictive control (MPC) has been proved to be a promising control strategy to be employed in this field. However, MPC implementation relies on the model of the system, and inaccurate models can deteriorate the control performance, while overly complicated models can lead to the prohibitive computational burden. Because of this, existing models do not usually allow the MPC controller to adjust multiple set points (e.g., both temperature and flow rates) and do not include the dynamics of the heating and ventilation subsystems with their local controllers. In this paper, we address the challenge of developing more reliable HVAC models for MPC controllers based on the experimental data. Data are obtained from an experiment designed using a graph theoretical technique, which guarantees maximum information content in the data. The resulting models are employed to design local controllers of the heating and ventilation subsystems, which are experimentally tested in a real HVAC test bed. A supervisory MPC controller that incorporates the closed-loop models of the heating and ventilation subsystems is then developed. This can lead to a control strategy able to more effectively adapt key HVAC set points based on weather conditions, occupancy, and actual thermal comfort, as shown by a numerical study based on the data from the HVAC test bed.

Supervisory Model Predictive Control for Optimal Energy Management of Networked Smart Greenhouses Integrated Microgrid

This paper presents a novel high-level centralized control scheme for a smart network of greenhouses integrated microgrid (NGIM) forming a smart small power grid in the context of smart grids. The main purpose is to present an innovative control strategy-based coordinated model predictive control (MPC) that considers fluctuations of stochastic renewable sources as well as weather conditions. A comprehensive finite-horizon scheduling optimization model is formulated to optimally control the operation of the NGIM, which integrates both forecasts and newly updated information collected from the available sensors at the network level. The model can be implemented as a supervisory control and energy management system for the NGIM to manipulate the indoor climate and optimize the crop production. The cooperation is reached through a bidirectional communication infrastructure, where a master central controller is available at the network level and is in charge of coordinating and managing various control signals. An MPC-based algorithm is used for the future operation scheduling of all subsystems available in the NGIM. The MPC strategy is tested through a case study where the influences of climate data on the operation of the NGIM are analyzed via numerical results. Note to Practitioners —Under the smart grid paradigm, smart greenhouses can be taken as an alternative that can mitigate and face the development challenges of the agricultural sector. Smart greenhouses can be considered as active players that may play a key role in modernizing the agriculture by offering viable and new smart management solutions, advanced control strategies, and innovative decision-support tools, whose objective is to better support growers, investors, and professionals. Smart network of greenhouses integrated microgrid (NGIM) can play an increasing role in enhancing the sustainable energy supply in the agricultural sector. In addition, the incorporation of new information and communication technologies, advanced metering infrastructure, and optimal control strategies can support the agricultural sector to meet an increasing number of regulations on quality and environment. In this paper, a comprehensive scheduling optimization model-based MPC that considers fluctuations of stochastic renewable sources, as well as weather conditions, is formulated to optimally control the operation of the NGIM. We developed and validated an intelligent control and management system-based MPC algorithm for a NGIM that may be considered as a practical solution to mitigate and address the development challenges and support the transition to precision and sustainable agriculture as well as the modernization of the agriculture.

Coordinated predictive control for wind farm with BESS considering power dispatching and equipment ageing

This study presents a supervisory model predictive control (MPC) scheme for coordinated active power control of a wind farm with a centralised battery energy storage system (BESS). The control target is wind turbine generator (WTG) and BESS coordination to respond to transmission system operator power dispatching, along with equipment ageing deceleration. To alleviate the WTG mechanical fatigue and extend the BESS expected lifetime, the twist angle variation and an index introduced by a weighted ampere-hour (Ah) throughput model are considered in the cost function. The damage equivalent load and weighted Ah throughput are used for the equipment ageing evaluation. Simulations and comparisons are conducted to demonstrate the effectiveness and applicability of the proposed MPC-based supervisory controller design.

Model predictive control of fuel cells system within hybrid renewable energy generation

This paper presents model predictive control (MPC) strategies with a shrinking horizon approach to track local control systems when subject to supervisory trajectories. The supervisory trajectories are generated using economic receding horizon optimization based on energy management in energy-intensive industries (e.g., chlor-alkali process) with a hybrid renewable energy system (HRES), including solar, wind, and fuel cell sub-systems to provide sustainable power supply. A planer solid oxide fuel cell system is adopted in this study, and its power output is regulated using a constrained shrinking horizon MPC controller. The feasibility of MPC control algorithm in regulating energy sub-systems within a supervisory MPC framework will be studied and evaluated at different parameters. The main contribution of this paper is to provide practical control options when addressing technical viability concerns of hybrid energy system implementation.

Supervisory Model Predictive Control of the Heat Integrated Distillation Column

This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized control system outperforms the decentralized system, because it handles the interactions in the HIDiC process better. The integral absolute error (IAE) is reduced by a factor of 2 and a factor of 4 for control of the top and bottoms compositions, respectively.

Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system

In this paper, a comprehensive review of the artificial neural network (ANN) based model predictive control (MPC) system design is carried out followed by a case study in which ANN models of a residential house located in Ontario, Canada are developed and calibrated with the data measured from site. A new algorithm called best network after multiple iterations (BNMI) is introduced to help in determining the appropriate ANN architecture. The prediction performance of the developed models using BNMI algorithm was significantly better (between 6% and 59% better goodness of fit for various models) when compared to a previous study carried out by the authors which used the default single iteration ANN training algorithm of MATLAB®. The ANN models were further used to design the supervisory MPC for the residential HVAC system. The MPC generated the dynamic temperature set-point profiles of the zone air and buffer tank water which resulted in the operating cost reduction of the equipment without violating the thermal comfort constraints. When compared to the fixed set-point (FSP), MPC was able to save operating cost between 6% and 73% depending on the season.

Trial results from a model predictive control and optimisation system for commercial building HVAC

This paper presents the results from two real-world trials of an optimised supervisory model predictive control (MPC) system for heating, ventilation and air conditioning (HVAC) in commercial buildings. The system learns a model from historical data and uses weather forecasts and a given temperature set-point profile to predict building zone conditions and thermal comfort with the aim of optimising building controls for a number of HVAC zones throughout a day. The multi-objective optimisation minimises running cost and CO2 emissions, subject to operator preferences, while constraining occupant thermal discomfort to an acceptable range to find the best zone temperature set-point schedule for the building. This schedule is then applied to the building by a feedback control loop, which balances the power supplied to each zone for heating, cooling and ventilation.A complementary online occupant comfort feedback tool was deployed to all occupant computers in the trial office buildings. This tool allows occupants to submit feedback on their thermal comfort and satisfaction at any point in time via electronic surveys, as well as allowing these surveys to be issued to occupants at scheduled times. This feedback fine-tuned the thermal comfort model used to constrain the optimisation, allowing for errors in the comfort model to be compensated. Thermal comfort feedback was also used to measure and compare relative occupant comfort levels with a baseline.This control system was trialled on two office buildings in Australia, over two winter months and results compared with the performance of the incumbent building management and control system (BMCS). An average energy reduction of 19% and 32% was achieved in the two buildings over 51 and 10 days of operation respectively without substantially affecting measured or modelled occupant thermal satisfaction levels.

Plant-wide hierarchical optimization and control of an industrial hydrocracking process

Hydrocracking is a crucial refinery process in which heavy hydrocarbons are converted to more valuable, low-molecular weight products. Hydrocracking plants operate with large throughputs and varying feedstocks. In addition the product specifications change due to varying economic and market conditions. In such a dynamic operating environment, the potential gains of real-time optimization (RTO) and control are quite high. At the same time, real-time optimization of hydrocracking plants is a challenging task. A complex network of reactions, which are difficult to characterize, takes place in the hydrocracker. The reactor effluent affects the operation of the fractionator downstream and the properties of the final products. In this paper, a lumped first-principles reactor model and an empirical fractionation model are used to predict the product distribution and properties on-line. Both models have been built and validated using industrial data. A cascaded model predictive control (MPC) structure is developed in order to operate both the reactor and fractionation column at maximum profit. In this cascade structure, reactor and fractionation units are controlled by local decentralized MPC controllers whose set-points are manipulated by a supervisory MPC controller. The coordinating action of the supervisory MPC controller accomplishes the transition between different optimum operating conditions and helps to reject disturbances without violating any constraints. Simulations illustrate the applicability of the proposed method on the industrial process.

Learning-based tuning of supervisory model predictive control for drinking water networks

This paper presents a constrained Model Predictive Control (MPC) strategy enriched with soft-control techniques as neural networks and fuzzy logic, to incorporate self-tuning capabilities and reliability aspects for the management of drinking water networks (DWNs). The control system architecture consists in a multilayer controller with three hierarchical layers: learning and planning layer, supervision and adaptation layer, and feedback control layer. Results of applying the proposed approach to the Barcelona DWN show that the quasi-explicit nature of the proposed adaptive predictive controller leads to improve the computational time, especially when the complexity of the problem structure can vary while tuning the receding horizons.

Distributed Supervisory Predictive Control of Distributed Wind and Solar Energy Systems

In this work, we design a distributed supervisory model predictive control (MPC) system for optimal management and operation of distributed wind and solar energy generation systems integrated into the electrical grid to facilitate the development of the so-called “smart electrical grid”. We consider a topology in which two spatially distributed energy generation systems, a wind subsystem and a solar subsystem, are integrated in a DC power grid, providing electrical power to a local area, and each subsystem is coupled with an energy storage device. A supervisory MPC optimization problem is first formulated to take into account optimality considerations on system operation and battery maintenance; then a sequential and an iterative distributed supervisory MPC architectures are developed to coordinate the actions of the subsystems accordingly. Simulations of 24-hour system operation are carried out under the different control architectures to demonstrate the applicability and effectiveness of the distributed supervisory predictive control design.