Model-based Predictive Control Research Articles

Hybrid model-based predictive HVAC control through fast prediction of transient indoor temperature fields

Hybrid model-based predictive HVAC control through fast prediction of transient indoor temperature fields

Modeling the Dynamics of Prosthetic Fingers for the Development of Predictive Control Algorithms

In the field of biomechanical modeling, the development of a prosthetic hand with dexterity comparable to the human hand is a multidisciplinary challenge involving complex mechatronic systems, intuitive control schemes, and effective body interfaces. Most current commercial prostheses offer limited functionality, typically only one or two degrees of freedom (DoF), resulting in reduced user adoption due to discomfort and lack of functionality. This research aims to design a computationally efficient low-level control algorithm for prosthetic hand fingers to be able to (a) accurately manage finger positions, (b) anticipate future information, and (c) minimize power consumption. The methodology employed is known as model-based predictive control (MBPC) and starts with the application of linear identification techniques to model the system dynamics. Then, the identified model is used to implement a generalized predictive control (GPC) algorithm, which optimizes the control effort and system performance. A test bench is used for experimental validation, and the results demonstrate that the proposed control scheme significantly improves the prosthesis’ dexterity and energy efficiency, enhancing its potential for daily use by people with hand loss.

Control of continuous digester kappa number using generalized model predictive control

Kappa number variability at the digester impacts pulp yield, physical strength properties, and lignin content for downstream delignification processing. Regulation of the digester kappa number is therefore of great importance to the pulp and paper industry. In this work, an industrial application of model-based predictive control (MPC), based on generalized prediction control, was developed for kappa number feedback control and applied to a dual vessel continuous digester located in Western Canada. The problem was complicated by the need to apply heat at multiple locations in the cook. In this study, the problem was reduced from a multiple to a single input system by identifying three potential single variable permutations for temperature adjustment. In the end, a coordinated approach to the heaters was adopted. The process was perturbed and modeled as a simple first order plus dead time model and implemented in generalized predictive control (GPC). The GPC was then configured to be equivalent to Dahlin’s controller, which reduced tuning parameterization to a single closed loop time constant. The controller was then tuned based on robustness towards a worst-case dead time mismatch of 50%. The control held the mean value of the kappa number close to the setpoint, and a 40% reduction in the kappa number’s standard deviation was achieved. Different kappa number trials were run, and the average fiberline yield for each period was evaluated. Trial results suggested yield gains of 0.3%–0.5% were possible for each 1 kappa number target increase.

RBF-ARX model-based trust region nonlinear model predictive control and its application on magnetic levitation ball system

RBF-ARX model-based trust region nonlinear model predictive control and its application on magnetic levitation ball system

Triplex event-triggered recursive quantizer-based networked control system under communication delay and packet dropouts

In light of network congestion, communication delay, and packet dropouts, a triplex event-triggered recursive quantizer-based networked control system (TERQNCS) is proposed in this paper. To constrain quantization error while alleviating network congestion, the recursive quantizer is constructed in the TERQNCS. Considering it is insufficient to adequately alleviate network congestion only with either the quantizer or the event trigger, a triplex event-triggered mechanism is designed through the recursive quantizer. For revealing the impacts of communication delay and packet dropouts, four Bernoulli random variables are adopted to model the network channels in the TERQNCS. Then, the model-based predictive controller is designed by virtue of the predictive states, estimated states, and Bernoulli-based model. The proposed TERQNCS can achieve the desired system performance under communication delay and packet dropouts while adequately alleviating network congestion and constraining quantization error. The stability of the TERQNCS is proved by theoretical analysis. Besides, the advantages of the TERQNCS are substantiated by a tunnel diode circuit networked control system.

Predictive heating load management and energy flexibility analysis in residential sector using an archetype gray-box modeling approach: Application to an experimental house in Québec

This paper presents a methodology to develop archetype gray-box models and use them in an economic model-based predictive control algorithm to simulate optimal heating load management in response to a newly-introduced static time-of-use tariff for Québec’s residential sector, rate Flex-D. The methodology is evaluated through a case study, wherein in situ measurements from a two-storey unoccupied research house of Hydro-Québec are used to develop an 11R6C network with a heuristic zoning-by-floor approach and compute the sequence of optimal electric heating input for the next control horizon. Properly-tuned economic model-based predictive control under rate Flex-D shows potential for an approximately 30% reduction in daily heating cost compared to the reference operation, with a minimal average deviation of indoor air temperature from the reference setpoint. Also, the analysis of the response’s sensitivity to weather forecast uncertainties indicates that the most influential uncontrolled input directing the performance of economic model-based predictive control is the structure price signal, rendering the impact of uncertainty in the weather forecast negligible.

Enhanced trajectory tracking control algorithm for ROVs considering actuator saturation, external disturbances, and model parameter uncertainties

In this study, an augmented model-based predictive control (AMPC) algorithm was designed for a work-class remotely operated vehicles (ROV) with actuator saturation constraints, model parameter uncertainty, and external disturbances. Firstly, model parameter uncertainties and external disturbances (primarily umbilical cable forces) were considered in the kinematic model of the ROV. The ROV's actuator saturation constraints were introduced when designing the optimal control problem (OCP). Secondly, Bellman's principle of optimality was applied to the solution process of the OCP, decomposing it into multiple easily solvable sub-optimization problems to reduce computational complexity. Then, the recursive feasibility and input-to-state practical stability (ISpS) of the AMPC algorithm were proven in the presence of model parameter uncertainties and external disturbance forces. It was also demonstrated that the state convergence region of the system employing the AMPC algorithm was smaller than that of the system using the min-max MPC algorithm when reaching a steady state. Finally, numerical simulations verified that under the influence of external disturbance forces, model parameter uncertainties, and positioning system noise, the ROV using the AMPC algorithm achieved higher accuracy in tracking the desired trajectory compared to the min-max MPC algorithm.

A Novel Finite-Set Ultra-Local Model-Based Predictive Current Control for AC/DC Converters of Direct-Driven Wind Power Generation with Enhanced Steady-State Performance

Compared with the standard finite-set model-based predictive current control (FS-MPCC), the finite-set ultra-local model-based predictive current control (FS-ULMPCC) removes the use of actual system parameters and thus has some advantages like good robustness and easy implementation. However, the steady-state performance of FS-ULMPCC is relatively weak. In this paper, a novel FS-ULMPCC method is proposed and applied to the AC/DC converter of a direct-driven wind power generation system. The proposed method is designed based on a linear-extended state observer (LESO). In particular, a new control set reconstruction strategy is proposed to improve the steady-state performance. Only three options are included in the reconstructed control set, and each one is associated with two independent, active voltage vectors and their durations. The proposed FS-ULMPCC method is compared with the traditional one through experiments. The proposed method includes enhanced steady-state performance and reduced computational burden.

Model-based predictive torque control of open-end winding IPMSMs driven by direct self-control

Model-based predictive torque control of open-end winding IPMSMs driven by direct self-control

Improved Model-Free Predictive Control of a Three-Phase Inverter

Model predictive control (MPC) performance depends on the accuracy of the system model. Moreover, the optimization algorithm of MPC requires numerous online computations. These inherent limitations of MPC hinder its application in power electronics systems. This paper proposes a two-part solution for these challenges for a three-phase inverter with an output LC filter. The first part of the control scheme is a linear and modified model-free approach based on the auto-regressive structure (ARX) with exogenous input. The second part is the computationally efficient optimization algorithm based on the active set method to solve the optimization problem of the MFPC. The objective of the control scheme is to regulate the output voltages of the inverter in the presence of constraints. The constraints are the maximum admissible filter current and optimal duty cycle to avoid any damage to the system. To validate the performance of the proposed control scheme, simulations and hardware-in-loop (HIL) real-time investigations have been performed, comparing the results of the proposed approach with the model-based predictive control. The results showcase the computational efficiency and effectiveness of the MFPC approach, demonstrating its potential for overcoming the limitations of traditional MPC in power electronics systems.

A novel dynamic predictive control model for variable air volume air conditioning systems using deep learning

Model-based predictive control has proven effective for managing indoor temperature and humidity in variable air volume (VAV) air conditioning systems. However, delays in temperature and humidity adjustments in response to changes in internal and external environmental conditions can impede high-performance operation. This study introduces a dynamic predictive control model for the multi-zone indoor temperature and humidity of VAV systems, designed to predict and control these parameters in real-time. Utilizing the RC network two-node wall structure method and backward finite difference scheme, a multi-zone building model is developed. Coupled with a deep fuzzy cognitive map (DFCM) for temperature and humidity prediction, and controlled by a PID controller, the model dynamically regulates the opening degrees of terminal air dampers and chilled water valves. Implemented on a networked control platform in a large commercial building, the model consistently maintained indoor temperature and humidity within ±0.5 °C and ±0.1 g/(kg dry air) of the target values under varying conditions, demonstrating substantial improvements in stability and operational efficiency. The research enhances the predictability and control of HVAC systems through advanced algorithmic integration, improving real-time indoor climate management in complex building environments. This method offers a practical improvement in HVAC control technologies, which could be beneficial for applications in commercial building management systems, providing a useful tool for supporting energy efficiency and sustainability in modern infrastructures.

Infrared signal-based implementation of model-based predictive control (MPC) for cost saving in a campus building

Model-based predictive control (MPC) is an advanced control method that is used to achieve energy and cost savings in building energy management. However, there are various challenges to implementing MPC in actual buildings, such as additional engineering costs, physical damage to systems, and security concerns. This study proposes a non-intrusive implementation method for MPC based on a low-cost communication system. Firstly, a grey-box building model was developed along with the heating, ventilation, and air conditioning (HVAC) models based on the actual measurements of the test building. A simulation case study of MPC was performed, along with baseline feedback control, using these constructed models. The MPC outperformed the feedback control in terms of comfort, electricity consumption, and cost. It was implemented in an actual test zone to demonstrate the feasibility of a low-cost and non-intrusive implementation method that was realized using Arduino-based infrared signal communication and to evaluate the cost-saving potential of the MPC when compared with the baseline control. This experiment was conducted under a confined schedule for occupancy and set-point temperature for three days for the MPC and two days for the baseline feedback control in the cooling season. The cost savings of the MPC are as high as 5.8 %. Additional feedback cases were considered with a non-confined occupancy schedule and set-point temperature. The resulting energy consumption and cost savings are 25.2 % and 33.7 %, respectively. In this study, the applicability of the Arduino-based low-cost and non-intrusive implementation method for MPC was demonstrated, and the actual saving percentage of the MPC was compared with the conventional control method in a real building.

Effective Nonlinear Predictive and CTC-PID Control of Rigid Manipulators

Effective nonlinear control of manipulators with dynamically coupled arms, like those with direct drives, is the subject of the paper. The main proposal of the paper are model-based predictive control (MPC) algorithms, with nonlinear state-space models and most recent disturbance attenuation technique. This technique makes controller design and calculations simpler, avoiding necessity of dynamic modeling of disturbances or resorting to additional techniques like SMC. The core of the paper are computationally effective MPC-NPL (Nonlinear Prediction and Linearization) algorithms, where computations at every sample are divided into two parts: prediction of initial trajectories using nonlinear model, then optimization using simplified linearized model. For a comparison a known CTC-PID algorithm, which is also model-based, is considered. It is applied in standard form and also proposed in more advanced CTC-PID2dof version. For all algorithms a comprehensive comparative simulation study is performed, for a direct drive manipulator under disturbances. Additional contribution of the paper is an investigation of influence of sampling period length and computational delay time on performance of the algorithms, which is practically important when using model-based algorithms and fast sampling.

Nonlinear model-based predictive control of a yeast fermentation bioreactor using new bee colony-WNN modelling structure

This paper concentrates on the application of a newly developed model for the nonlinear control of a yeast fermentation bioreactor. The primary control objectives are the maintenance of temperature and product concentration within the desired range. So, having an accurate model in control strategy is not ignorable and this model influences on close-loop controllability. In the context of online control, the parameters are changed (the system is time varying) and precision of extracted model is more important. Moreover, there is a demand for a computationally efficient model, and the convergence speed during the training of the neural network is significant. To overcome the challenges associated with slow convergence, the risk of being trapped in local minima, and the imperative of achieving computational efficiency, this study introduces a novel combination of a pruned Bee Colony Wavelet Neural Network for the online modeling of the bioreactor. The method's superiority lies in its ability to conduct searches in multiple sections independently, thereby avoiding local minima. The hybrid model controllability is further investigated using Dynamic Matrix Control (DMC) and Generalized Matrix Control (GPC) techniques. Subsequently, the paper explores the application of Nonlinear Model Predictive Control (NMPC) by solving a Gauss-Newton quadratic programming problem online, followed by a comparative analysis of the results. It is demonstrated that the precision of the model closely influences profitability and enhances product yields.

Agent-Based System for Continuous Control and its Application to Activated Sludge Process

This paper presents a concept of architecture and ontology layouts for development of multiagent model-based predictive control systems. The presented architecture provides guidelines to simplify development of agent-based systems and improve its maintainability. The proposed multiagent system (MAS) layout is split into multiple subsystems that include agents dedicated to performing assigned tasks. MAS implementation was prepared which can use provided algorithms, actuators and can react to changes in its environment to reach the best available control quality. An example of MAS based on the proposed architecture is shown in application of dissolved oxygen (DO) concentration control in a laboratory activated sludge setup with biological reactor. For that application, MAS incorporates agent-based controllers from Boundary-Based Predictive Controllers (BBPC) family. Presented experiments prove flexibility, resilience and online reconfiguration ability of the proposed multiagent system.

Model-Based Predictive Control of a Solar Reactor

The present paper deals with the modelling and control of a solar reactor designed to produce syngas, by exploiting concentrated solar power. A model of the reactor based on the thermodynamic equilibrium is developed. Two model-based predictive control strategies are proposed: the first strategy (MPC strategy 1) aims to maintain the reactor's temperature at its nominal value whereas the second strategy (MPC strategy 2) aims to maintain the reactor's temperature at its nominal value, while maximizing the use of solar energy. Finally, these strategies are compared to a reference strategy, which is based on a combination of a rule-based controller and an adaptive PID controller with optimized gains. The robustness of the MPC controller to forecast errors is also studied by testing different DNI forecasting models. Parts of this paper were published as journal articleKarout, Y.; Curcio, A.; Eynard, J.; Thil, S.; Rodat, S.; Abanades, S.; Vuillerme, V.; Grieu, S. Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass. Clean Technol. 2023, 5, 329-351. https://doi.org/10.3390/cleantechnol5010018

Autonomous Vehicle and Pedestrian Interaction

Driving assistance systems and even autonomous driving have and will have an important role in sustainable mobility systems. Traffic situations where participants’ cognitive levels are different will cause challenges in the long term. When a pedestrian crosses the road, an autonomous vehicle may need to navigate safely while maintaining its desired speed. Achieving this involves using a predictive model to anticipate pedestrian movements and a strategy for the vehicle to adjust its speed proactively. This research combined model-based predictive control (MPC) with a social-force model (SFM) to effectively control the autonomous vehicle’s longitudinal speed. A genetic algorithm (GA) was also integrated into the approach to address the optimisation problem. A comparison between the proposed approach (MPC-GA) and the conventional MPC technique proved the outperformance of MPC-GA.

The control of superheater steam temperature in power plants using model predictive controller

The temperature of the steam produced by the superheater has been regarded as one of the most crucial parameters for steam power plant regulation. It must be precisely regulated within a restricted temperature range; overheating would result in the degradation of the substance. The recommended hybrid technique employed the Model Predictive Controller and Improved Fruit Fly Optimization Algorithm to regulate temperature levels and guarantee stability. Using the proposed method, model-based predictive control aims to enhance control quality and identify the optimal value. By employing this approach, the superheater can attain the optimal configurations for the steam temperature plant, thereby simultaneously enhancing the stability of the plant. The principal aim of this manuscript is to attain a reduction in the Integral Absolute Error to 40.6%. The simulation was conducted using the MATLAB/Simulink platform, and the outcomes demonstrate that the proposed method exhibits superior performance compared to those that have been documented in the literature.

Implementing a digital twin for optimized real-time control of Gothenburg's regional sewage system

Abstract The emerging challenges facing the City of Gothenburg and surrounding municipalities include the impacts of a variable climate, rapid growth and urbanization, aging infrastructure and changing community values and customer expectations. A digital twin (DT), based on the Future City Flow (FCF) platform, was created for the catchment to address many of these challenges in terms of improved real-time sewerage performance and predictability in the face of storms, improved DT visualization of the regional sewage transport system for operators and greater responsiveness to avoid service interruption. The FCF-based DT provides a sound base for modelling, simulating, forecasting and controlling the catchment. The versatility of the FCF DT makes it useful for gaining insight into catchment dynamics and the simulator can be used to study the effects of different scenarios or changes in the catchment. The predictive control imbedded in the FCF DT recommends setpoints for controlling the catchment that can be used by the operators for decision support or even be directly applied in the control system as model-based predictive control. A DT requires good quality online data from the catchment and, perhaps most importantly, high-quality rain forecasts, especially when prognosing long time horizons.

Performance analysis of a robust MF-PTC strategy for induction motor drive

The load model-based conventional predictive torque control (PTC) strategy performs as a high-performance controller at transient and steady-state conditions. However, its performance is poor on the issue of parameters variation. A robust model-free PTC (MF-PTC) strategy has been proposed in this paper to overcome the aforementioned drawback. An auto-regressive exogenous (ARX) model instead of a load model has been used to establish a model-free controller. This model is usually formed based on their input-output transfer function. A recursive least square algorithm has been employed to estimate the unknown parameters of the ARX model. Then, an observable canonical state-space model uses those estimated parameters to achieve an accurate prediction of the control variables. The performance of the proposed scheme can be affected by the variation of resistance. A resistance estimator based on the model reference adaptive system observer has been applied to improve the robustness of the system against variation of the stator and rotor resistance, and inductance measurement uncertainty. Simulation results show that the proposed MF-PTC scheme is robust against parameters uncertainty and works well at transient and steady-state conditions.