Model Predictive Control Parameters Research Articles

An innovative practical roadmap for optimal control strategies in malware propagation through the integration of RL with MPC

While there has been considerable research into optimal control formulations for mitigating cyber threats, a significant gap persists between the theoretical and numerical insights derived from such research and the practical implementation of these optimal mitigation strategies in real-time scenarios. This paper introduces a multifaceted approach to enhance and optimize optimal control strategies by seamlessly integrating reinforcement learning (RL) algorithms with model predictive control (MPC) techniques for the purpose of malware propagation control. Optimal control is a critical aspect of various domains, ranging from industrial processes and robotics to epidemiological modeling and cybersecurity. The traditional approaches to optimal control, particularly open-loop strategies, have limitations in adapting to dynamic and uncertain environments. This paper addresses these limitations by proposing a novel roadmap that leverages RL algorithms to fine-tune and adapt MPC parameters within the context of malware propagation containment. In sum, this practical roadmap is anticipated to serve as a valuable resource for researchers and practitioners engaged in the development of cybersecurity solutions.

Advanced Control Strategies for Continuous Stirred Tank Reactors:

This paper investigates the control of a Continuous Stirred Tank Reactor (CSTR), with a primary emphasis on achieving temperature stability and optimizing reactant conversion. The CSTR poses challenges due to its nonlinear and exothermic behavior. To address these challenges, Model Predictive Control (MPC) is employed, a powerful strategy for handling complex systems. Additionally, Particle Swarm Optimization (PSO) is introduced to fine-tune MPC parameters, ensuring optimal performance. By integrating PSO with MPC, this study enhances control capabilities, specifically targeting the intricate demands of CSTR systems.

Cargo E-Bike Robust Speed Control Using an MPC Battery Thermal Lumped Model Approach

Cargo e-bikes are expected to convey heavy loads in all aspects of daily life. Also, these vehicles are expected to maintain a consistent speed to meet mobility needs while optimizing the battery design. In this paper, a control model is developed to improve rotational speed motor control via the battery model predictive controller (MPC) thermal model designed based on experimental field test data. Experimental field tests are performed to provide the relation between battery surface and ambient temperatures in different road types and weight conditions. For this purpose, in different slope ranges, the pedal load/activity and voltage-current data are logged to use as experimental input in an MPC-integrated 1D model. To obtain the desired thermal conditions in the Li-Ion battery, the MPC battery thermal model is defined based on the thermal lumped model approach. In the next step, the generated MPC model is used as a function for longitudinal speed control in the MPC motor torque control model subjected to uncertain road disturbances. Then, the outputs of the control models are compared using the MPC parameters oc weight factors and prediction horizon. Thus, the speed control model for cargo e-bikes is presented with increased robustness using the MPC battery thermal lumped model approach considering energy and Li-Ion battery life-cycle efficiency methods regardless of driving performance needs.

Modeling and Control of an Indirect Solar Dryer in Forced Convection Mode Using System Identification and Model Predictive Control

In nonlinear process, system modeling is vital part to predict and examine the performance of a plant. Here, indirect solar dryer performed in forced convection mode taken as a plant. Several experimental tests were performed by drying tomato slices, both in forced as well as in natural convection mode to study the dynamic characteristics of it. The efficiency of indirect solar dryer was better with forced convection than the natural convection mode. In this investigation System Identification technique is used to predict the model equation for forced convection. Designing a conventional control strategy to ensure efficiency is cumbersome due to the plant’s multivariable nature and nonlinear dynamics. The regular proportional-integral-derivative (PID) controller provides unproductive control action for nonlinear system. A model predictive controller (MPC) is intended for the proposed system ensuring efficiency of the indirect solar dryer. The MPC parameters must be selected appropriately to ensure optimal performance. The Matlab platform was used to get the both MPC algorithms and conventional PID performance.

Evolutionary algorithm-based model predictive control for a reactive distillation column in biodiesel production

Biodiesel is touted to be an alternative to the fossil fuels as it is conducive to the environment. This investigation proposes a control framework to produce biodiesel in a reactive distillation column via a transesterification process. To extract quality product, the temperature profile must be maintained along the column as per the requirements. However, constant interactions among the products inside the column disturb the temperature profile and consequently the product quality. Therefore, this investigation treats the process as a single input and single output system, where in the process interactions are modelled as disturbances. A model predictive controller (MPC) is designed for the proposed system to ensure product quality. The MPC parameters must be selected appropriately to ensure optimal performance. In this regard, to tune the MPC parameters optimally, we use two evolutionary algorithms namely, the real coded genetic algorithm (RGA) and the bio-geography based optimization algorithm (BBO). The results indicate the proposed control strategy provides offset free set point tracking when compared to the multivariable control strategy employed using the MPC algorithm. Among the two evolutionary controllers used for tuning the MPC parameters, the RGA MPC controller provides a satisfactory performance.

Model predictive control with exogenous auto-regressive model to improve performance in the CO2 removal

Model predictive control (MPC) is used in the CO2 removal process in the Subang field to improve its control performance. MPC is used to maintain the CO2 concentration at the sweet gas output by controlling the feed gas pressure (PIC-1101), makeup water flow rate (FIC-1102), and amine flow rate (FIC-1103). The empirical model applied to MPC to represent the process model is the auto-regressive exogenous (ARX) model. The ARX model is compared with the first order plus dead time (FOPDT) model based on the root mean square error (RMSE) between the model and the actual process, then MPC parameters are tuned which include sampling time (T), prediction horizon (P) and control horizon (M) to control for the three variables. Improved control performance is measured based on the integral square error (ISE). The results show that the ARX model is the best model for the CO2 removal process with an RMSE value of 35%-91% smaller than the FOPDT model. The optimal control parameters Prediction Horizon (P), Control Horizon (M) and Sampling Time (T) in the CO2 removal process are 75, 25 and 1 on PIC-1101, 25, 10 and 1 on FIC-1102, and 30, 25 and 1 on FIC-1103. The MPC-ARX (MPC using ARX model) can improve the control performance of 33% in the servo control and 6-56% on the regulatory control. However, not all of them showed an increase in control performance improvement from previous studies even though they had used the best model (ARX). This is due to the MPC parameter setting that is not yet appropriate, so it needs to be retuning.

Optimization of the model predictive control meta-parameters through reinforcement learning

Model predictive control (MPC) is increasingly being considered for control of fast systems and embedded applications. However, MPC has some significant challenges for such systems, such as its high computational complexity. Further, the MPC parameters must be tuned, which is largely a trial-and-error process that affects the control performance, the robustness, and the computational complexity of the controller to a high degree. This paper presents a multivariate optimization method based on reinforcement learning (RL) that automatically tunes the control algorithm’s parameters from data to achieve optimal closed-loop performance. The main contribution of our method is the inclusion of state-dependent optimization of the meta-parameters of MPC, i.e. parameters that are non-differentiable wrt. the MPC solution. Our control algorithm is based on an event-triggered MPC, where we learn when the MPC should be re-computed, and a dual-mode MPC and linear state feedback control law applied in between MPC computations. We formulate a novel mixture-distribution RL policy determining the meta-parameters of our control algorithm and show that with joint optimization we achieve improvements that do not present themselves with univariate optimization of the same parameters. We demonstrate our framework on the inverted pendulum control task, reducing the total computation time of the control system by 36% while also improving the control performance by 18.4%.

Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes

This paper proposes a method to encourage safety in Model Predictive Control (MPC)-based Reinforcement Learning (RL) via Gaussian Process (GP) regression. The framework consists of 1) a parametric MPC scheme that is employed as model-based controller with approximate knowledge on the real system's dynamics, 2) an episodic RL algorithm tasked with adjusting the MPC parametrization in order to increase its performance, and 3) GP regressors used to estimate, directly from data, constraints on the MPC parameters capable of predicting, up to some probability, whether the parametrization is likely to yield a safe or unsafe policy. These constraints are then enforced onto the RL updates in an effort to enhance the learning method with a probabilistic safety mechanism. Compared to other recent publications combining safe RL with MPC, our method does not require further assumptions on, e.g., the prediction model in order to retain computational tractability. We illustrate the results of our method in a numerical example on the control of a quadrotor drone in a safety-critical environment.

An Improvement of Model Predictive for Aircraft Longitudinal Flight Control Based on Intelligent Technique

This paper introduces a new intelligent tuning for the model predictive control (MPC) based on an effective intelligent algorithm named the bat-inspired algorithm (BIA) for the aircraft longitudinal flight. The tuning of MPC horizon parameters represents the main challenge to adjust the system performance. So, the BIA algorithm is intended to overcome the tuning issue of MPC parameters due to conventional methods, such as trial and error or designer experience. The BIA is adopted to explore the best parameters of MPC based on the minimization of various time domain objective functions. The suggested aircraft model takes into account the aircraft dynamics and constraints. The nonlinear dynamics of aircraft, gust disturbance, parameters uncertainty and environment variations are considered the main issues against the control of aircraft to provide a good flight performance. The nonlinear autoregressive moving average (NARMA-L2) controller and proportional integral (PI) controller are suggested for aircraft control in order to evaluate the effectiveness of the proposed MPC based on BIA. The proposed MPC based on BIA and suggested controllers are evaluated against various criteria and functions to prove the effectiveness of MPC based on BIA. The results confirm that the accomplishment of the suggested BIA-based MPC is outstanding to the NARMA-L2 and traditional PI controllers according to the cross-correlation criteria, integral time absolute error (ITAE), system overshoot, response settling time, and system robustness.

Learning for MPC with stability & safety guarantees

The combination of learning methods with Model Predictive Control (MPC) has attracted a significant amount of attention in the recent literature. The hope of this combination is to reduce the reliance of MPC schemes on accurate models, and to tap into the fast developing machine learning and reinforcement learning tools to exploit the growing amount of data available for many systems. In particular, the combination of reinforcement learning and MPC has been proposed as a viable and theoretically justified approach to introduce explainable, safe and stable policies in reinforcement learning. However, a formal theory detailing how the safety and stability of an MPC-based policy can be maintained through the parameter updates delivered by the learning tools is still lacking. This paper addresses this gap. The theory is developed for the generic robust MPC case, and applied in simulation in the robust tube-based linear MPC case, where the theory is fairly easy to deploy in practice. The paper focuses on reinforcement learning as a learning tool, but it applies to any learning method that updates the MPC parameters online.

A P-Q Coordination Based Model Predictive Control for DFIG High-Voltage Ride Through

This paper presents a P-Q coordination based model predictive control (MPC) for doubly fed induction generator (DFIG) to handle high-voltage ride through (HVRT) considering bipolar blocking of high-voltage direct current (HVDC) system. The overvoltage at the point of common coupling (PCC) after DC bipolar blocking and its sensitivity to current injection of DFIG is firstly analyzed based on a typical sending-end AC grid equivalent model. Then, the current feasible region of DFIG operation during overvoltage process is explicitly revealed considering generator and converter constrains. In the proposed HVRT control, a model predictive control (MPC) based method is developed where the minimum voltage tracking error is taken as the main objective function and the constrains are updated every control horizon based on real-time current feasible region. Through the rolling optimization and feedback correction framework, the rotor active and reactive currents of DFIG are coordinated and PCC voltage can be finally controlled to its target value to achieve HVRT. Further, the key MPC parameters are determined by solving an optimal problem to achieve better system robust stability and dynamic performances. Finally, the validity of the proposed control is verified by simulations in MATLAB/Simulink.

Finite-horizon, energy-efficient trajectories in unsteady flows.

Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes and ocean currents. Often constrained in their actuation capabilities, the dynamics of these mobile sensors depend strongly on the background flow, making their deployment and control particularly challenging. Therefore, efficient trajectory planning with partial knowledge about the background flow is essential for teams of mobile sensors to adaptively sense and monitor their environments. In this work, we investigate the use of finite-horizon model predictive control (MPC) for the energy-efficient trajectory planning of an active mobile sensor in an unsteady fluid flow field. We uncover connections between trajectories optimized over a finite-time horizon and finite-time Lyapunov exponents of the background flow, confirming that energy-efficient trajectories exploit invariant coherent structures in the flow. We demonstrate our findings on the unsteady double gyre vector field, which is a canonical model for chaotic mixing in the ocean. We present an exhaustive search through critical MPC parameters including the prediction horizon, maximum sensor actuation, and relative penalty on the accumulated state error and actuation effort. We find that even relatively short prediction horizons can often yield energy-efficient trajectories. We also explore these connections on a three-dimensional flow and ocean flow data from the Gulf of Mexico. These results are promising for the adaptive planning of energy-efficient trajectories for swarms of mobile sensors in distributed sensing and monitoring.

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment

This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.

A Novel Sooty Terns Algorithm for Deregulated MPC-LFC Installed in Multi-Interconnected System with Renewable Energy Plants

This paper introduces a novel metaheuristic approach of sooty terns optimization algorithm (STOA) to determine the optimum parameters of model predictive control (MPC)-based deregulated load frequency control (LFC). The system structure consists of three interconnected plants with nonlinear multisources comprising wind turbine, photovoltaic model with maximum power point tracker, and superconducting magnetic energy storage under deregulated environment. The proposed objective function is the integral time absolute error (ITAE) of the deviations in frequencies and powers in tie-lines. The analysis aims at determining the optimum parameters of MPC via STOA such that ITAE is minimized. Moreover, the proposed STOA-MPC is examined under variation of the system parameters and random load disturbance. The time responses and performance specifications of the proposed STOA-MPC are compared to those obtained with MPC optimized via differential evolution, intelligent water drops algorithm, stain bower braid algorithm, and firefly algorithm. Furthermore, a practical case study of interconnected system comprising the Kuraymat solar thermal power station is analyzed based on actual recorded solar radiation. The obtained results via the proposed STOA-MPC-based deregulated LFC confirmed the competence and robustness of the designed controller compared to the other algorithms.

An Improved Approach for Robust MPC Tuning Based on Machine Learning

A robust tuning method based on an artificial neural network for model predictive control (MPC) of industrial systems with parametric uncertainties is put forward in this work. Firstly, an efficient approach to characterize the mapping relationship between the controller parameters and the robust performance indices is established. As there are normally multiple conflicted robust performance indices to be considered in MPC tuning, the neural network is further used to fuse the indices to produce a simple label representing the acceptable level of the robust performance. Finally, an automated algorithm is proposed to tune the MPC parameters for the considered uncertain system to achieve the desired robust performance. In addition, the regulation of the pH value of the sewage treatment system is used to verify the effectiveness of the robust tuning algorithm which is described in this paper.

Plant and Controller Optimization for Power and Energy Systems With Model Predictive Control

Abstract This article explores the optimization of plant characteristics and controller parameters for electrified mobility. Electrification of mobile transportation systems, such as automobiles and aircraft, presents the ability to improve key performance metrics such as efficiency and cost. However, the strong bidirectional coupling between electrical and thermal dynamics within new components creates integration challenges, increasing component degradation, and reducing performance. Diminishing these issues requires novel plant designs and control strategies. The electrified mobility literature provides prior studies on plant and controller optimization, known as control co-design (CCD). A void within these studies is the lack of model predictive control (MPC), recognized to manage multi-domain dynamics for electrified systems, within CCD frameworks. This article addresses this through three contributions. First, a thermo-electromechanical hybrid electric vehicle (HEV) powertrain model is developed that is suitable for both plant optimization and MPC. Second, simultaneous plant and controller optimization is performed for this multi-domain system. Third, MPC is integrated within a CCD framework using the candidate HEV powertrain model. Results indicate that optimizing both the plant and MPC parameters simultaneously can reduce physical component sizes by over 60% and key performance metric errors by over 50%.

Frequency Regulation of an Isolated Microgrid With Electric Vehicles and Energy Storage System Integration Using Adaptive and Model Predictive Controllers

Energy storage system (ESS) possesses tremendous potential to counter both the rapid growth of intermittent renewable energy resources (RESs) and provide frequency support to the microgrid (MG). Since the deployment of ESS has overcome the imbalance between generation and consumption, however, their massive cost, as well as degradation tendency, are the restricting considerations that demand alternative solutions to provide stable microgrid operation. To assist ESS, the electric vehicles (EVs) are incorporated into the system. EVs have been gradually commercially viable and considerable focus has been paid to vehicle-to-grid technologies. Appropriate collaboration between ESS and EVs has good capability to manage the frequency irregularities to ensure the efficient operation of the MG. This article presents a novel combination of two control techniques i.e., model predictive control (MPC) and adaptive droop control (ADC), to tackle the frequency regulation issue in the isolated MG, by effectively controlling the ESS and EVs during the large-scale integration of RESs or huge change in load demand. Firstly, the MPC regulates the ESS according to the system frequency deviation, and secondly, the ADC manages the power of EVs according to system specifications by retaining the least possible power for potential usage of EVs. Moreover, an advanced genetic algorithm is applied to tune the MPC and ADC parameters in order to achieve optimized performance. An isolated MG is modeled and verified in MATLAB/Simulink using the above-mentioned control techniques. Further, different case studies are taken into account to validate the combination of ADC and MPC for frequency regulation of an isolated MG. Additionally, the proposed MPC controller is compared with fuzzy logic proportional-integral (FPI) controller and proportional-integral (PI) controller, the MPC provides better performance results as compared with FPI and PI controllers.

Design and Simulation of a Model Predictive Controller (MPC) for a Seismic Uniaxial Shake Table

Shake table is one of the apparatus that aids in researches to generate techniques, structural developments, and strategies to prevent, prepare, and minimize an earthquake’s devastating effects. One important factor that should be considered in a shake table is the system dynamics due to control-structural interactions, which could either be linear or non-linear. To accurately model both has always been the challenge but becomes more plausible with the availability of faster hardware and computers and the continuous decrease in latency. Model Predictive Controller (MPC) is a type of controller extensively used in the industry that can be used on linear and non-linear systems. This study presents the design and simulation of an MPC for a uniaxial shake table intending to analyze the system’s behavior and accuracy. MATLAB Simulink was utilized to handle the simulation analysis of the controller. Different MPC parameters such as sample time, prediction horizon, control horizon, and closed-loop performance were manipulated and adjusted to observe their effects on the output of the system. A signal that mimics the actual earthquake data was inputted into the controller, and the system's behavior and outputs were measured and presented through graphical representations. To determine the accuracy of the system’s output, its relationship with the reference signal was compared. From the simulation produced, the system demonstrated high accuracy levels and could be adjusted depending on the set performance aggressiveness of the system.

Resilient Model Predictive Control of Cyber–Physical Systems Under DoS Attacks

This article presents a resilient model predictive control (MPC) framework to attenuate adverse effects of denial-of-service (DoS) attacks for cyber-physical systems (CPSs), where the system dynamics is modeled by a linear time-invariant system. A DoS attacker targets at blocking the controller to actuator (C-A) communication channel by launching adversarial jamming signals. We show that, in order to guarantee exponential stability of the closed-loop system, several conditions for resilient MPC should be satisfied. And these established conditions are explicitly related to the duration of DoS attacks and MPC parameters such as the prediction horizon and the terminal constraint. Two key techniques, including the μ-step positively invariant set and the modified initial feasible set are exploited for achieving exponential stability in the presence of DoS attacks. Moreover, the maximum allowable duration of the DoS attacker is also obtained by using the μ-step positively invariant set. Finally, the effectiveness of the proposed MPC algorithm is verified by simulated studies and comparisons.