Model Predictive Control Optimization Research Articles

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.

Aerodynamic modeling and performance analysis of model predictive controller for fixed wing vertical takeoff and landing unmanned aerial vehicle

This research focuses mainly on the aerodynamic modelling and performance analysis of a model predictive controller for a hybrid fixed-wing vertical takeoff and landing of unmanned aerial vehicle. The aerodynamics, which comprises several aerodynamic characteristics including the lift, drag, and thrust coefficients, is modelled using Newton’s second law of motion. The force and moment equations were obtained, and they were then converted into matrix and state equation form, together with the transformation matrices. The essential equations with six degrees of freedom (6 DoF) and 12 state matrices including the control input and manipulating variables were obtained. The proposed model predictive controller (MPC) was designed using the optimal model predictive controller design parameters, such as a sampling time of 0.1, a prediction horizon of 15, a control horizon of 3 and additional controller settings. With a settling period of 3 s, an overshoot of 0.5865, and a steady state inaccuracy of 0.00785, the MATLAB simulation demonstrates that the system variables, including roll, pitch, and yaw, are stabilized. This MPC control is more effective in anticipating and optimizing the UAV than other control strategies. Eventually, the controller’s simulation on MATLAB Simulink demonstrates the controller’s ability to stabilize and control the system in a real-time application. Autonomous vertical takeoff and landing operations depend heavily on the mathematical model and architecture of the flight controller. Among the uses are inspection, monitoring, and rescue.

Model Predictive Control With Guaranteed Feasibility of Inequality Path Constraints.

This article concerns nonlinear model predictive control (MPC) with guaranteed feasibility of inequality path constraints (PCs). For MPC with PCs, the existing methods, such as direct multiple shooting, cannot guarantee feasibility of PCs because the PCs are enforced at finitely many time points only. Therefore, this article presents a novel MPC framework that is capable of not only achieving stability control but also guaranteeing feasibility of PCs during the rolling optimization stages of MPC. Under the above MPC framework, an algorithm is first proposed by applying the semi-infinite programming technique to the rolling optimization of MPC. However, it takes heavy computational time to achieve guaranteed feasibility of PCs. Therefore, to guarantee feasibility of PCs meanwhile effectively reducing the computation burden of the closed-loop system, an event-triggered sampling mechanism is constructed in the above path-constrained MPC algorithm. Moreover, sufficient conditions are given for asymptotic convergence of the closed-loop systems. Finally, the effectiveness of the proposed results is illustrated via a cart-damper-spring system.

Optimized model predictive control for solar assisted earth air heat exchanger system in greenhouse

Agricultural greenhouses play a crucial role in addressing the threat of climate change to agricultural systems. The greenhouse systems control exhibits nonlinear characteristics and large lag, both affecting the regulation of the greenhouse thermal environment. In this paper, a control strategy was designed to set the dynamic reference value of the control objective of the greenhouse model, considering factors such as solar radiation and the heating capacity of water body. A complete greenhouse model was developed using TRNSYS, and then the model predictive control building and optimization were implemented through MATLAB. The control effects of traditional proportional-integral-differentiation control, initial model predictive control, and optimized model predictive control were compared. The results showed that the regulation of greenhouse temperature was improved by 10.6 % in the coldest mouth compared to the conventional proportional-integral-differentiation control by the optimized model predictive control. During a typical cold month, the violation of constraints was reduced by 29.7 % by dynamically setting the target of the greenhouse with the optimized model predictive control. The performance of greenhouse climate control is enhanced by the proposed optimized model predictive control method, ensuring a stable regulation of the crop growth environment.

A behaviour digital twin for residential demand response: Modelling intention and motivation to improve the prediction of the likelihood of reaction to behavioural triggers

Background: Residential demand response is a resource in the evolving energy infrastructure which thus far has not achieved its full potential. Amongst the reasons for this underutilisation is a lack of understanding, and therefore predictability, in relation to the uncertainty of the behaviour of human actors and its potential impact on energy demand side management. Optimal model predictive control of energy assets requires a digital twin to operate, however, most approaches so far are focused predominantly on technical indicators only and neglect the individuality of people and their behaviour in the operation. To fully integrate human led actions into such a system, the digital twin must therefore also provide social and psychological indicators to facilitate better predictability of reactions to demand response triggers. Methods: In the following a behaviour digital twin model will be presented based on the theory of planned behaviour and the self-determination theory, which provide well-established and validated tools to capture indicators of intention and motivation. The key contribution of this work is to operationalise and combine these models into a software tool, which continuously adapts its parameters to the evolving behaviour of users and provides up-to-date predictions. Results: The resulting model predicts the likelihood of each individual to react to appropriate demand response triggers, which can be used in model predictive control involving human actors to optimally select whom to target and when. Conclusions: The presented behaviour digital twin aims at bridging the gap between research in psychology to evaluate and assess drivers of behaviour and innovations in the space of model predictive control to optimally facilitate asset operation in residential settings.

Demand response of an Electric Vehicle charging station using a robust-explicit model predictive control considering uncertainties to minimize carbon intensity

This paper presents a novel approach to address uncertainties and enable demand response in Electric Vehicle (EV) charging station optimization. A two-stage optimization strategy is proposed, integrating Robust Optimization and explicit Model Predictive Control (eMPC). The first stage involves day-ahead planning using Robust Optimization technique to limit the hourly power consumption of EVs, considering worst-case scenarios caused by uncertainties in EV consumption and CO2 emissions. The objective is to minimize environmental impact by reducing CO2 emissions. An Explicit Model Predictive Control strategy is developed in the second stage for real-time operation. The explicit solution, calculated offline, models uncertainties such as the initial state of charge of the battery energy storage, photovoltaic power production, and EV power consumption. During real-time operation, the explicit solution is accessed using measured data from the charging station, refining the schedule derived from the first stage. The proposed solution is implemented and evaluated at an EV charging station in Trieste, Italy. The results demonstrate a significant 69% reduction in CO2 emissions compared to a deterministic approach while maintaining a real-time computation time of less than 0.1 s.

An improved state space model predictive control for linear systems with input disturbance

This paper presents an improved model predictive control (MPC) algorithm for linear systems with input disturbance. Based on the developed extended non-minimum state space input disturbance (ENMSS-ID) model, the input disturbance model structure is incorporated into the MPC framework and the objective function of the MPC optimization problem is improved to weigh the system output increments. This enables the algorithm simultaneously to achieve good input disturbance rejection performance for systems with known input disturbances and reduce the controllers’ sensitivity to model mismatch. An existing optimal estimation method is introduced to estimate the input disturbance, together with the proposed strategy to improve estimation convergence. Offset-free property is also proven to show the steady-state performance of the designed control scheme. Finally, two benchmark plants are studied to illustrate the effectiveness and advantages of the proposed algorithm.

Model Predictive Current Control of Permanent Magnet Synchronous Motor Based on Sliding‐Mode Disturbance Observer

A nonlinear multivariable highly coupled system that is susceptible to both internal and external perturbations is the permanent magnet synchronous motor. A new reaching law‐based control approach that combines a sliding‐mode disturbance observer and an enhanced two‐vector model predictive current control is suggested in order to enhance the dynamic performance of the permanent magnet synchronous motor and its robustness to system disturbances. First, a new reaching law is developed in this paper to resolve the conflict between the traditional reaching law's chattering of the system and the reaching time of the sliding‐mode surface. This new reaching law not only makes the system's response more rapid but also reduces chattering; second, taking into account the possibility of systems with internal parameter uptake and external load disturbance, a sliding‐mode disturbance observer based on t is developed. Finally, the q‐axis current obtained from the speed sliding‐mode control rate is added to the model predictive current control, and an optimal duty cycle model predictive current control based on space vector modulation is designed because the traditional two‐vector model predictive current control is to optimize the duty cycle and voltage vectors separately, and the switching frequency is not fixed. The method suggested in this research may successfully suppress the sliding‐mode control system chattering and increase the resilience and dynamic response performance of the speed control system, according to simulation findings compared to the traditional reaching law and traditional duty cycle. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Development of Local Path Planning Using Selective Model Predictive Control, Potential Fields, and Particle Swarm Optimization

This paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach involves defining multiple sets of coefficients for adaptability to the surrounding environment. The simulation results demonstrate that the algorithm is appropriate for generating obstacle avoidance paths. The algorithm was implemented on the ROS platform using NVIDIA’s Jetson Xavier, and driving experiments were conducted with a steer-type AGV. Through measurements of computation time and real obstacle avoidance experiments, it was shown to be practical in the real world.

Enhanced Model Predictive Control for Induction Motor Drives in Marine Electric Power Propulsion System

Marine electric propulsion is an important topic in the research of modern ships and underwater vehicles. The propulsion motor drives based on model predictive control (MPC) are becoming increasingly popular in marine propulsion systems as an emerging technology. However, the multi-objective optimization in conventional MPC requires cumbersome weighting factor tuning. The relatively large computational cost is also detrimental to the industrial application of MPC. Aiming at reducing the computational complexity of multi-objective optimization without weighting factors, this paper proposes an enhanced ranking-based MPC method for induction motor drives in marine electric power propulsion. The presented control set pre-optimization aims to reduce the computational complexity of enumeration and ranking. Based on the sign of torque prediction deviation, the proposed method avoids enumerating all fundamental voltage vectors. Consequently, the number of candidate elements in the initial control set are reduced to four without excessively excluding feasible solutions. By converting predicted numerical errors into ranking results, the proposed MPC seeks the optimal solution among the candidates through improved ranking evaluation. Considering the situation of simultaneous optimal ranking, the normalization error judgment is developed to further optimize the optimal solution selection process. The simulation and experimental results confirm that the proposed MPC is simple and effective. Without the involvement of tuning the weighting factors, the proposed method achieves good performance.

Optimal Transport and Model Predictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm

This paper presents a simultaneous task assignment and trajectory planning method for unmanned system swarm by using optimal transport and model predictive control (OT-MPC). Unlike the conventional hierarchical assignment and planning, the proposed approach addresses both the task assignment and trajectory planning subproblems concurrently. To be specific, a unified cost function is designed to solve task assignment and trajectory planning problem. Moreover, the multi-tasks are assigned by using optimal transport, which establishes an optimal mapping between tasks and unmanned system vehicles based on transportation cost. The trajectory planning is achieved by using model predictive control, which generates high-quality navigation trajectories considering obstacle avoidance. Finally, the proposed method is applied to the unmanned surface vehicles swarm. Numerical simulations and experiments were conducted to validate the effectiveness of the proposed method.

An Optimized Model Predictive Control Method With Fixed Switching Frequency for Eliminating Common-Mode Voltage of Five-Level Converters

In this paper, an optimized model predictive control (MPC) method with fixed switching frequency (FSF) is proposed to eliminate the common-mode voltage (CMV) and balance the capacitor voltage. Firstly, the zero CMV vector diagram is divided by finite control set MPC (FCS-MPC) to obtain the candidate vectors effectively. Then, in order to control the capacitor voltage and avoid the great computational burden originated from the redundant switching combinations, a piecewise control scheme is proposed to determine the required ones directly. The sequence generated by the selected vectors is further optimized for the purpose of saving switching transitions according to the internal and external parts of the vector diagram. Finally, the duty ratios are evaluated based on the results of the FCS-MPC in dividing sectors so as to avoid any extra complex calculation. The experimental evaluation has been carried out to validate the feasibility and effectiveness of the proposed method.

A Suboptimal Economic Model Predictive Control Algorithm for Large and Infrequent Disturbances

We present a suboptimal economic model predictive control (MPC) algorithm that combines the strengths of two common suboptimal MPC approaches, one based on a warm start and one based on an optimality gap. This algorithm is specifically designed to address a class of large and infrequent disturbances that are relevant when considering discrete actuators and production scheduling in control problems. We establish that this algorithm provides the same nominal performance guarantee as optimal economic MPC and is inherently robust, in an economic context, to large and infrequent disturbances. This inherent robustness guarantee is not attained by either individual algorithm. We conclude with a small production scheduling example to demonstrate the benefits of the proposed algorithm for a practical application.

Load frequency control of an isolated microgrid using optimized model predictive control by GA

Load frequency control of an isolated microgrid using optimized model predictive control by GA

Synergizing Wind and Solar Power: An Advanced Control System for Grid Stability

In response to the escalating global energy crisis, the motivation for this research has been derived from the need for sustainable and efficient energy solutions. A gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, has been recognized, leading to the introduction of a novel hybrid system that combines photovoltaic (PV) and wind energy. The innovation of this study lies in the methodological approach that has been adopted, integrating dynamic modeling with a sophisticated control mechanism. This mechanism, a blend of model predictive control (MPC) and particle swarm optimization (PSO), has been specifically designed to address the fluctuations inherent in PV and wind power sources. The methodology involves a detailed stability analysis using Lyapunov’s theorem, a critical step distinguishing this system from conventional renewable energy solutions. The integration of MPC and PSO, pivotal in enhancing the system’s adaptability and optimizing the maximum power point tracking (MPPT) process, improves control efficiency across key components like the doubly fed induction generator (DFIG), rectifier-sourced converter (RSC), and grid-side converter (GSC). Through rigorous MATLAB simulations, the system’s robust response to changing solar irradiance and wind velocities has been demonstrated. The key findings confirm the system’s ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. Not only has this study filled a crucial gap in renewable energy control systems, but it has also set a precedent for future research in sustainable energy technologies.

A new Correlation-Similarity Conjoint Algorithm for developing Encoder-Decoder based deep learning multi-step prediction model of chemical process

As an important procedure for the multi-step modeling of chemical process, the feature selection approach for unequal-length process variable series is frequently studied based on statistical analysis rather than chemical mechanism. As such, in this contribution, a convolution-based Correlation-Similarity Conjoint Algorithm (CSCA) considering chemical process mechanism including underlying variable interactions is developed to perform correlation analyses between unequal-length variable series, so that the most correlated features with minimum redundancy can be detected for the multi-step prediction modeling of variable series. Aided by CSCA, the chemical encoder-decoder based deep learning models are built for multi-step predicting flowrates, pressures and temperatures in the actual industrial process. The proposed CSCA proves to be effective in series feature selection, promoting the developments of the multi-step prediction encoder-decoder based deep learning model of different process variables, which are of great value for prospective model predictive control and real-time optimization in chemical engineering.

Stability Margin of Data-Driven LQR and Its Application to Consensus Problem

In contrast with traditional control input design techniques based on mathematical models of the system, in data-driven control approaches, which have recently gained substantial attention, the controller is derived directly from the data that are collected from experiments or observations of the target system. In particular, several data-driven optimal control and model predictive control (MPC) techniques have been proposed. In this paper, it is shown that the recently proposed data-driven LQR (Linear Quadratic Regulator) has a stability margin that is the set of the uncertainties in the control input channels maintaining the closed-loop stability. As an application of the proposed stability margin of the data-driven LQR, the consensus problem is considered. Since the control design for the consensus of multi-agent systems can be reformulated into the robust stabilization of a linear system with uncertainty in the input channel, it is demonstrated that the derived stability margin can be used to design a controller for the consensus of multi-agent systems.

Controller Design for Air Conditioner of a Vehicle with Three Control Inputs Using Model Predictive Control

Fuel consumption optimization is a critical field of research within the automotive industry to meet consumer expectations and regulatory requirements. A reduction in fuel consumption can be achieved by reducing the energy consumed by the vehicle. Several subsystems contribute to the overall energy consumption of the vehicle, including the air conditioning (A/C) system. The loads within the A/C system are mainly contributed by the compressor, condenser fan, and underhood aerodynamic drag, which are the components targeted for overall vehicle energy use reduction in this paper. This paper explores a new avenue for A/C system control by considering the power consumption due to vehicle drag (regulated by the condenser fan and active grille shutters (AGS)) to reduce the energy consumption of the A/C system and improve the overall vehicle fuel economy. The control approach used in this paper is model predictive control (MPC). The controller is designed in Simulink, where the compressor clutch signal, condenser fan speed, and AGS open-fraction are inputs. The controller is connected to a high-fidelity vehicle model in Gamma Technologies (GT)-Suite (which is treated as the real physical vehicle) to form a software-in-the-loop simulation environment, where the controller sends actuator inputs to GT-Suite and the vehicle response is sent back to the controller in Simulink. Quadratic programming is used to solve the MPC optimization problem and determine the optimal input trajectory at each time step. The results indicate that using MPC to control the compressor clutch, condenser fan, and AGS can provide a 37.6% reduction in the overall A/C system energy consumption and a 32.7% reduction in the error for the air temperature reference tracking compared to the conventional baseline proportional integral derivative control present in the GT-Suite model.

Alternating Direction Method of Multipliers-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control

Alternating Direction Method of Multipliers-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control

Safety-Preserving Lyapunov-Based Model Predictive Rendezvous Control for Heterogeneous Marine Vehicles Subject to External Disturbances.

This article investigates the cooperative rendezvous control problem for perturbed heterogeneous marine systems composed of an autonomous underwater vehicle (AUV) and an autonomous surface vehicle (ASV). A novel Lyapunov-based model predictive control (LMPC) framework is presented to accomplish safe and precise rendezvous under input limitations and external disturbances. First, by incorporating the prescribed performance control (PPC) technique into the LMPC framework, we transform the original ascending state of the AUV into a self-constrained state, which serves as the decision variable of the model predictive control (MPC) optimization problem. Then, PPC-aided auxiliary control laws based on disturbance observers (DOBs) are designed to establish a robust contractive constraint to provide stability margins. Combining the LMPC with the PPC technique makes the original state-constrained problem an equivalent state-constraint-free problem. By addressing the MPC problem for the equivalent unconstrained system, the proposed method preserves the rendezvous safety. With the robust contractive constraint, the proposed safety-preserving LMPC (SP-LMPC) controller can inherit robustness and stability from the robust auxiliary control laws. Furthermore, theoretical analyses are conducted to assess recursive feasibility and closed-loop stability. With comprehensive theoretical support, the proposed method provides a new framework to simultaneously address state constraints and disturbances for highly nonlinear marine systems. Finally, simulations and comparisons are conducted to demonstrate the effectiveness and advantages of the proposed algorithm.