Model Predictive Control Controller Research Articles

Current control for a supercapacitor-based battery equalization system

This paper presents a novel supercapacitor-based energy equalization system and discusses a new equalization current control method. The proposed battery equalization system is composed of a bidirectional boost–buck circuit, a switch matrix, and a supercapacitor, which can realize stable electric current transmission between batteries and supercapacitors. The buck or boost mode of the circuit is triggered automatically based on the threshold of the voltage drop between the battery and the supercapacitor. A modified control logic of the metal-oxide-semiconductor field-effect transistor is proposed to improve the efficiency of the circuit, and a model predictive control (MPC) algorithm is designed to track the target current. Simulation results indicate that for a conventional electric current transmitting circuit, the current fluctuates violently under a fixed pulse width modulation duty. In contrast, the proposed supercapacitor-based energy equalization system tracks the target current well under the control of the MPC controller. In a wide operating range, the transmission efficiency of the new energy equalization system with the modified control logic is 13.0% higher than that of the conventional electric current transmitting circuit with the original control logic.

MPC motion control of boom descending of unmanned excavator based on regenerative valve

The control precision of the working device has always been a challenging aspect in unmanned excavator research due to the adoption of a triangular drive mode and a complex hydraulic system in the working mechanism. The article focuses on the research of autonomous control for the downward motion of a robotic arm in an unmanned excavator equipped with a regeneration valve. The study aims to achieve precise tracking of fast movement trajectories during operator manipulation, utilizing Model Predictive Control (MPC). Furthermore, the exceptional disturbance rejection capability of the MPC algorithm is demonstrated through interference application. A comprehensive model considering mechanical, hydraulic, and electrical factors is established for the excavator boom. The MPC algorithm is applied to achieve precise control over the boom descent process, providing a foundation for motion control in unmanned excavators. This article presents a theoretical analysis to elucidate the robustness principle of MPC in the descent control of uncertain dynamic arms. By incorporating real parameters, we successfully track predetermined planned paths at different speeds and validate them on a 20-ton hydraulic excavator. The results demonstrate that the MPC control algorithm accurately manipulates the boom descent motion while exhibiting excellent disturbance rejection performance. Compared to PID control algorithms, MPC offers wider target adaptability range and better disturbance rejection performance, making it suitable for rapid application in controlling working devices of unmanned excavators.

Koopman operator-based multi-model for predictive control

AbstractThis work describes a new model structure developed for prediction in Model Predictive Control (MPC). The model has a multi-model structure in which independent sub-models are employed for the consecutive sampling instants. The model lifts process states into a high-dimensional space in which a linear process description is applied. Depending on the influence of the manipulated variables on lifted states, three general model versions are described and model identification algorithms are derived. As a result of the multi-model structure, model parameters are found analytically from computationally uncomplicated least squares problems using the Extended Dynamic Mode Decomposition algorithm, but the evolution of states over the horizon used in MPC is taken into account. Next, the MPC algorithm for the described model is derived. It requires solving online simple quadratic optimisation tasks. The effectiveness of three considered model configurations and three versions of the lifting functions is examined for a nonlinear DC motor benchmark. Their impact on model accuracy, complexity, possible control accuracy and MPC calculation time is thoroughly discussed. Finally, a more complex polymerisation reactor process is considered to showcase the practical applicability of the presented approach to modelling and MPC.

Design principles of voltage controlled DC-DC boost converters through the integration of PID and model predictive control

This paper presents the design of the transfer function between the control and output variable of a DC-DC boost converter using a combination of Proportional Integral Derivative (PID) and output-based Model Predictive Control (MPC) mechanisms for eradicating the effect of the non-minimum phase (NMP) behavior on the system. This is made possible due to the existence of a right half plane (RHP) zero. It is observed that the MPC outperformed the conventionally designed PID approach by a significant margin. Based on the computed results, it can be emphasized that the combination of output-based MPC and PID controller demonstrated a strong alignment with optimal designs, exhibiting a fast convergence rate than PID and PID-IMC. Simulation results are presented to showcase the fast transient response and a high level of robustness of the proposed control methodology.

Trajectory Tracking Control Design for 4WS Vehicle Based on Particle Swarm Optimization and Phase Plane Analysis

With the rapid development of today’s society, the traffic environment has become more and more complex. As an essential part of intelligent vehicles, trajectory tracking has attracted significant attention for its stability and safety. It is prone to poor accuracy and instability in extreme working conditions like high speed. In this paper, a trajectory tracking control strategy to ensure lateral stability at a high speed and low attachment limit conditions is proposed for distributed drive vehicles. The model predictive controller (MPC) was used to control the front wheel angle, and the particle swarm optimization (PSO) algorithm was designed to optimize the MPC control parameters adaptively. The sliding mode controller controls the rear wheel angle, and the vehicle instability degree is judged by analyzing the β − β˙ phase plane. The controllers of different instability degrees are designed herein. Finally, a torque divider is designed to consider the actuation anti-slip. The designed controller is verified by Carsim and MATLAB-Simulink co-simulation. The results show that the trajectory tracking controller designed in this paper effectively improves the tracking accuracy under the premise of ensuring stability.

Coordinating multiple Power-To-Gas plants for optimal management of e-fuel seasonal storage

Seasonal storage is a key feature of future decarbonized energy systems with a high share of renewable energy integration. Power-to-Gas technologies represent a promising solution to enable such storage. They allow the conversion of surplus renewable electricity into e-fuels and their storage in the long-term. Their utilization enables the integration of the electrical, fuel and heating sectors, by converting electricity into fuels and recovering the waste heat from the process. Nevertheless, to design the most profitable management strategy for such systems, advanced control tools are required. This study introduces a novel control architecture for multiple multi-energy systems that share an e-fuel seasonal storage. Each energy system has its own short-term control logic, based on Model-Predictive Control (MPC), which manages day-ahead energy exchanges, while a long-term MPC controller considers yearly dynamics and the system as a whole. This gives additional constraints to the short-term controllers, which ensure the fulfillment of yearly goals. A multi-temporal and multi-spatial hierarchical control architecture is proposed, which enables optimal seasonal storage management, and its operation is verified in a Model-in-the-Loop configuration. The controller efficiently uses seasonal storage to balance seasonal mismatch between production and demand, resulting in higher utilization of renewable energy, lower emissions and costs.

Research on model predictive control of autonomous underwater vehicle based on physics informed neural network modeling

In the rapidly evolving field of Autonomous Underwater Vehicles (AUVs), achieving precise control remains a critical endeavor. This study presents a pioneering integration of Model Predictive Control (MPC) with a Physics-Informed Neural Network (PINN), aiming to enhance control system precision and operational efficiency in AUVs. The efficacy of MPC lies in its adept handling of the intricate constraints and inherent nonlinear dynamics intrinsic to AUV systems. Concurrently, the PINN architecture incorporates the fundamental physical laws represented by Partial Differential Equations (PDEs), augmenting the predictive fidelity of the system. Firstly, this research implements the novel PINN-enhanced MPC framework for trajectory tracking and conducts a comparative evaluation against adaptive proportional-integral-derivative (PID) and Gaussian-process-based MPC controllers. This comparative analysis elucidates the advancements in control mechanisms attributable to the PINN integration. Furthermore, this study meticulously assesses the PINN-MPC's proficiency in navigating through static and dynamic obstacles within three-dimensional marine environments, a critical capability for AUV operations. Through extensive and meticulous simulations, the proposed approach demonstrates notable progress in overcoming environmental challenges and executing intricate operational tasks, such as obstacle avoidance, with heightened efficiency and dexterity. This research constitutes a substantial contribution to the theoretical advancement and elucidation of control systems in the AUV domain, bearing profound practical implications. It lays the foundation for the development of increasingly sophisticated, advanced, and reliable AUV missions, signifying a crucial advancement in the realms of underwater exploration and operational technology.

Intelligent Reduced-Dimensional Scheme of Model Predictive Control for Aero-Engines

Model Predictive Control (MPC) has many advantages in controlling an aero-engine, such as handling actuator constraints, but the computational burden greatly obstructs its application. The current multiplex MPC can reduce computational complexity, but it will significantly decrease the control performance. To guarantee real-time performance and good control performance simultaneously, an intelligent reduced-dimensional scheme of MPC is proposed. The scheme includes a control variable selection algorithm and a control sequence coordination strategy. A constrained optimization problem with low computational complexity is first constructed by using only one control variable to define a reduced-dimensional control sequence. Therein, the control variable selection algorithm provides an intelligent mode to determine the control variable that has the best control effect at the current sampling instant. Furthermore, a coordination strategy is adopted in the reduced-dimensional control sequence to consider the interaction of control variables at different predicting instants. Finally, an intelligent reduced-dimensional MPC controller is designed and implemented on an aero-engine. Simulation results demonstrate the effectiveness of the intelligent reduced-dimensional scheme. Compared with the multiplex MPC, the intelligent reduced-dimensional MPC controller enhances the control quality significantly by 34.06%; compared with the standard MPC, the average time consumption is decreased by 64.72%.

Energy-efficient operation of the thermal management system in electric vehicles via integrated model predictive control

The thermal management system (TMS) in electric vehicles (EVs) is a comprehensive system that integrates an air conditioning system for the cabin, a temperature control system for the battery, and a cooling system for the motor. The currently used PI control strategy can only meet the basic TMS functions and cause high energy consumption. In this paper, we present a novel model predictive control (MPC) strategy for the TMS to optimize operational performance in real time. Different from the independent PI control for the individual components, MPC can predict future operation conditions and provide the optimal operating inputs in advance. A complete control-oriented model for MPC is developed, and the MPC strategy is designed to minimize the total power consumption of the TMS under the control-oriented model and constraints. The evaluation is carried out under several cases including the fixed ambient temperature, realistic ambient temperature, and different vehicle speeds. The results showed that the novel MPC strategy saved energy consumption by 5.9%–10.3% in these cases when compared to the PI strategy, demonstrating the effectiveness and feasibility of the proposed MPC control.

Comparison of Two-Wheel and Four-Wheel Steering Using Event-Triggered Predictive Motion Control and Scale Vehicles

Abstract This study compares the trajectory tracking performance of two- and four-wheel steering systems, especially under normal driving conditions. Specifically, the lateral motion is controlled by an event-triggered model predictive control (MPC), which activates either when consecutive control steps surpass the predictive horizon or when tracking error exceeds a predetermined lateral offset. Using a modified 1/10th scale Tamiya TT-02 RC car as a test platform, the tracking performance of both two- and four-wheel systems are evaluated. Results from the experiments highlight the better tracking performance of the four-wheel steering system over the traditional two-wheel systems and demonstrate the benefit of using event-triggered MPC for lateral motion control even under normal driving conditions, contrary to common belief that four-wheel steering systems are beneficial only in tight steering maneuvers.

Integrating Model Predictive Control With Stormwater System Design: A Cost‐Effective Method of Urban Flood Risk Mitigation During Heavy Rainfall

AbstractThe integration of green‐gray infrastructures with advanced control approaches is revolutionizing the stormwater system retrofitting, emerging as an innovative strategy to mitigate urban flood risks. However, a major challenge lies in balancing the substantial investments of these infrastructure projects with their environmental benefits, such as reduced flooding volume and lower peak flow. Model predictive control (MPC), a dynamic and intelligent control approach, optimizes these environmental benefits but is underutilized in the system design phase for cost‐effectiveness analysis. This study introduces a multi‐scenario model framework that incorporates MPC and other control approaches into stormwater system designs, including the implementation of controlled storage tanks and green infrastructures. This framework provides comprehensive modeling tools for practitioners to evaluate the flood control benefits and costs across various infrastructure designs and control scenarios, ultimately identifying solutions that are both environmentally and economically viable. A case study conducted in a small urban catchment area in Shenzhen City, China, demonstrates the effectiveness of this framework. The results indicate that MPC outperforms other control scenarios, particularly under heavy or extreme rainfall conditions. Notably, MPC not only provides superior environmental benefits but also yields considerable cost savings, ranging from 1,787 to 9,371 USD per hectare compared to static control, equating to a 5% reduction in cost relative to rule‐based control. Such findings suggest that integrating MPC is a cost‐effective alternative to extensive infrastructure expansion for flood management, which significantly enhances the benefit contribution of controlled infrastructures without substantial additional expenses.

Networked Output-Feedback MPC: A Bounded Dynamic Variable and Time-Varying Threshold-Dependent Event-Based Approach.

The event-triggered model predictive control (MPC) problem is addressed for polytopic uncertain systems. A new dynamic event-triggered mechanism (DETM) with a bounded dynamic variable and a time-varying threshold is proposed to manage measurement data packet releases. The dynamic output-feedback MPC issue is detailed as a "min-max" optimization problem (OP) with an objective function over an infinite horizon, where the hard constraint on the predictive control is required. By applying a Lyapunov-like function containing the bounded dynamic variable, an auxiliary OP constrained by several matrix inequalities is proposed, and the design methods of the output-feedback gains are provided if this auxiliary OP is feasible. The designed MPC controller ensures that the closed-loop system is input-to-state practically stable. Two examples including an event-triggered DC motor are given to illustrate the validity of the developed MPC algorithm. Simulation results verify that the proposed DETM has advantages over some existing triggering mechanisms in decreasing the consumption of resources while meeting the required performance.

A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances

The path tracking performance of autonomous vehicles is degraded by common disturbances, especially those that affect the safety of autonomous vehicles (AVs) in obstacle avoidance conditions. To improve autonomous vehicle tracking performances and their computational efficiency when subjected to common disturbances, this paper proposes a tube linear model predictive controller (MPC) framework for autonomous vehicles. A bicycle vehicle dynamics model is developed and employed in the tube MPC control design in the proposed framework. A robust invariant set is calculated with an efficient linear programming (LP) method, and it is used to guarantee that the constraints are satisfied under common disturbance conditions. The results show that the computational cost of robust positively invariant sets that are constructed by the LP method is much less than that obtained by the traditional method. In addition, all the trajectories of the tube linear MPC successfully avoided obstacles when under disturbance conditions, but only about 80% of the trajectories obtained with the traditional MPC successfully avoided obstacles under disturbance conditions. The proposed framework is effective.

Comparison of time-invariant and adaptive linear grey-box models for model predictive control of residential buildings

Model predictive control (MPC) is a promising optimal control technique for activating building energy flexibility using its thermal mass. The performance of the MPC controller is directly related to the accuracy of the model prediction. Grey-box models, based on physical laws and calibrated on measurement data, are commonly used to represent the building thermal dynamics in MPC. Most research works use Linear Time-Invariant (LTI) grey-box models even though weather conditions vary significantly throughout the heating season. This is critical as inaccurate model prediction can lead to a lower performance of the MPC controller. This study introduces two adaptive MPC schemes to overcome this limitation of LTI models. The first one, called the Partially Adaptive MPC, only updates the effective window area of the prediction model. The second one, called the Fully Adaptive MPC, updates all the parameters of the grey-box model. The adaptive MPC performance is compared with MPC using LTI models in two different tests. The simulation-based results show that MPC based on LTI performs well if the control model is trained during a period with similar weather conditions as the period when the MPC will be applied. The Partially Adaptive MPC is unable to deliver satisfactory prediction performance due to the limited number of parameters that are updated. The Fully Adaptive MPC has the best performance compared to the other MPCs, especially as it avoids thermal comfort violations.

Reinforcement Learning-Based Event-Triggered Active-Battery-Cell-Balancing Control for Electric Vehicle Range Extension

Optimal control techniques such as model predictive control (MPC) have been widely studied and successfully applied across a diverse field of applications. However, the large computational requirements for these methods result in a significant challenge for embedded applications. While event-triggered MPC (eMPC) is one solution that could address this issue by taking advantage of the prediction horizon, one obstacle that arises with this approach is that the event-trigger policy is complex to design to fulfill both throughput and control performance requirements. To address this challenge, this paper proposes to design the event trigger by training a deep Q-network reinforcement learning agent (RLeMPC) to learn the optimal event-trigger policy. This control technique was applied to an active-cell-balancing controller for the range extension of an electric vehicle battery. Simulation results with MPC, eMPC, and RLeMPC control policies are presented along with a discussion of the challenges of implementing RLeMPC.

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

Real-world implementation of a cloud-based MPC for HVAC control in educational buildings

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building’s heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively.

Distributed MPC for Multi-Vehicle Cooperative Control Considering the Surrounding Vehicle Personality

In real traffic environment, a single control mode of traditional autonomous vehicles cannot meet various driving requirements for different drivers, which will decrease the acceptance of autonomous vehicles, and even may further cause traffic risks. This paper studies the cooperative strategies between ego vehicle and surrounding vehicles with the naturalistic experiment data, and then designs an autonomous vehicle control method based on the distributed Model Predictive Control (MPC) in order to consider the interaction relationship of ego vehicles and surrounding vehicles. Finally, the proposed method is verified by software simulation and Hardware in the Loop (HIL) simulation experiments, and the experiment results demonstrate that the control method proposed in this paper not only can control the vehicle to complete the typical driving tasks smoothly, in terms of car-following and lane-changing, but also can reflect the different cooperative strategies among different driving behavior characteristics, which can improve safety and acceptance of autonomous vehicles to promote the practical application of autonomous vehicle technology.

Event-Triggered Deep Learning Control of Quadrotors for Trajectory Tracking

This paper proposes an event-triggered deep learning control strategy to achieve real-time trajectory tracking control for quadrotors. In the training data collection phase, the event-triggered model predictive control (MPC) method is applied to the quadrotor in the simulation environment to generate training data. Then, a deep neural network (DNN) controller is trained to approximate the optimal control policy of the event-triggered MPC. To further save computing resources of on-board processor, the event-triggered mechanism is incorporated with the DNN controller, and the dual-mode approach is employed in it. Finally, simulation and experimental results show that the proposed controller can ensure almost similar trajectory tracking performance to the event-triggered MPC controller while requiring a lower control computation cost.

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.