Model Predictive Control Controller Research Articles

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.

Design of Multi-Input Multi-Output Non-linear Model Predictive Control for Main Steam Temperature of Super Critical Boiler

Flexible operation of coal-fired power plants is becoming increasingly necessary for successful integration of large-scale renewable power generation into the power grid. The maximum ramp rate and the number of load cycles are generally limited by the thermal stress experienced by the boiler pressure parts, turbine metallurgy and creep and fatigue of critical thick-walled components Main steam temperature is a critical operating parameter that must be controlled within acceptable limits for safe operation. Main steam temperature deviation beyond acceptable limit has impact on boiler pressure parts and turbine material of construction due to creep and fatigue effect. Base load operating units do not require steep ramp rate and hence recommended ramping rates are kept low within the safe operating zone in comparison to the flexible operation of the units with wide range load change width. Thermal stresses are caused by the temperature changes inside the thick-walled components and turbine steam admission parameters. Hence, the quality of main steam temperature control plays a vital role in flexible operation of the coal fired units. Conventional cascaded PID temperature control loop architecture performs well at steady state condition within a limited variation of load change at low ramp rate but it acts slowly and performs poorly at transient operating conditions of flexible operation of the boiler turbine with wide range load variation and load cycle with high ramp rate and remains far from rated conditions. In this paper, a Multi-Input Multi-Output (MIMO) Non-linear Model Predictive Control (MPC) design for regulation of the main steam temperature of a Once-Through supercritical Boiler is proposed. The controller is based on a non-linear dynamic model which incorporates dynamics of the variables of interest. It has the capability to operate effectively across a wide load range while maintaining main steam temperature within acceptable limits. A notable advancement in this design of MPC is the incorporation of coal flow demand and feedwater flow demand as additional control inputs alongside primary and secondary spray flows. In simulation test cases, the MPC controller demonstrates satisfactory performance and computational efficiency.

A time-varying driving style oriented model predictive control for smoothing mixed traffic flow

Connected and automated vehicles (CAVs) have great potential to smooth mixed traffic flow. To focus on the fact that the driving styles of HDVs are time-varying, a new control framework based on time-varying model predictive control (MPC) is proposed for mixed traffic flow in longitudinal control. Firstly, a genetic algorithm can be employed to calibrate the parameters of the car-following model based on offline trajectory data, representing the long-term driving style of HDVs. Secondly, the short-term driving style is recognized through modifying the long-term driving style based on real-time trajectory data within a sliding window. Then, this paper seeks to design a time-varying MPC control framework to realize a smoother mixed traffic flow. Finally, compared to the baseline model, the method we proposed achieves the high-performance both in fuel consumption and driving comfort under various scenarios, and maintains fast online calculations. Besides, it also displays the significant potential of saving fuel consumption in a large-scale traffic system, reducing by over 11.28% with 17∼33% penetration rates of CAVs.

A Helly Model-Based MPC Control System for Jam-Absorption Driving Strategy against Traffic Waves in Mixed Traffic

Traffic waves in traffic flow significantly impact road throughput and fuel consumption and may even lead to severe safety issues. Currently, in connected and autonomous environments, the jam-absorption driving (JAD) strategy shows good performance in dissipating traffic waves. However, the previous JAD strategy has mostly focused on wave dissipation without adequately assessing traffic efficiency and safety. To address this gap, an optimal control problem for JAD in mixed traffic is proposed to reduce traffic waves. The prediction model is developed using the car-following model within a model predictive control (MPC) framework. The Helly model is selected for the manual vehicle. This is because the Helly model is a linear model that describes the car-following phenomenon accurately without delay effect. In addition, the objective function of the prediction model considers both traffic safety and efficiency while satisfying mechanical and safety constraints. Simulation results indicate that the proposed methodology can effectively reduce traffic jams and improve traffic performance on a one-lane freeway. The optimal method is more applicable to complex traffic wave scenarios, providing a new perspective for reducing traffic jams on the freeway.

Koopman fault‐tolerant model predictive control

AbstractThis paper introduces a novel data‐driven approach to develop a fault‐tolerant model predictive controller (MPC) for non‐linear systems. By adopting a Koopman operator‐theoretic perspective, the proposed method leverages historical data from the system to construct a data‐driven model that captures the non‐linear behaviour and fault characteristics. The fault influence is addressed through an online estimation of a time‐varying Koopman predictor, which allows for adjusting the MPC control law to counteract the fault effects. This estimation is performed in a higher dimensional Koopman feature space, where the dynamics behave linearly. As a result, the non‐linear fault‐tolerant MPC optimization problem can be replaced with a more practical and feasible linear time‐varying one using the approximated Koopman predictor. Moreover, by incorporating the online update procedure, the time‐varying Koopman predictor can represent the dynamics of the faulty system. Hence, the controller can adapt and compensate for the faults in real‐time, integrating the fault diagnosis module in the MPC framework and eliminating the need for a separate fault detection unit. Finally, the efficacy of the proposed approach is demonstrated through case study results, which highlight the ability of the controller to mitigate faults and maintain desired system behaviour.

Enhancing fuel cell performance through a dual MPC strategy for coordinated temperature management

Ensuring the optimal operating temperature is imperative for achieving efficient performance in proton exchange membrane fuel cells. Consequently, this study introduces a dual-model predictive control strategy to regulate the water pump and cooling fan in a cooling system. Initially, we establish an electrochemical and thermal model for fuel cell stacks and validate the model’s accuracy through experimental data. The system model is linearized, and the model predictive control (MPC) controller is formulated using the MATLAB/Simulink toolbox. Subsequently, it is collaboratively simulated with the electrochemical model of the fuel cell stack and the temperature model. To evaluate the effectiveness of the MPC controller, we conducted a comparative analysis with the traditional proportional–integral–derivative (PID) control and water pump MPC under step load, uniform load increase, and variable target scenarios. The findings indicate that in contrast to the PID control, the MPC controller significantly decreases the stack temperature difference fluctuation by more than 50%, maintaining the stack temperature within ±0.6 K of the set value. Furthermore, we independently assessed the performance of the MPC controller under varying ambient temperatures. The findings illustrate that the dual MPC method proficiently adapts cooling parameters across different ambient temperature ranges (288.15 K–308.15 K), ensuring the stable performance of the fuel cell. The model is linearized, and the simulation work is explained mainly on the MATLAB/Simulink platform. In order to compare the effectiveness of the MPC controller, the comparison with the MPC controller strategy of the water pump is added, which can better reflect the effectiveness of the proposed collaborative MPC controller strategy.

Path tracking control of automated vehicles based on adaptive MPC in variable scenarios

AbstractFor complex and dynamic high‐speed driving scenarios, an adaptive model predictive control (MPC) controller is designed to ensure effective path tracking for automated vehicles. Firstly, in order to prevent model mismatch in the MPC controller, a tire cornering stiffness estimation algorithm is designed and a soft constraint on slip angle is added to further enhance the controller's precision in tracking trajectories and the vehicle's driving stability. Secondly, the improved particle swarm optimization (IPSO) method with dynamic weights and penalty functions is suggested to address the issue of insufficient accuracy in solving quadratic programming. Additionally, the standard particle swarm optimization (PSO) algorithm is used to seek the most suitable time horizon parameters offline to obtain the best time horizon data set under different vehicle speeds and adhesion coefficients, and then it is optimized online by an adaptive network‐based fuzzy inference system (ANFIS) to enhance the model predictive controller's adaptability in different operating conditions. Finally, simulation experiments are conducted under three different operating conditions: docked roads, split roads, and variable vehicle speeds. The results indicate that the designed adaptive MPC controller can accurately and stably track the reference trajectory in various scenarios.

Model predictive control for automatic operation of space nuclear reactors: Design, simulation, and performance evaluation

Space nuclear reactors possess the inherent benefits of independence from solar energy and the capability to withstand intricate external circumstances. Distinguishing themselves from terrestrial reactors, space nuclear reactors function within the realm of outer space, operating autonomously throughout their entire operational cycles. Consequently, the establishment of a dependable automatic control system assumes utmost significance. In this paper, a high-fidelity nonlinear space thermionic nuclear reactor model is established, encompassing neutron physics, thermal–hydraulic, and thermoelectric conversion system models. An automatic control system with a model predictive controller (MPC) is designed based on the system. A simplified state-space model is established based on system identification as the internal model for the MPC controller, demonstrating good agreement with the dynamic response of the nonlinear system model. The deployment of the MPC controller for system control simulations exhibits excellent control performance under various operating conditions. In the case of a nuclear power step response, the MPC controller achieves an overshoot of 0.07 % and a settling time of 0.65 s. For an electric power step response, the MPC controller yields an overshoot of 0.28 % and a settling time of 36.6 s. These control performances are significantly better than those achieved by the PID controller. In power tracking control, the MPC controller demonstrates virtually error-free tracking performance. When external reactivity disturbances are introduced, the MPC controller outperforms the PID controller in swiftly compensating for the disturbances, maintaining the system's operating state at the desired level.

Optimization of frequency dynamic characteristics in microgrids: An improved MPC-VSG control

For the power imbalance caused by the load switching in microgrids (MGs), which in turn causes the frequency crossing limit problem. In this paper, we propose an improved model predictive control (MPC) based on the existing MPC-VSG control, combining adaptive inertia damping control and adaptive weight coefficient control for joint control, and adjusting the inertia damping coefficient of VSG in real-time by adaptive inertia damping control. In MPC control, the measured parameters at the current moment are used to predict the state quantities at the future moment, and then the target function is designed with the angular frequency deviation and the VSG power output as performance indicators, and the weight coefficients in the target function are updated in real-time by detecting the frequency deviation. After solving the objective function to obtain the required power compensation, the power reference value of the VSG is updated to optimize the dynamic response of the frequency. Experiment and comparison analysis with two existing methods show that the proposed method can further optimize the frequency response and smooth the output power of other power supply components in the microgrid when they are disturbed.

Comparison of Fuzzy Logic Control and Model Predictive Control for a Smart Adaptive Cruise Control Vehicle System

Adaptive Cruise Control (ACC), Cruise Control (CC), and Automatic Emergency Braking (AEB) serve as the basis of longitudinal automated driving, and as Adaptive cruise control (ACC), cruise control (CC), and automatic emergency braking (AEB) serve as the basis of longitudinal automated driving, and as such have been the subject of much research. Model predictive control (MPC) and fuzzy logic are often considered to be the next steps in improving the capability of these systems, but the two control strategies have not been compared to each other in the ACC, CC and AEB applications. Also, the three features (ACC, CC and AEB) have never been compiled into a single fuzzy logic controller. The purpose of this paper is to design a fuzzy logic-based ACC, CC, and AEB controller and compare it to an equivalent MPC controller. All three controllers control the desired longitudinal acceleration, and their functionality is tested using Matlab’s Fuzzy Logic Designer and other Simulink toolboxes. Ultimately, the results of the analysis demonstrate that the proposed fuzzy controller operates just as well if not better than the MPC controller and that the fuzzy controller is able to operate well in all tested scenarios.

Hierarchical Model Predictive Control for Modular Multilevel Converters: A Linear Parameter Varying Approach

Modular multilevel converters (MMCs) are novel voltage source converters (VSCs) with the capacity to achieve higher efficiency and lower total harmonics distortion, but more challenging to control. MMCs can be modelled using a hierarchical structure comprising a model predictive controller (MPC) that controls the current in the top layer and sends signals to capacitor voltage modules in the lower layer (which can run in open-loop or closed-loop with a simple local controller). Typically, the prediction model used in the MPC current controller does not take into account the capacitor voltage dynamics in the bottom layer, which reduces the efficiency of the hierarchical control scheme and MMC circuit. Therefore, in this paper, we develop an extended prediction model for current control that includes the capacitor voltage dynamics for the modules in the bottom layer. This results in a nonlinear MPC control problem of higher complexity but with improved performance. To reduce the complexity of the nonlinear MPC problem for MMCs, we make use of a linear parameter varying embedding of the nonlinear prediction model, which allows solving a sequence of quadratic programs online instead of a nonlinear program. The proposed control scheme shows better performance while decreasing the computation load. Furthermore, compared to classical hierarchical control schemes, the proposed scheme reduces the capacitor voltage ripple by 20%.

Stability Analysis and Controller Synthesis Using Single-Hidden-Layer ReLU Neural Networks

This paper presents algorithms to solve analysis and controller synthesis problems for dynamical systems modeled as a recurrent single-hidden-layer rectified linear unit neural network (ReLU NN), or equivalently, a piecewise affine dynamical system. Such models are interesting since they may arise through the use of modern machine learning methods for system identification, or as closed-loop solutions in certain classes of model predictive control (MPC) problems. A key idea in the proposed approach is to use piecewise affine Lyapunov functions parametrized as ReLU neural networks, and similarly parametrized controllers. This compatible representation between the Lyapunov function and the dynamics simplifies the automation of analysis of and controller synthesis for learned models. Our method of verifying a candidate Lyapunov function is faster than methods based on mixed integer programming. We ‘learn’ controllers and Lyapunov functions using both weight updates and network architecture search, without gradients. We demonstrate the proposed algorithm on examples involving learned models, explicit MPC controllers, and constrained controller synthesis.

Desain dan Simulasi Model Predictive Control pada Sistem Pembagian Daya untuk Kendaraan Listrik Hibrida Fuel Cell – Baterai

This research examines the performance of implementing Model Predictive Control (MPC) in the energy management system of fuel cell – battery hybrid electric vehicles. Three parameter variations were carried out on the MPC controller, namely variations in the horizon value, objective function, and weighting in the objective function. In testing variations in the horizon value of the designed MPC controller, it shows that the higher the horizon value used, the SOC and the final SOC are faster and closer to the optimal SOC determined by compensating for higher hydrogen consumption. Testing of objective function variations shows that the objective function implemented on the MPC controller influences the system response characteristics. It was found in objective function testing that optimal use of fuel cell power produces fuel cell power output with a working efficiency range of 57% - 60% when compared to other objective functions that work in the efficiency range of 49.2% - 57%. Finally, in testing variations in weighting values, it was found that the higher the weighting of an expression in the objective function, the more the optimizer will penalize the expression so that the solver will minimize the expression in the optimization process. Therefore, the MPC controller parameter values ​​need to be paid attention to so that the response characteristics are in accordance with the design. which are desired. It should be noted that this research does not use a speed prediction model so it is assumed that the speed is known without any uncertainty.

Active alleviation of fatigue stress on blades by adaptively maneuvered deformable trailing edge flaps (DTEF).

The concept of "smart rotor" is an evolving advancement in wind turbine which enables an intelligent active flow control in rotor. The deformable trailing edge flap (DTEF) is a part of smart rotor concept which implements a customized active load control. The trailing edge flap actuator effectively replaces the tedious blade pitch actuation and conserves the actuation energy required for pitching the entire blade. The DTEFs require a fast computing, anticipatory controller for optimally tuning the flap angle with minimal power compromise. This work analyzes the performance of advanced control strategies like model predictive control (MPC), adaptive MRAC control, and DQ controllers. The MRAC controller is found to reduce the fatigue stress by 40% and the MPC controller damps up to 70% more efficiently than the typical feedback controller. The control strategies are aided by the LiDAR-based preview wind data for the active manipulation of trailing edge flap angle control. The validation of proposed controller is done using power analysis curve and the component fatigue lifetime analysis using MLIFE software. The above analyses are done in NREL Onshore 5-MW FAST wind turbine model which could be interfaced with MATLAB with modified AeroDyn code for active flap deflection.

Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm

The deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm.

Fast Nonlinear Predictive Control Using Classical and Parallel Wiener Models: A Comparison for a Neutralization Reactor Process

The Wiener model, composed of a linear dynamical block and a nonlinear static one connected in series, is frequently used for prediction in Model Predictive Control (MPC) algorithms. The parallel structure is an extension of the classical Wiener model; it is expected to offer better modeling accuracy and increase the MPC control quality. This work discusses the benefits of using the parallel Wiener model in MPC. It has three objectives. Firstly, it describes a fast MPC algorithm in which parallel Wiener models are used for online prediction. In the presented approach, sophisticated trajectory linearization is performed online, which leads to computationally fast quadratic optimization. The second objective of this work is to study the influence of the model structure on modeling accuracy. The well-known neutralization benchmark process is considered. It is shown that the parallel Wiener models in the open-loop mode generate significantly fewer errors than the classical structure. This work’s third objective is to validate the efficiency of parallel Wiener models in closed-loop MPC. For the neutralization process, it is demonstrated that parallel models demonstrate better control quality using various indicators, but the difference between the classical and parallel models is not significant.