Model Predictive Control Method Research Articles

A long-horizon move-blocking based direct power model predictive control for dynamic enhancement of DC microgrids

This research paper presents a novel long-horizon move-blocking based direct power model predictive control (DPMPC) strategy, uniquely designed to enhance the dynamic performance of boost converters in the grid-connected mode within DC microgrids. A precise dynamic model is developed considering a boost converter connected to a DC-link supplying constant power and resistive loads. To predict the system's dynamic behavior over an extended interval and enhance its performance in the presence of constant power loads, the long-horizon based finite control set model predictive control (FCS-MPC) method is introduced. To address the computational complexity associated with the conventional long-horizon DPMPC approach, a move-blocking (MB) strategy is incorporated into FCS-MPC, reducing the computational burden while improving the dynamic performance of the boost converter. Unlike recent studies that utilize DPMPC with short prediction interval, this paper presents evidence that a longer prediction interval is crucial for maintaining system stable and enhancing the dynamic response and also utilizing MB to make the control implementable in the real-time. The simulation results conducted using MATLAB/Simulink demonstrate the effective performance of the proposed strategy across various operating conditions of the boost converter. Experimental results further validate the strategy's capability to enhance the dynamic performance of the boost converter. Importantly, the experimental findings confirm the feasibility of implementing the proposed long-horizon move-blocking DPMPC strategy in real-time applications.

Research on Modeling Method of Autonomous Underwater Vehicle Based on a Physics-Informed Neural Network

Research on Modeling Method of Autonomous Underwater Vehicle Based on a Physics-Informed Neural Network

Coordinated management of oxygen excess ratio and cathode pressure for PEMFC based on synthesis variable-gain robust predictive control

In this research endeavor, a novel synthesis variable-gain robust model predictive control (SVGRPC) method using the min-max technique with parameter dependent Lyapunov functions is introduced to achieve the desired trajectory tracking of oxygen excess ratio (OER) and cathode pressure in the polymer electrolyte membrane fuel cells (PEMFCs) system. Initially, a simplified control-oriented model of the air supply system is developed and then transformed into a polytopic form to accommodate the inherent uncertainties in the PEMFC. Subsequently, the polytopic model is integrated with the reference trajectory to construct an augmented state-space model for deriving an error state representation. In the framework of robust model predictive control (RPC) utilizing a parameter-dependent Lyapunov function, a series of variable feedback gains are employed to mitigate conservatism. Additionally, the online solution process imposes a significant computational burden, presenting challenges for the real-time implementation of RPC-based control strategy. Therefore, the offline computing is introduced to significantly reduce the computational burden, resulting in the development of the SVGRPC strategy proposed in this paper. Subsequently, the SVGRPC strategy computes explicit linear state-feedback control laws by solving linear matrix inequalities (LMIs) using an offline control algorithm. The effectiveness of the proposed controller is then validated through assessments conducted under three distinct operating conditions of the PEMFC system. The outcomes of this study affirm that the proposed controller improves the requirements of control performance when compared to the online RPC with a quadratic Lyapunov function while significantly alleviating the computational workload.

Model predictive control (MPC) applied to a simplified model, magnetic nanoparticle hyperthermia (MNPH) treatment process

Magnetic nanoparticle hyperthermia (MNPH) has emerged as a promising cancer treatment that complements conventional ionizing radiation and chemotherapy. MNPH involves injecting iron-oxide nanoparticles into the tumor and exposing it to an alternating magnetic field (AMF). Iron oxide nanoparticles produce heat when exposed to radiofrequency AMF due to hysteresis loss. Minimizing the non-specific heating in human tissues caused by exposure to AMF is crucial. A pulse-width-modulated AMF has been shown to minimize eddy-current heating in superficial tissues. This project developed a control strategy based on a simplified mathematical model in MATLAB SIMULINK® to minimize eddy current heating while maintaining a therapeutic temperature in the tumor. A minimum tumor temperature of 43 [°C] is required for at least 30 [min] for effective hyperthermia, while maintaining the surrounding healthy tissues below 39 [°C]. A model predictive control (MPC) algorithm was used to reach the target temperature within approximately 100 [s]. As a constrained MPC approach, a maximum AMF amplitude of 36 [kA/m] and increment of 5 [kA/m/s] were applied. MPC utilized the AMF amplitude as an input and incorporated the open-loop response of the eddy current heating in its dynamic matrix. A conventional proportional integral (PI) controller was implemented and compared with the MPC performance. The results showed that MPC had a faster response (30 [s]) with minimal overshoot (1.4 [%]) than PI controller (115 [s] and 5.7 [%]) response. In addition, the MPC method performed better than the structured PI controller in its ability to handle constraints and changes in process parameters.

A tube-based model predictive control method for intelligent vehicles path tracking

A tube-based model predictive control method for intelligent vehicles path tracking

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.

Current-sensorless model predictive control for DAB converter based on back-flow power optimization

To solve problems such as high back-flow power, low system efficiency and poor dynamic properties of dual-active-full-bridge DC-DC converters in energy routers under traditional single-phase-shift modulation, the present paper introduces a current-sensorless-based model predictive control method combining back-flow power optimization based on Extended-Phase-Shift (EPS) modulation strategy. The transmission power model of the DAB converter is analyzed, taking into account the extended phase shift and the optimal shift comparison combination is found by the Lagrange multiplier method with the return power as the objective function. Then, the cost function is constructed to track the output voltage to achieve model predictive control without a current sensor. Finally, the accuracy and efficiency of the proposed strategy are verified by simulation. The results demonstrate the efficacy of this approach in mitigating system back-flow power and enhancing dynamic performance.

Coordinated control of yaw and roll stability in heavy vehicles considering dynamic safety requirements

In the field of heavy vehicle stability research, traditional safety requirements are often based on static scenario settings. However, the complexity and variability of actual road environments require safety control strategies that can be adapted to different driving conditions and environmental changes in real-time. To address this challenge, the paper proposes a coordinated control strategy for yaw and roll stability that considers the dynamic safety requirements. First, a quantitative analysis method for vehicle stability is proposed based on the dissipated energy theory, taking into account the lateral-vertical dynamics coupling characteristics. Additionally, a dynamic safety requirements identification model is developed by integrating the vehicle's future driving risk prediction algorithm. To meet dynamic safety requirements, a dynamic weight model predictive control method based on randomized ensembled double Q-learning reinforcement learning is designed. This method adjusts the control weights of yaw and roll stability online to flexibly address various destabilization risks, aiming to achieve more precise and efficient stability control. Through simulation and experimental verification, the results demonstrate that the proposed coordinated control strategy can effectively enhance the stability and safety of heavy vehicles in complex and dynamic driving environments.

A Gaussian-Process-Based Model Predictive Control Approach for Trajectory Tracking and Obstacle Avoidance in Autonomous Underwater Vehicles

To achieve the efficient and precise control of autonomous underwater vehicles (AUVs) in dynamic ocean environments, this paper proposes an innovative Gaussian-Process-based Model Predictive Control (GP-MPC) method. This method combines the advantages of Gaussian process regression in modeling uncertainties in nonlinear systems, and MPC’s constraint optimization and real-time control abilities. To validate the effectiveness of the proposed GP-MPC method, its performance is first evaluated for trajectory tracking control tasks through numerical simulations based on a 6-degrees-of-freedom, fully actuated, AUV dynamics model. Subsequently, for 3D scenarios involving static and dynamic obstacles, an AUV horizontal plane decoupled motion model is constructed to verify the method’s obstacle avoidance capability. Extensive simulation studies demonstrate that the proposed GP-MPC method can effectively manage the nonlinear motion constraints faced by AUVs, significantly enhancing their intelligent obstacle avoidance performance in complex dynamic environments. By effectively handling model uncertainties and satisfying motion constraints, the GP-MPC method provides an innovative and efficient solution for the design of AUV control systems, substantially improving the control performance of AUVs.

Dynamic Modeling and Improved Nonlinear Model Predictive Control of a Free-Floating Dual-Arm Space Robot

With the increasing demand for space missions, space robots have become the focus of research and attention. As a typical representative, the free-floating dual-arm space robot has the characteristics of multiple degrees of freedom, a floating base, and dynamic coupling between the manipulator and the base, so its modeling and control are very challenging. To address these challenges, a novel dynamic modeling and control method is proposed for a free-floating dual-arm space robot. First, an explicit dynamic model of a free-floating dual-arm space robot is established based on the explicit canonical multi-rigid-body dynamic modeling theory and combined with the concept of a dynamic equivalent manipulator. The establishment process of this model is not only simple and canonical to avoid the definition and calculation of many intermediate variables, but the symbolic result expression of the model also has the characteristics of iteration, which is convenient for computer automatic modeling. Next, aiming at addressing the problem of trajectory tracking and the base attitude stability of a free-floating dual-arm space robot with parameter perturbation and external disturbance, an improved nonlinear model predictive control method introducing the idea of sliding mode variable structure is proposed. Theoretical analysis shows that the proposed controller has better robustness than the traditional nonlinear model predictive controller. Then, an in-orbit service task is designed to verify the effectiveness of the proposed dynamic modeling and control strategy of the free-floating dual-arm space robot. Finally, the dynamic modeling and control methods proposed are discussed and summarized. The proposed methods can not only realize the tracking of the desired trajectory of the arms of the free-floating space robot, but can also realize the stable control of the base of the free-floating space robot. This paper provides new insights into the difficult problems regarding the dynamics and control of free-floating dual-arm space robots.

Two‐layer dynamic economic nonlinear model predictive control for a Lithium‐ion battery charge process with random disturbances

AbstractThe charging process of lithium‐ion batteries is necessary for normal operation, and improper lithium‐ion battery charging strategy can cause side reactions, significant temperature rise, performance degradation, and safety concerns. This paper proposes a two‐layer dynamic economic nonlinear model predictive control economic model predictive control (EMPC) method for lithium‐ion battery charge management in which the non‐Gaussian random noise of voltage and current signal are taken into account. In the two‐layer EMPC, the upper layer finds the optimal reference trajectory, and the power in the future prediction horizon is chosen as an economic indicator to optimize the upper layer. The lower layer tracks the optimal trajectory, and model predictive control based on generalized correntropy is used. The economic cost of the system and the dynamic changes of the process are considered to greatly reduce the computational complexity and realize rapid battery charge management. Finally, the simulation results show that the tracking error of the two‐layer EMPC method based on generalized correntropy is stable around 0, while the tracking error of the two‐layer EMPC method based on MSE is stable around 0.003. The average control action times of the proposed two‐layer EMPC and single‐layer EMPC are 0.0052 and 0.0267 s, respectively. It is verified that the proposed method performs better for the lithium‐ion battery charging process with random disturbances.

Research on Intelligent Vehicle Trajectory Tracking Control Based on Improved Adaptive MPC.

Intelligent vehicle trajectory tracking exhibits problems such as low adaptability, low tracking accuracy, and poor robustness in complex driving environments with uncertain road conditions. Therefore, an improved method of adaptive model predictive control (AMPC) for trajectory tracking was designed in this study to increase the corresponding tracking accuracy and driving stability of intelligent vehicles under uncertain and complex working conditions. First, based on the unscented Kalman filter, longitudinal speed, yaw speed, and lateral acceleration were considered as the observed variables of the measurement equation to estimate the lateral force of the front and rear tires accurately in real time. Subsequently, an adaptive correction estimation strategy for tire cornering stiffness was designed, an AMPC method was established, and a dynamic prediction time-domain adaptive model was constructed for optimization according to vehicle speed and road adhesion conditions. The improved AMPC method for trajectory tracking was then realized. Finally, the control effectiveness and trajectory tracking accuracy of the proposed AMPC technique were verified via co-simulation using CarSim and MATLAB/Simulink. From the results, a low lateral position error and heading angle error in trajectory tracking were obtained under different vehicle driving conditions and road adhesion conditions, producing high trajectory-tracking control accuracy. Thus, this work provides an important reference for improving the adaptability, robustness, and optimization of intelligent vehicle tracking control systems.

Research on Intelligent Platoon Formation Control Based on Kalman Filtering and Model Predictive Control

Recently, the intelligent platoon has attracted a lot of attention in both academic and industrial research. For each intelligent platoon, all vehicles drive sequentially in a line, which helps to improve fuel economy and road capacity. Consider two adjacent vehicles in the intelligent platoon, and there is no mechanical boundary between them. However, an intelligent platoon may still suffer from the issues of poor vehicle-following performance during the process of vehicle-following, especially when it obtains its own position and other parameters inaccurately. To address this issue, this paper proposes a model predictive control method based on an improved version of Kalman filtering, aiming to enhance the anti-interference capacity of intelligent platoons in scenarios where the following vehicles have acquired inaccurate parameters resulting from environmental disturbances and sensor noise. Firstly, this paper establishes a three-degree-of-freedom single-track model for the following vehicle, conducting dynamic analysis of its lateral, longitudinal, and yaw movements. Then, this paper develops a horizontal and longitudinal formation driving control frame of the intelligent vehicle platoon. Moreover, this paper also has employed Kalman filtering for interference reduction of state parameters and designs an improved model predictive controller. The proposed scheme is verified and evaluated through a joint simulation within Carsim and MATLAB/Simulink, and the results demonstrate that the longitudinal following error is reduced by 37% and the lateral following error is reduced by 51% compared to traditional algorithms, effectively improving the stability of intelligent vehicle platoons during following driving.

Multi-index control strategy from cement calcination denitration system: a model predictive control method for combined control of nitrogen oxide and ammonia escape.

The cement industry is one of the main sources of NOx emissions, and automated denitration systems enable precise control of NOx emission concentration. With non-linearity, time delay and strong coupling data in cement production process, making it difficult to maintain stable control of the denitration system. However, excessive pursuit of denitration efficiency is often prone to large ammonia escape, causing environmental pollution. A multi-objective prediction model combining time series and a bi-directional long short-term memory network (MT-BiLSTM) is proposed to solve the data problem of the denitration system and achieve simultaneous prediction of NOx emission concentration and ammonia escape value. Based on this model, a model predictive control framework is proposed and a control strategy of denitration system with multi-index model predictive control (MI-MPC) is built based on neural networks. In addition, the differential evolution (DE) algorithm is used for rolling optimization to find the optimal solution and to obtain the best control variable parameters. The control method proposed has significant advantages over the traditional PID (proportional integral derivative) controller, with a 3.84% reduction in overshoot and a 3.04% reduction in regulation time. Experiments prove that the predictive control framework proposed in this paper has better stability and higher accuracy, with practical research significance.

Control strategy for power factor correction circuits based on model predictive control

Power factor correction circuits are being used more extensively, and with that, the requirements and standards have become increasingly stringent. To improve the power factor of the circuit, thereby improving its operational efficiency and mitigating the load effects in boost power factor correction circuits, reducing Total Harmonics Distortion is crucial for improving the quality of the current. An improvement upon the conventional dual-loop PID control algorithm has been proposed. It involves integrating Model Predictive Control into the current loop design and incorporates a Luenberger observer. Model Predictive Control methods can be applied for the control of multivariable systems, especially in complex multivariable systems, as they exhibit strong robustness. Finally, by performing both simulation and experimental validations and analyzing the data results, it is shown that under full load conditions, the power factor λ is 0.968, the efficiency η is 91.11%, the voltage total harmonic distortion Uthd is 1.88%, and the current total harmonic distortion Ithd is 7.04%. These data results strongly confirm the performance of the control mechanism proposed in this paper.

GA-LSTM speed prediction-based DDQN energy management for extended-range vehicles

In this paper, a dual deep Q-network (DDQN) energy management model based on long-short memory neural network (LSTM) speed prediction is proposed under the model predictive control (MPC) framework. The initial learning rate and neuron dropout probability of the LSTM speed prediction model are optimized by the genetic algorithm (GA). The prediction results show that the root-mean-square error of the GA-LSTM speed prediction method is smaller than the SVR method in different speed prediction horizons. The predicted demand power, the state of charge (SOC), and the demand power at the current moment are used as the state input of the agent, and the real-time control of the control strategy is realized by the MPC method. The simulation results show that the proposed control strategy reduces the equivalent fuel consumption by 0.0354 kg compared with DDQN, 0.8439 kg compared with ECMS, and 0.742 kg compared with the power-following control strategy. The difference between the proposed control strategy and the dynamic planning control strategy is only 0.0048 kg, 0.193%, while the SOC of the power battery remains stable. Finally, the hardware-in-the-loop simulation verifies that the proposed control strategy has good real-time performance.

Improved Adaptive CCS-MPCC for Distorted Model Parameters Mitigation of IPMSM Drives

In this study, a robust adaptive model continuous-control-set model predictive current control (CCS-MPCC) method is proposed for interior permanent magnet synchronous motors (IPMSMs) considering the existing disturbances, unmodeled dynamics, and parameter variations. The novel aspect of this research is that it merges the improved dynamic, useful properties of model predictive control (MPC), which includes easy implementation and fast dynamic response, together with the most effective criterion of an adaptive controller (i.e., robustness to model uncertainties), that result in enhanced dynamic and steady-state control performance despite unknown and changing disturbances. Unlike the conventional CCS-MPCC approach that heavily depends on the accurate knowledge of the system model to attain improved control performance, the proposed method achieves satisfactory control performance (e.g., fast dynamic response, smaller steady-state error, and low stator current THD) by stabilizing the state errors to approach zero and compensating for variations in the model parameters using the designed feedback control terms and adaptive control terms, respectively. The stability of the proposed control method is ensured by the state errors asymptotically decaying to zero. The proposed approach was simulated and then implemented on a PSIM simulation tool and a prototype IPMSM test bench using TI TMS320F28335 DSP, respectively, to confirm its feasibility.

Motion Planning and Control of Autonomous Aggressive Vehicle Maneuvers

Aggressive vehicle maneuvers such as those performed by professional racing drivers achieve high agility motion at the edge of handling limits. These aggressive maneuvers can be used to design human-inspired active safety features for next-generation “accident-free” vehicles. We present a motion planning and control design for autonomous aggressive vehicle maneuvers. The motion planner takes advantages of the sparse stable trees and the enhanced rapidly exploring random tree (RRT*) algorithms. The use of the sparsity property helps to reduce the computational cost of the RRT* method by removing non-useful nodes in each iteration and therefore to rapidly converge to the optimal solution. The proposed motion control design allows the vehicle to operate outside the stability region to accomplish a safe, agile maneuver. A safety region is computed to augment the stability region and the motion control is built on a modified nonlinear model predictive control method. We implement the proposed planner and controller and demonstrate the autonomous aggressive maneuvers on a 1/7-scale racing vehicle platform. Comparison with human expert driver and other existing methods is also presented to demonstrate the performance and robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Motion planning and control of human driver-inspired aggressive vehicle maneuvers is a challenging task because of high-agility, unstable fast vehicle motions. This paper is motivated by addressing this challenge in autonomous driving technologies. Instead of restricting vehicle motions within a stability region that is taken by existing methods, we augment the conservative stability region to a safety region with guaranteed performance. To improve the computational efficiency of sampling-based motion planners, we take advantage of sparsity and also integration of a nonlinear predictive control method to compute feasible vehicle motion in searching space. The stability of the vehicle motion controller and sub-optimality of the motion planner are analyzed and guaranteed. Using a scaled vehicle testbed, we validate and compare the proposed motion planning and control design with other existing methods and human expert driver. The experimental results demonstrate the superior performance than the other methods and comparable with human expert driving skills.

Cascade NMPC-PID control strategy of active heave compensation system for ship-mounted offshore crane

In this paper, the problem associated with active heave motion compensation during heavy-lift operations performed by ship-mounted offshore cranes beneath rough marine environment is investigated. To effectively decouple the correlated motion of the suspended payload from the support vessel via the secondary regulated active heave compensation (SRAHC) system, a novel hierarchical control strategy is proposed by integrating a cascade control structure with an adaptive robust control scheme that incorporates a vertical motion forecast algorithm based on autoregressive integrated moving average (ARIMA) model, a conventional proportional-integral-derivative (PID) framework, and a nonlinear model predictive control (NMPC) method. The introduction of the cascade structure is motivated by its prompt error response against control lag and efficient system order reduction for computational burden alleviation, which forms a crucial foundation for the NMPC-based real-time regulation of PID gains, ensuring optimal evolution of the SRAHC system while improving its practical feasibility. Furthermore, the enhancements in heave compensation and trajectory tracking performance, noise resistance, constraint satisfaction, and engineering application potential of the proposal are demonstrated through a thorough comparative analysis conducted in a practical co-simulation research system.

Petri Net Model Predictive Control Method for Batch Chemical Systems

In order to address the problem of the real-time scheduling and control of batch chemical systems, this work proposes a model predictive control method based on Petri nets. First, a method is presented to construct a batch chemical system’s timed Petri net model. Second, a control structure is designed to augment the Petri net model to control the valves. This results in timed Petri nets that formally represent the process specifications of a batch chemical system. Third, a model predictive control method is developed to schedule and control timed Petri nets, where a proposed heuristic function is utilized to perform the optimization computation. The model parameters are dynamically adjusted using online data, and both scheduling and valve control instructions are calculated in real time. Finally, a series of experiments is carried out in a beer canning plant to verify the proposed method. According to the experimental results, the scheduling and control problem can be solved in real time, where the online computations can be performed in milliseconds, and the resulting scheduling strategies are optimal or near-optimal.