Predictive Control Research Articles

Transfer Learning for Thickener Control

Thickener control is a key area of focus in the minerals processing industry, particularly due to its crucial role in water recovery, which is essential for sustainable resource management. The highly nonlinear nature of thickener dynamics presents significant challenges in modeling and optimization, making it a strong candidate for advanced surrogate modeling techniques. However, traditional data-driven approaches often require extensive datasets, which are frequently unavailable, especially in new plants or unexplored operational domains. Developing data-driven models without enough data representative of the dynamics of the system could result in incorrect predictions and consequently, unstable response of the controller. This paper proposes the application of a methodology that leverages transfer learning to address these data limitations to enhance surrogate modeling and model predictive control (MPC) of thickeners. The performance of three approaches—a base model, a transfer learning model, and a physics-informed neural network (PINN)—are compared to demonstrate the effectiveness of transfer learning in improving control strategies under limited data conditions.

A Symmetry-Inspired Hierarchical Control Strategy for Preventing Rollover in Articulated Rollers

In off-road environments, the lateral rollover stability of articulated unmanned rollers (URs) is critical to ensure operational safety and efficiency. This paper introduces the concept of a rollover energy barrier (REB), a symmetry-based metric that quantifies the energy margin between the current state and the critical rollover threshold of articulated rollers. URs exhibit dynamic asymmetry due to their hydraulic steering systems, which differ significantly from traditional passenger vehicles. To address these challenges, we propose a hierarchical control framework inspired by the principles of dynamic symmetry. This framework integrates Nonlinear Model Predictive Control (NMPC) and Active Disturbance Rejection Control (ADRC): NMPC is used for trajectory planning by incorporating the REB into the cost function, ensuring rollover stability, while ADRC compensates for dynamic asymmetries, model uncertainties, and external disturbances during trajectory tracking. Simulation and experimental results validate the effectiveness of the proposed control strategy in enhancing the rollover stability and tracking performance of the URs under off-road conditions.

Networked Predictive Trajectory Tracking Control for Underactuated USV with Time-Varying Delays

This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding mode control technique and predictive control scheme. By leveraging a first-order forward Euler discretization approach, a discrete-time model of USVs was initially formulated. Then, a virtual velocity controller was developed to convert the position tracking into expected velocity tracking, which was achieved by utilizing a sliding mode control. Subsequently, a networked predictive control technique was performed to compensate for the time-varying delays. Finally, theoretical analysis and extensive comparative simulation tests demonstrated that the proposed control scheme guaranteed complete compensation for time-varying delays while ensuring the stability of the closed-loop system.

Integrated learning‐based estimation and nonlinear predictive control of an ammonia synthesis reactor

AbstractThis paper presents an advanced machine learning‐based framework designed for predictive modeling, state estimation, and feedback control of ammonia synthesis reactor dynamics. A high‐fidelity two‐dimensional multiphysics model is employed to generate a comprehensive dataset that captures variable dynamics under various operational conditions. Surrogate long short‐term memory neural networks are trained to enable real‐time predictions and model‐based control. Additionally, a feedforward neural network is developed to estimate the outlet ammonia concentration and hotspot temperature using spatially distributed temperature readings, thereby addressing the challenges associated with real‐time concentration and maximum temperature measurements. The machine learning‐based predictive modeling and state estimation methods are integrated into a model predictive control architecture to regulate ammonia synthesis. Simulation results demonstrate that the machine learning surrogates accurately represent the nonlinear process dynamics with minimal discrepancy while reducing optimization costs compared to the high‐fidelity model, ensuring adaptability and effective guidance of the reactor to desired set points.

Squat Motion of a Humanoid Robot Using Three-Particle Model Predictive Control and Whole-Body Control.

Squatting is a fundamental and crucial movement, often employed as a basic test during robot commissioning, and it plays a significant role in some service industries and in cases when robots perform high-dynamic movements like jumping. Therefore, achieving continuous and precise squatting actions is of great importance for the future development of humanoid robots. In this paper, we apply three-particle model predictive control (TP-MPC) combined with weight-based whole-body control (WBC) to a humanoid robot. In this approach, the arms, legs, and torso are simplified into three particles. TP-MPC is utilized to optimize the rough planning's reference trajectory, while WBC is employed to follow the optimized trajectory. The algorithm is tested through simulations of a humanoid robot performing continuous squatting motions. It demonstrates the ability to achieve more accurate trajectory tracking compared to using WBC alone and also optimizes the issue of excessive knee torque spikes that occur with WBC alone during squatting. Moreover, the algorithm is less computationally intensive, and it is capable of operating at a frequency of 100 Hz.

Experimental Validation of a Predictive Control Co-Design Algorithm

Abstract Control co-design (CCD) represents a promising solution for coordinating the physical design and control of dynamic engineering systems as technological demands become more stringent. Predictive control co-design (pCCD), recently introduced to the CCD literature, optimizes combinations of feedforward and feedback static gain sets at the system design stage to combine the robustness and preview control afforded by using state-of-the-art control methods, like MPC, in CCD with the computational efficiency of open-loop CCD methods that solve CCD problems with a single optimization level. This work contributes an experimental validation of pCCD to the literature. First, pCCD is performed offline on a spring-mass-damper system. The co-designed system's optimal response is then experimentally validated online. Results are compared to an analogous system co-designed with an open-loop CCD method. The experimental system co-designed using pCCD yielded a sum squared error with respect to a desired reference signal 40 times smaller than the system co-designed using open-loop CCD. The results indicate pCCD yields co-designed systems with superior online robustness in comparison to open-loop CCD methods. Moreover, systems co-designed using pCCD are more robust to both modeling error and unexpected disturbances inputs or changes in desired reference signals encountered online.

Resilient Dual-mode Model Predictive Control for Constrained Linear Networked Control Systems With Random DoS Attacks

Resilient Dual-mode Model Predictive Control for Constrained Linear Networked Control Systems With Random DoS Attacks

Utilizing Linear Quadratic Regulator and Model Predictive Control for Optimizing the Suspension of a Quarter Car Vehicle in Response to Road Excitation

Utilizing Linear Quadratic Regulator and Model Predictive Control for Optimizing the Suspension of a Quarter Car Vehicle in Response to Road Excitation

Collision Avoidance for Maritime Autonomous Surface Ships Based on Model Predictive Control Using Intention Data and Quaternion Ship Domain

With the increasing proportion of ships in logistics and the growing prosperity of traffic in maritime, negotiation and cooperative collision avoidance between ships is becoming more and more essential for navigational safety. This paper proposes a Model Predictive Control method that utilizes intention data of the target ship and a quaternion ship domain model to achieve collision avoidance while considering COLREGs, named IQMPC. Firstly, by utilizing the intention data of other ships, trajectories of the own ship and the target ship are well predicted to detect potential collision risks and take optimal avoidance actions in advance while risks exist. Secondly, the quaternion ship domain with its adjacent area is divided into four different parts to reflect the urgency of ship encounters. Collision risk evaluation functions are designed to determine avoidance actions conforming to COLREGs. Thirdly, several different ship encounter scenarios were simulated based on IQMPC to verify its capability.

Speed-sensorless model predictive thrust control of linear induction motor based on improved extended Kalman filter observer

This paper puts forth a strategy for sensorless finite-set model predictive thrust control (FS-MPTC) of linear induction motors (LIM) based on an improved extended Kalman filter (IEKF) observer. It is designed to address the shortcomings of existing LIM sensorless vector control methodologies, namely their lack of robustness and ineffective decoupling due to dynamic end effects. First, a fifth-order nonlinear discrete model of LIM is established in order to meet the requirements of sensorless FS-MPTC. An extended Kalman filter (EKF) observer based on the state-space equations is derived with the objective of achieving synchronous online observation of current, flux linkage and speed. In order to address the limitations of the traditional EKF, which employs a fixed system noise covariance matrix, this paper introduces an adaptive adjustment mechanism based on the difference between the theoretical predictions and actual measurements of the observer. This mechanism allows for the covariance matrix Q to be adjusted in real-time. The enhanced IEKF observer is then put forth. Simulation and experimental outcomes illustrate that, in comparison to the conventional EKF, the IEKF is capable of effectively adapting to internal noise fluctuations resulting from disparate operational conditions, system disturbances and LIM dynamic end effects. It is able to accurately estimate speed and flux linkage, operating stably under both high and low-speed conditions with commendable dynamic and robust performance.

Data-Driven Event-Triggered Predictive Post-Impact Control of Space Robot with Uncertainties

Satellite-mounted robotic systems play an important role in various on-orbit services. The present work focuses on controlling a multi-arm space robot in the post-impact phase under the presence of uncertainties, which may arise due to dynamical parameter variation, joint friction, and disturbances. This work proposes a Gaussian Process-based Model Predictive Control (GP-MPC) to achieve the desired motion by mitigating the effects of uncertainties. Whereas the GP helps in predicting uncertainties from collected data, the MPC provides control inputs subject to the cost function and constraints. Further, an event-triggered control strategy is employed to reduce the computational burden associated with GP-MPC. It uses a Chernoff-bound-based triggering threshold to decide the occurrence of events. The generated control inputs may disturb the base’s attitude, and reactionless manipulation is also presented as a special case to mitigate it. The analytical outcomes are verified through numerical simulations on a dual-arm planer space robot.

Adaptive model predictive controller for power regulation and load reduction in large wind turbines

Pitch control is related to the efficient energy conversion and safe operation of wind turbine, which is a challenging control issue because of the highly nonlinear dynamics of wind turbine, system constraints and stochastic environmental disturbances, etc. In this article, we propose the adaptive model predictive pitch control strategy based on linear parameter varying (LPV) model to solve the pitch control issue of large-scale wind turbine. Firstly, a high-fidelity LPV model is constructed to approximate the high-nonlinearity dynamics of wind turbine. The gap metric theory is introduced to optimize the selection of local models. Based on the proposed LPV model, an adaptive model predictive controller with linear time-varying Kalman filter is designed for large-scale wind turbine. The benchmark 5 megawatt (MW) wind turbine model provided by FAST is employed to evaluate the controller performance. The results show that, compared with the traditional gain scheduling proportional–integral control strategy, the proposed control strategy can achieve better power regulation and load reduction performance under gust, step and turbulent wind.

Composite model predictive current control for PMSM servo system

This research delves into the development of a composite model predictive controller designed for the current loop in a permanent magnet synchronous motor (PMSM) servo system. In order to enhance the performance of a system, the proposed controller integrates an extended state observer (ESO) and actuating delay compensation. The ESO is employed for the estimation of the lumped disturbance affecting the PMSM, while the prediction model incorporates both the estimated lumped disturbance and the delayed actuating output to derive an optimized control law. Simulation and experimental results further validate the effectiveness of the approach, offering an innovative solution to the actuating delay challenge and a considerable increase in the bandwidth of the current loop.

A two-layer optimal scheduling method for microgrids based on adaptive stochastic model predictive control

Abstract Renewable energy is highly susceptible to weather and environmental factors, and changes dramatically in a short period, which makes it difficult for traditional fixed-period scheduling of microgrids to capture the time-series variations of source and load, exacerbating the power imbalances of systems. To address this problem, a novel two-layer rolling optimization framework for microgrids based on adaptive stochastic model predictive control is proposed in this paper. Firstly, a two-layer microgrid optimization model based on stochastic model predictive control is established, in which measurement technology plays an indispensable role in the quality of uncertain scenarios and optimal decision results of the microgrid, as well as providing impetus for the development of the microgrid. In the upper-layer prescheduling stage, the optimal control sequence under multiple uncertainties is obtained by solving a scenario-based chance constraint programming model. In the lower-layer power compensation stage, the measured scenario errors are regarded as fluctuations, and the energy storage devices are preferentially used for smoothing processing. Secondly, an adaptive period division method using Wasserstein distance-based hierarchical clustering is developed to guide intraday online scheduling. The concepts of distribution similarity and distribution loss are presented to adaptively divide the periods under uncertain conditions, so as to overcome the disturbance of uncertainty and improve the flexibility of microgrid scheduling. Finally, the simulation results show that the proposed method can flexibly deal with the uncertainty at different time scales, and achieve 53.63 kWh less compensation power and 19.12% lower operating cost than traditional fixed-period scheduling methods, and thus effectively improve the economy and reliability of microgrid operation.

A photovoltaic-storage system configuration and operation optimization model based on Model Predictive Control

A photovoltaic-storage system configuration and operation optimization model based on Model Predictive Control

Physics-Based Modelling of a Milk Cooling System for Intelligent Energy Management

Forecast-based energy management can play a largerole in a smarter and more efficient use of renewableenergies based on demand side management. Usingapproaches such as model predictive control, individualconsumption devices can be shifted within operationconstraints so that their electricity consumptionoptimally matches generation. In agriculture, largethermal storages make up a sizeable part of electricityconsumption, and offer a potential use in the short termshifting of demand. Necessary for this are accuratemodels to forecast behaviour of such dynamic systems,so that minimal power demand and fulfilment of operationconstraints can be ensured when computing optimalcontrols. This work focuses on the physics-basedmodelling of a milk cooling storage through parameteridentification on real measurement data. Emphasizedare the derivation of a suitable model ODE with regardsto available data, and evaluation of the model ona rolling horizon. All major features of the measurementdata can be recreated by the model forecasts, andmodel performance values show errors of around 30%relative to mean temperature. Model performance isconsidered suitable for use in energy management atleast on short forecast horizons, while practicabilityon longer horizons is subject to further research.

Model Predictive Control of Aerodynamic Levitation Systems

Airjet-driven levitation systems are an important emerging technology, as they can ensure the contactless movement of objects. From a control engineering perspective, the position set-point tracking of the levitating object poses many challenges because the aerodynamic characteristics of the air jet used for levitation are difficult to model. The unmodelled disturbances significantly affect the control performances. Moreover, the bound constraints imposed by the mechanical design and the applied actuators should also be considered during controller design. In this work, we present a motion control approach suitable for vertical levitation systems actuated by a fan-induced air jet. We developed the necessary hardware and software elements for the control equipment of the levitation system. We apply the MPC (Model Predictive Control) approach to ensure high-precision position regulation under various aerodynamic conditions with bounded control signals. We validate the applicability of the control system through simulation and real-time experimental measurements.

An Adaptive Weight Model Predictive Control Strategy for Vienna Rectifiers Under Unbalanced Loads

ABSTRACTThis study tackles the challenge of managing midpoint potential imbalance and suppressing current zero‐crossing distortion in Vienna rectifiers under conditions of unbalanced load. To address this, an adaptive weight model predictive control strategy is proposed. The research begins by analyzing the interaction between maintaining midpoint potential balance and mitigating current zero‐crossing distortion, taking into account influences, such as current ripple, sampling inaccuracies, and load imbalance. A prediction model is formulated to predict the behavior of the voltage vector coupling error. Using this predictive framework, a multiobjective model predictive control strategy is developed, incorporating a weight adaptive optimization technique to enhance the coordination between objectives. Experimental results validate the effectiveness of the proposed approach in controlling neutral‐point potential imbalance, reducing current harmonics, and suppressing zero‐crossing distortion, demonstrating its advantages over existing methods.

Flexible Model Predictive Control for Bounded Gait Generation in Humanoid Robots.

With advancements in bipedal locomotion for humanoid robots, a critical challenge lies in generating gaits that are bounded to ensure stable operation in complex environments. Traditional Model Predictive Control (MPC) methods based on Linear Inverted Pendulum (LIP) or Cart-Table (C-T) methods are straightforward and linear but inadequate for robots with flexible joints and linkages. To overcome this limitation, we propose a Flexible MPC (FMPC) framework that incorporates joint dynamics modeling and emphasizes bounded gait control to enable humanoid robots to achieve stable motion in various conditions. The FMPC is based on an enhanced flexible C-T model as the motion model, featuring an elastic layer and an auxiliary second center of mass (CoM) to simulate joint systems. The flexible C-T model's inversion derivation allows it to be effectively transformed into the predictive equation for the FMPC, therefore enriching its flexible dynamic behavior representation. We further use the Zero Moment Point (ZMP) velocity as a control variable and integrate multiple constraints that emphasize CoM constraint, embed explicit bounded constraint, and integrate ZMP constraint, therefore enabling the control of model flexibility and enhancement of stability. Since all the above constraints are shown to be linear in the control variables, a quadratic programming (QP) problem is established that guarantees that the CoM trajectory is bounded. Lastly, simulations validate the effectiveness of the proposed method, emphasizing its capacity to generate bounded CoM/ZMP trajectories across diverse conditions, underscoring its potential to enhance gait control. In addition, the validation of the simulation of real robot motion on the robots CASBOT and Openloong, in turn, demonstrates the effectiveness and robustness of our approach.

Design and Simulation of an Automatic Alignment System for Ship Multi-Support Shafting

The current shafting alignment process primarily relies on manual adjustments, resulting in low efficiency and limited precision. This study designs an automatic alignment system for multi-support shafting to improve alignment efficiency and accuracy, advancing the development of intelligent systems. The overall design of the automatic alignment system is proposed, including the establishment of shafting and actuator models. The alignment performance of two control strategies under different shafting deviations is validated through simulations. The results indicate that both the full-state feedback control strategy and the model predictive control strategy can regulate bearing load errors to within 1%. Compared to manual alignment, both strategies demonstrate significantly higher efficiency. The proposed automatic alignment system is feasible and lays a foundation for the development of high-precision shafting alignment systems.