Model-based Control Research Articles

From Data to Draught: Modelling and Predicting Mixed-Culture Beer Fermentation Dynamics Using Autoregressive Recurrent Neural Networks

The ascendency of the craft beer movement within the brewing industry may be attributed to its commitment to unique flavours and innovative styles. Mixed-culture fermentation, celebrated for its novel organoleptic profiles, presents a modelling challenge due to its complex microbial dynamics. This study addresses the inherent complexity of modelling mixed-culture beer fermentation while acknowledging the condition monitoring limitations of craft breweries, namely sporadic offline sampling rates and limited available measurement parameters. A data-driven solution is proposed, utilising an Autoregressive Recurrent Neural Network (AR-RNN) to facilitate the production of novel, replicable, mixed-culture fermented beers. This research identifies time from pitch, specific gravity, pH, and fluid temperature as pivotal model parameters that are cost-effective for craft breweries to monitor offline. Notably, the autoregressive RNN fermentation model is generated using high-frequency multivariate data, a departure from intermittent offline measurements. Employing the trained autoregressive RNN framework, we demonstrate its robust forecasting prowess using limited offline input data, emphasising its ability to capture intricate fermentation dynamics. This data-driven approach offers significant advantages, showcasing the model’s accuracy across various fermentation configurations. Moreover, tailoring the design to the craft beer market’s unique demands significantly enhances the model’s practicable predictive capabilities. It empowers nuanced decision-making in real-world mixed-culture beer production. Furthermore, this model lays the groundwork for future studies, highlighting transformative possibilities for cost-effective model-based control systems in the craft beer sector.

A Rigid-Flexible Coupling Dynamic Model for Robotic Manta with Flexible Pectoral Fins

The manta ray, exemplifying an agile swimming mode identified as the median and paired fin (MPF) mode, inspired the development of underwater robots. Robotic manta typically comprises a central rigid body and flexible pectoral fins. Flexible fins provide excellent maneuverability. However, due to the complexity of material mechanics and hydrodynamics, its dynamics are rarely studied, which is crucial for the advanced control of robotic manta (such as trajectory tracking, obstacle avoidance, etc.). In this paper, we develop a multibody dynamic model for our novel manta robot by introducing a pseudo-rigid body (PRB) model to consider passive deformation in the spanwise direction of the pectoral fins while avoiding intricate modeling. In addressing the rigid-flexible coupling dynamics between flexible fins and the actuation mechanism, we employ a sequential coupling technique commonly used in fluid-structure interaction (FSI) problems. Numerical examples are provided to validate the MPF mode and demonstrate the effectiveness of the dynamic model. We show that our model performs well in the rigid-flexible coupling analysis of the manta robot. In addition to the straight-swimming scenario, we elucidate the viability of tailoring turning gaits through systematic variations in input parameters. Moreover, compared with finite element and CFD methods, the PRB method has high computational efficiency in rigid-flexible coupling problems. Its potential for real-time computation opens up possibilities for future model-based control.

The construction and use of cognitive maps in model-based control.

When making decisions, we sometimes rely on habit and at other times plan toward goals. Planning requires the construction and use of an internal representation of the environment, a cognitive map. How are these maps constructed, and how do they guide goal-directed decisions? We coupled a sequential decision-making task with a behavioral representational similarity analysis approach to examine how relationships between choice options change when people build a cognitive map of the task structure. We found that participants who encoded stronger higher-order relationships among choice options showed increased planning and better performance. These higher-order relationships were more strongly encoded among objects encountered in high-reward contexts, indicating a role for motivation during cognitive map construction. In contrast, lower-order relationships such as simple visual co-occurrence of objects did not predict goal-directed planning. These results show that the construction of cognitive maps is an active process, with motivation dictating the degree to which higher-order relationships are encoded and used for planning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Research on Characteristic Model-based Adaptive Control of High-speed Permanent Magnet Synchronous Motor With Time Delay

Research on Characteristic Model-based Adaptive Control of High-speed Permanent Magnet Synchronous Motor With Time Delay

A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

Model-based deduction learning control: A novel method for optimizing gas turbine engine afterburner transient

The afterburning phase of an aero gas turbine engine is essential for boosting engine thrust. Traditional methods that combine open-loop afterburner fuel flow with closed-loop nozzle throat area control always degrade control quality during the transients of afterburner activation and deactivation. This results in fluctuations in the turbine outlet total pressure, consequently decreasing the fan surge margin, and may even lead to afterburner ignition failure or fan surge. A model-based deduction learning control method is proposed to address these issues. This method comprises: 1) a model-based offline experience deduction and learning module to enhance the coordination of afterburner fuel flow and nozzle throat area control during the early stages of afterburner activation or deactivation; 2) a power lever angle reference trajectory module designed to enhance the linearity of thrust output; 3) a nonlinear integrated online output module to maintain control stability. Simulation results have shown that the method effectively reduces the fluctuations in turbine outlet total pressure, bolsters the fan surge margin, and improves the linearity of thrust during the afterburning phase.

Model-Based Control of the Scanning Tunneling Microscope: Enabling New Modes of Imaging, Spectroscopy, and Lithography

Model-Based Control of the Scanning Tunneling Microscope: Enabling New Modes of Imaging, Spectroscopy, and Lithography

Robust Adaptive Control of a Bimanual 3T1R Parallel Robot With Gray-Box-Model and Prescribed Performance Function

An elaborated bimanual 4-degrees-of-freedom (DOF) parallel robot is designed with large translational workspace, light moving mass and direct-drive actuation. The transient response greatly affects the throughput when using this robot for high-speed pick-and-place applications. However, this cannot be properly taken care by existing black-box model-based control schemes. Meanwhile, it is tedious to derive its dynamic model analytically and such a model is inapplicable for the real-time control. In this work, a gray-box-model-based control structure is proposed, with the retained inertial dynamics are directly derived by the principle of virtual work and the other parts are estimated by adaptive neural networks. This ensures calculation efficiency and integrity of the dynamics. Moreover, a prescribed performance function is constructed to ensure the specified tracking requirements both in transient and steady states. On the basis, the robust integral of signum of error term is incorporated to compensate the structural and unstructural uncertainties, which further improves the robustness during high-frequency motion. Comparative real-time experiments have been performed on the actual robot with attainment of the predefined performance and higher tracking accuracy.

Research on vehicle speed prediction model based on traffic flow information fusion

Resource scarcity, global climate change and environmental pollution are increasingly constraining the development of the automotive industry. China proposes to reach the carbon peak by 2030; to reach the carbon neutral double carbon target by 2060 and gradually promote a green and low-carbon transition in energy development. The development of new energy vehicles is an important approach for China to realize its energy structure transformation in the automobile industry. HEV, as a transitional product of automobile energy transformation, has the advantages of both internal combustion engine vehicles and electric vehicles, which can improve the fuel efficiency and the emission problem of internal combustion engine vehicles and the range is longer compared to electric vehicles. One of the important aspects of HEV research is the design of whole vehicle energy management strategy based on the model predictions. Particularly, model-based predictive control is one of the mainstream energy management strategies nowadays, and its optimization effect is mainly subject to the model prediction accuracy. In this study, we constructed the ITS environment of a local roadway through simulation, compared the speed prediction effects of different speed prediction methods in different prediction time domains, and fused the historical information of vehicles (speed of the vehicle in front, distance, signal status, distance, and remaining time). It is found that N-BEATS is more effective in predicting vehicle speed in different prediction time domains, and the prediction accuracy of the speed prediction model is effectively improved after its fusion of multivariate information.

A 4-Phase Combined Adhesion Threshold Algorithm for Wheel Slide Protection Systems in Rail Vehicles

The wheel slide protection control system for rail vehicles plays a crucial role in ensuring a consistent braking performance in all operating environments, making it a vital factor in the safety and efficiency of rail transportation. In this paper, a hybrid approach to wheel slide protection control is presented, which combines the rule-based control strategy and the model-based control methods using adhesion force estimation. Model-based control usually relies on mathematical models to characterize the vehicle dynamics, requiring online estimators to be designed or extra sensors to be added for practical application. Rule-based control operates on predefined rules and thresholds and the available data from vehicles in service. A comparative test was conducted between the traditional rule-based control strategy and the proposed combined control strategy using a semi-physical simulation test bench. The performance differences of the control strategies were analyzed from two perspectives: adhesion utilization and air consumption. It was observed that among the traditional 2-phase, 3-phase, 4-phase and the optimized 4-phase combined control method, the combined control strategy has the best adhesion utilization and the traditional 4-phase control strategy has the least air consumption.

Simulation and Control of Solar Thermal Power Plants

In this paper, first the overall modeling approach for an optimized control of a hot-gas cycle for solar thermal power plants in the Modelica language is pointed out. The emphasis of the modeling work lies on the development of dynamic component models to be used in control systems. Depending on the control task, the discretization of the models has to be adapted. Main components of the hot-gas cycle are the solar thermal receiver and the storage system. The steam cycle is preliminarily only included as heat sink. Second, for control purposes a linear model-based controller (MPC) was implemented in Modelica based on an external state-of-the-art QP solver [2] linked to the Modelica model. The performance of the MPC is compared with a basic automation scheme based on classical PID controllers.

Modelling and control of electromechanical disc brake for heavy-duty vehicles

This paper proposes a validated procedure that can be used for development stage of model-based controller for heavy-duty electromechanical disc brakes. Firstly, system dynamics model of a single piston electromechanical disc brake is constructed in Matlab/Simulink environment in consideration of nonlinear friction model. To ensure the accuracy of system dynamics model, open loop measurements (clamping force, motor angle and motor current) are conducted on a prototype of single piston electromechanical disc brake. Experimental data is used for the determination of system variables. The predicted outputs are verified by comparison of experimental measurement results. For the control purpose of electromechanical brake, a multi-stage closed loop architecture is introduced. To regulate clamping force, PID and sliding mode controllers are developed in Matlab/Simulink in consideration of braking performance requirements for heavy-duty vehicles. For management of running clearance between brake disc and pad, PID position controller is developed in simulation environment. Those controller parameters obtained in the simulation process are introduced to the control hardware. After that closed loop clamping force and position measurements are conducted. When the rise time values of both clamping force controllers are compared, it is seen that sliding mode controller can reach to the settling point faster than PID controller. As for the position controller, the rise time requirement has been achieved by the designed PID controller. The switching process of force and position controllers are implemented and functional closed loop measurements are conducted for different reference input signals. It is seen from the results that the rise time requirement for position controller has been achieved. The system response of clamping force parameter is considerably stable.

Machine Learning-Based Modeling and Predictive Control of Combustion Phasing and Load in a Dual-Fuel Low-Temperature Combustion Engine

<div>Reactivity-controlled compression ignition (RCCI) engine is an innovative dual-fuel strategy, which uses two fuels with different reactivity and physical properties to achieve low-temperature combustion, resulting in reduced emissions of oxides of nitrogen (NO<sub>x</sub>), particulate matter, and improved fuel efficiency at part-load engine operating conditions compared to conventional diesel engines. However, RCCI operation at high loads poses challenges due to the premixed nature of RCCI combustion. Furthermore, precise controls of indicated mean effective pressure (IMEP) and CA50 combustion phasing (crank angle corresponding to 50% of cumulative heat release) are crucial for drivability, fuel conversion efficiency, and combustion stability of an RCCI engine. Real-time manipulation of fuel injection timing and premix ratio (PR) can maintain optimal combustion conditions to track the desired load and combustion phasing while keeping maximum pressure rise rate (MPRR) within acceptable limits.</div> <div>In this study, a model-based controller was developed to track CA50 and IMEP accurately while limiting MPRR below a specified threshold in an RCCI engine. The research workflow involved development of an imitative dynamic RCCI engine model using a data-driven approach, which provided reliable measured state feedback during closed-loop simulations. The model exhibited high prediction accuracy, with an <i>R</i><sup>2</sup> score exceeding 0.91 for all the features of interest. A linear parameter-varying state space (LPV-SS) model based on least squares support vector machines (LS-SVM) was developed and integrated into the model predictive controller (MPC). The controller parameters were optimized using genetic algorithm and closed-loop simulations were performed to assess the MPC’s performance. The results demonstrated the controller’s effectiveness in tracking CA50 and IMEP, with mean average errors (MAE) of 0.89 crank angle degree (CAD) and 46 kPa and Mean absolute percentage error (MAPE) of 9.7% and 7.1%, respectively, while effectively limiting MPRR below of 10 bar/CAD. This comprehensive evaluation showcased the efficacy of the model-based control approach in tracking CA50 and IMEP while constraining MPRR in the dual-fuel engine.</div>

Adaptive resilient control for a class of nonlinear distributed parameter systems with actuator faults

This paper presents a new model-based fault resilient control scheme for a class of nonlinear distributed parameter systems (DPS) represented by parabolic partial differential equations (PDE) in the presence of actuator faults. A Luenberger-like observer on the basis of nonlinear PDE representation of DPS is developed with boundary measurements. A detection residual is generated by taking the difference between the measured output of the DPS and the estimated one given by the observer. Once a fault is detected, an unknown actuator fault parameter vector together with a known basis function is utilized to adaptively estimate the fault dynamics. A novel tuning algorithm is derived to estimate the unknown actuator fault parameter vector. Next, in order to achieve resilience, the controller from the healthy scenario is adjusted to mitigate the faults by using both estimated fault dynamics and a secondary measurement. Subsequently, an explicit formula is developed to estimate the time-to-resilience (TTR). Finally, a nonlinear example is utilized to illustrate the effectiveness of the proposed scheme.

MPMC-frame: Multiplatform migration control framework for manipulator control

Existing robot control models suffer from poor generalization performance due to varied tasks between manipulators, configuration differences, and physical limits of motion. To address this problem, a multiplatform migration control framework (MPMC-frame) based on a constrained dynamics model is proposed in this paper. First, a model of the robotic manipulator dynamics with configuration adjustment (CA) is constructed based on a neural network to unify the physical joint data of different manipulators. Second, the model-based controller that can be integrated into the framework is designed, and the constraint on the upper bound on the error of uncertain parameters in the control law to guarantee the control robustness and accuracy of the controller for different manipulator platforms. Final, the generic potential function is designed based on multiplatform task requirements, and a task parameter table is constructed to improve the joint motion control performance of MPMC-frame. The feasibility of the method proposed in this paper are verified through simulations and experiments based on the ABB_irb120 and AUBO_i5 robotic manipulators.

Integrated torque-vectoring and anti-roll moment distribution strategies based on optimal control: influence of model complexity and road curvature preview

Although the vehicle dynamics effects of variable anti-roll moment distribution actuated through active suspension systems are widely discussed in the literature, their model-based control has only been recently analysed, given the highly nonlinear nature of the involved dynamics. Moreover, the available studies do not discuss the trade-off between internal model complexity and controller performance, nor analyse the opportunities offered by vehicle connectivity, which enables the prediction of the steering angle and reference yaw rate profiles ahead. To address the gap, this paper introduces and assesses three optimal controllers for an electric vehicle with active suspensions, multiple powertrains, and a brake-by-wire system. The formulations are: (a) a gain scheduled output feedback linear quadratic regulator (OFLQR); (b) a nonlinear model predictive controller using a three-degree-of-freedom prediction model, without and with preview of the steering angle and reference yaw rate ahead, respectively referred to as NMPC-3 and NMPC-3-Pre; and (c) a nonlinear model predictive controller based on an eight-degree-of-freedom prediction model, referred to as NMPC-8 and NMPC-8-Pre depending on the absence or presence of preview. The results on an experimentally validated model show that: (i) NMPC-8 provides evident yaw rate tracking benefits with respect to (w.r.t) OFLQR and NMPC-3; and (ii) NMPC-8-Pre can bring ∼20% yaw rate tracking improvement w.r.t. an optimally tuned NMPC-8 configuration.

Realization of digital twin for dynamic control towardsample variation of ion exchange chromatography in antibody separation.

Digital twin (DT) is a virtual and digital representation of physical objects or processes. In this paper, this concept is applied to dynamic control of the collection window in the ion exchange chromatography (IEC) towardsample variations. A possible structure of a feedforward model-based control DT system was proposed. Initially, a precise IEC mechanistic model was established through experiments, model fitting, and validation. The average root mean square error (RMSE) of fitting and validation was 8.1% and 7.4%, respectively. Then a model-based gradient optimization was performed, resulting in a 70.0% yield with a remarkable 11.2% increase. Subsequently, the DT was established by systematically integrating the model, chromatography system, online high-performance liquid chromatography, and a server computer. The DT was validated under varying load conditions. The results demonstrated that the DT could offer an accurate control with acidic variants proportion and yield difference of less than 2% compared to the offline analysis. The embedding mechanistic model also showed a positive predictive performance with an average RMSE of 11.7% during theDT test under >10% sample variation. Practical scenario tests indicated that tightening the control target could further enhance the DT robustness, achieving over 98% success rate with an average yield of 72.7%. The results demonstrated that the constructed DT could accurately mimic real-world situations and perform an automated and flexible pooling in IEC. Additionally, a detailed methodology for applying DT was summarized.

The Effects of Parameter Settings on Triggering Time and Climb Rate during Lean-Premixed Combustion Thermoacoustic Oscillations

This study theoretically explored the effects of parameter settings on thermoacoustic oscillations with a low-order model. Three factors were explored—combustor length, inlet gas temperature and thermal power. The research findings indicate that optimizing the parameter settings can yield better thermoacoustic oscillation suppression results. The sound pressure amplitude decreased from 3.2 × 105 Pa to 2.1 × 105 Pa as the combustor length increased from 1.2 m to 6.0 m. The triggering time increased from 0.32 s to 0.91 s when the combustion chamber length increased. The climb rate declined from 23.38 × 105 Pa/s to 3.75 × 105 Pa/s when the combustor length was elongated. The sound pressure amplitude decreased from 3.44 × 105 Pa to 2.4 × 105 Pa as the gas temperature rose from 0 to 100 °C. The triggering time and climb rate variation tendency were similar when the gas temperature increased—both declined as the gas temperature rose. The sound pressure amplitude experienced a slight fluctuation when the thermal power rose. However, the triggering time decreased from 0.26 s to 0.043 s when the thermal power improved. The climb rate increased from 18.72 × 105 Pa/s to 27.65 × 105 Pa/s when the thermal power rose. The oscillation frequency presented was completely different in three cases that had different wavelengths and oscillation intensities. The triggering time and climb rate fluctuated extensively in varying conditions, and the above two factors were interrelated and contradictory to each other when thermoacoustic oscillation was excited. This study explored parameters’ effects on triggering time and climb rate, thereby providing references for constructing a model-based control system for thermoacoustic oscillation feedback control.

Inter-discharge optimization for fast, reliable access to ASDEX Upgrade advanced tokamak scenario

Rapid inter-discharge simulation and optimization using the RAPTOR code have allowed the development of a reliable and reproducible early heating strategy for an advanced tokamak (AT) scenario on ASDEX Upgrade. Solving for electron heat and current diffusion in RAPTOR with ad-hoc formulas for heat transport and electron cyclotron current drive (ECCD) efficiency is found to robustly recover the coupled dynamics of Te and q, while maintaining model parameters fixed for all discharges. The pedestal top boundary condition in pre-shot simulations is set by a newly derived scaling law for the electron pressure at ρ = 0.8, using a data set of previous AT discharges. RAPTOR simulations have allowed to develop an understanding of the onset of 3/2 tearing modes, which were observed to have a detrimental impact on confinement when low magnetic shear conditions are present at the rational surface during the high-β phase. Delaying the NBI heating, by a specific time interval found via simulations, has led to avoiding these modes. A non-linear optimization scheme has been applied to optimize the ECCD deposition radii to reach a stationary state with qmin>1 at the beginning of the flat-top phase, while ensuring a non-zero magnetic shear at q = 1.5 throughout the high-β phase, and has been successfully tested in experiment. However, further experiments, aiming for qmin>1.5 , have highlighted limitations of the present feedforward control approach in the presence of shot-to-shot variations that are not included in the applied model. Application of real-time model-based control is proposed to overcome model-reality mismatches in future work.

Cartesian Stiffness Shaping of Compliant Robots—Incremental Learning and Optimization Based on Sequential Quadratic Programming

Emerging robotic systems with compliant characteristics, incorporating nonrigid links and/or elastic actuators, are opening new applications with advanced safety features, as well as improved performance and energy efficiency in contact tasks. However, the complexity of such systems poses challenges in modeling and control due to their nonlinear nature and model variations over time. To address these challenges, the paper introduces Locally Weighted Projection Regression (LWPR) and its online learning capabilities to keep the model of compliant actuators accurate and enable the model-based controls to be more robust. The approach is experimentally validated in Cartesian position and stiffness control for a 4 DoF planar robot driven by Variable Stiffness Actuators (VSA), whose real-time implementation is supported by the Sequential Least Squares Programming (SLSQP) optimization approach.