Data-driven Control Research Articles

Experimental closed-loop flow separation control: Data- and phenomenological-driven approaches

Flow control aims at modifying a natural flow state to reach an other flow state considered as advantageous. In this paper, active feedback flow separation control is investigated with two different closed-loop control strategies, involving a reference signal tracking architecture. Firstly, a data-driven control law, leading to a linear (integral) controller is employed. Secondly, a phenomenological/model-driven approach, leading to a non-linear positive (integral) control strategy is investigated. While the former benefits of a tuning simplicity, the latter prevents undesirable effects and formally guarantees closed-loop stability. Both control approaches were validated through wind tunnel experiments of flow separation control over a movable NACA 4412 plain flap. These control laws were designed with respect to hot-film measurements, performed over the flap for different deflection angles. Both control approaches proved efficient in avoiding flow separation. The main contribution of this work stands in providing practitioners, simple but yet efficient control design methods for the flow separation phenomena. Equivalently important, a complete validation campaign data-set is also provided.

Correction: Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing

Correction: Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing

The neuron as a direct data-driven controller

In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks.

A Data-Driven Approach to Set-Theoretic Model Predictive Control for Nonlinear Systems

In this paper, we present a data-driven model predictive control (DDMPC) framework specifically designed for constrained single-input single-output (SISO) nonlinear systems. Our approach involves customizing a set-theoretic receding horizon controller within a data-driven context. To achieve this, we translate model-based conditions into data series of available input and output signals. This translation process leverages recent advances in data-driven control theory, enabling the controller to operate effectively without relying on explicit system models. The proposed framework incorporates a robust methodology for managing system constraints, ensuring that the control actions remain within predefined bounds. By means of time sequences, the controller learns the underlying system dynamics and adapts to changes in real time, providing enhanced performance and reliability. The integration of set-theoretic methods allows for the systematic handling of uncertainties and disturbances, which are common when the trajectory of a nonlinear system is embedded inside a linear trajectory state tube. To validate the effectiveness of our DDMPC framework, we conduct extensive simulations on a nonlinear DC motor system. The results demonstrate significant improvements in control performance, highlighting the robustness and adaptability of our approach compared to traditional model-based MPC techniques.

Semantic model-based large-scale deployment of AI-driven building management applications

Digitalization and Artificial Intelligent (AI) are revolutionizing building operation management. The abundance of data generated with the digitalization of buildings in the whole lifecycle can be harnessed to enhance building operational efficiency through data-driven control and optimization applications. However, the heterogeneity of data across building datasets hampers data interactivity and interoperability, presenting obstacles to the large-scale deployment of AI-enabled data-driven solutions. A semantic model-based framework is developed to integrate multi-sources data from buildings' air-conditioning system, supporting the large-scale deployment of AI-enabled data-driven building management applications. Both static and temporal data from multi sources are stored in the database guided by the semantic model. To demonstrate the framework's effectiveness, a building cooling load prediction application is implemented and evaluated across three typical buildings. The successful deployment of the proposed semantic model-based framework demonstrates its potential for facilitating large-scale deployment of AI-enabled data-driven applications in building sector.

Data-Driven Control for Linear Discrete-Time Systems with Input Saturation

Data-Driven Control for Linear Discrete-Time Systems with Input Saturation

Data-driven event-triggering mechanism for linear systems subject to input saturation

This paper focuses on designing an event-triggering mechanism aimed at reducing control updates while maintaining the stability of a saturated closed-loop system. It addresses the regional stabilization of linear systems under input saturation conditions from a data-driven perspective. To do so, we propose a systematic method to convert model-driven conditions into data-driven control design Linear Matrix Inequality (LMI) conditions, enabling the co-design of the event-triggering rule and the state feedback gain. The theoretical contribution is then applied to the control of a spacecraft rendezvous problem.

Decision Tree Variations and Online Tuning for Real-Time Control of a Building in a Two-Stage Management Strategy

This study examines the use of data-driven controllers for near real-time control of an HVAC and storage system in a residential building. The work is based on a two-stage management with, first, a day-ahead optimal scheduling, and second, a near real-time adaptive control to remain close to the commitments made in the first stage. A Model Predictive Control (MPC) is adopted from previous works from the authors. The aim of this paper is then to explore lightweight controllers for the real-time stage as alternatives to MPC, which relies on computational-intensive modeling and optimization. Decision Trees (DTs) are considered for this purpose, offering understandable solutions by processing input data through explicit tests of the inputs with predefined thresholds. Various DT variations, including regular, regressors, and linear DTs, are studied. Linear DTs, with a minimal number of leaves, exhibit superior performance, especially when trained on historical MPC data, outperforming the reference MPC in terms of energy exchange efficiency. However, due to impracticalities, an offline training approach for the DTs is proposed, which sacrifices performance. An online tuning strategy is then introduced, updating the DT coefficients based on real-time observations, significantly enhancing performance in terms of energy deviation reduction during real-time operation.

Neural network predictive controller based on the improved TPA-LSTM model for ultra-supercritical units

To mitigate the impact of large-scale renewable energy power on the national grid in China, it is imperative to enhance the flexible peaking capability of coal-fired thermal power units. The coordinated control system, central to the load control of coal-fired units, faces challenges such as multivariable coupling, sluggish response, and uncertain coal quality parameters. This paper introduces a neural network predictive controller based on the improved TPA-LSTM model, aimed at addressing these issues. Initially, a data-driven control model is established to break through the limitations of traditional linear predictive control and effectively handle disturbance uncertainties. Then, a multivariable coordinated control strategy based on the neural network controller is designed, achieving effective decoupling of multiple parameters and ensuring high adaptability across all load conditions. Additionally, by integrating an automatic model updating mechanism, the system can recalibrate in real-time when model mismatches occur due to equipment aging, maintenance, or changes in coal quality, thereby enhancing overall control performance. Simulation results demonstrate that this strategy has excellent control effectiveness, meeting the flexible peaking demands of 1000 MW ultra-supercritical units. The calibration feature of the data-driven model significantly improves control performance following model mismatches.

On the Performance of the Model-Free Adaptive Control For A Novel Moving-Mass Controlled Flying Robot

In this paper, the performance of Model-Free Adaptive Control (MFAC) has been investigated on a novel and specific moving-mass controlled (MMC) flying robot system. The novel one-degree-of-freedom (1 DOF) MMC flying robot test bed presented in this paper has highly nonlinear and slow dynamics with a variable center of gravity (CoG) and moment of inertia. This makes the control of this system a challenging problem. One of the solutions to this challenge is the use of data-driven control methods, in particular, MFAC. This controller uses a data-driven model to control the system using only input and output (I/O) data. This paper compares this data-driven controller with proportional-integral-derivative (PID) control, and Linear Quadratic Regulator (LQR) as two model-free and model-based controllers which are widely used controllers in industry. The results of the comparison show that in the various scenarios applied, MFAC has a clear superiority over the PID and LQR, and its adaptive structure gives more freedom of action in the implementation of different scenarios and the presented noise. The results are obtained using the Integral Time Absolute Error (ITAE) criteria and the mean maximum error has also been compared in a Monte Carlo analysis. For a more detailed study, the amount of control energy consumption was also compared, which showed a clear superiority of the MFAC. Also, the robustness of the controller was demonstrated by introducing uncertainty in the plant parameters and by running 100 Monte Carlo simulations with random initial conditions. Finally, despite the PID controller, the MFAC followed the desired scenarios well and compared to LQR consumed less energy. The results demonstrate that the MFAC outperformed the PID and LQR controllers in the presence of random initial conditions and noise in terms of mean maximum error (70.4%)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(70.4\\%)$$\\end{document}, mean ITAE (91%)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(91\\%)$$\\end{document}, and energy consumption (46%)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(46\\%)$$\\end{document}.

Data-driven real-time fuel cetane estimation and control design for multifuel UAVs

Multifuel compression ignition (CI) engines have been proven to be useful for the aviation industry, especially for powering unmanned aerial vehicles (UAVs). To operate multifuel UAVs at desired operating conditions, an appropriate control strategy is needed that can adapt based on changing fuel properties. Therefore, having knowledge of fuel properties at all times is critical. However, the challenge comes in identifying fuel property in real-time when fuel switching happens during engine operation. The current work proposes a novel data-driven control algorithm for estimating fuel property (cetane number) and adapting the control strategy in real-time. Gaussian process regression (GPR) is used for modeling the multifuel CI engine, and feedforward control is developed to correct the control inputs based on the estimated fuel cetane property. For feedforward control development, model inversion of data-driven GPR is performed. Based on the estimated fuel property and using the feedforward table, the control strategy gets adjusted in real-time within one combustion cycle to avoid the system performance from drifting away beyond specified bounds. The entire approach was implemented and validated on an engine test bed. Fuel switching is performed in real time, and the results demonstrated the capability of the algorithm to maintain the desired performance.

Fully Data-Driven Robust Output Formation Tracking Control for Heterogeneous Multiagent System With Multiple Leaders and Actuator Faults.

This article investigates a fully data-driven method to solve the robust output formation tracking control problem for the multiagent system (MAS) under actuator faults. The outputs of the followers are controlled to track those of multiple leaders with respect to a convex point while achieving an expected time-varying formation. To obviate the requirement of various system prior knowledge in typical MAS control, a hierarchical frame is developed with three learning and control stages using the online measured data. First, a distributed adaptive observer is designed to coordinate the state convex of multiple leaders while estimating unknown dynamics. The adaptive mechanism relaxes the demand for global topology. Second, by collecting and reusing the online system data, an off-policy reinforcement learning (RL) method is proposed in a continuous form to acquire nominal feedback gains from partial observations of the followers. Essential system models are learned along with the RL process, while solutions to the output regulation equations are implicitly obtained. Third, a comprehensive robust controller is further presented based on the previous learning results. To address the actuator faults with efficiency loss and bias, the adaptive neural networks and robust compensations are utilized in a model-free manner. The output formation tracking is achieved under a derived feasibility condition while stabilities of the learning and control methods are analyzed. Finally, simulation results demonstrate the validity of this fully data-driven control frame.

Temperature Control of Fuel Cell Based on PEI-DDPG

Proton exchange membrane fuel cells (PEMFCs) constitute nonlinear systems that are challenging to model accurately. Therefore, a controller with robustness and adaptability is imperative for temperature control within the PEMFC stack. This paper introduces a data-driven controller utilizing deep reinforcement learning for stack temperature control. Given the PEMFC system’s characteristics, such as nonlinearity, uncertainty, and environmental conditions, we propose a novel deep reinforcement learning algorithm—the deep deterministic policy gradient with priority experience playback and importance sampling method (PEI-DDPG). Algorithm design incorporates technologies such as priority experience playback, importance sampling, and optimized sample data storage structure, enhancing the controller’s performance. Simulation results demonstrate the proposed algorithm’s superior effectiveness in temperature control for PEMFC, leveraging the PEI-DDPG algorithm’s high adaptability and robustness. The proposed algorithm’s effectiveness is additionally validated on the RT-LAB experimental platform. The proposed PEI-DDPG algorithm reduces the average adjustment time by 8.3%, 17.13%, and 24.56% and overshoots by 2.12 times, 4.16 times, and 4.32 times compared to the TD3, GA-PID, and PID algorithms, respectively.

Robust Policy Iteration of Uncertain Interconnected Systems With Imperfect Data

This paper investigates the robust optimal control problem of a class of continuous-time, partially linear, interconnected systems. In addition to the dynamic uncertainties resulted from the interconnected dynamic system, unknown bounded disturbances are taken into account throughout the learning process, wherein the system’s dynamics and the disturbances are assumed unknown. These challenges lead the collected online data to be imperfect. In this scenario, traditional data-driven control techniques, such as adaptive dynamic programming (ADP) and robust ADP, encounter a challenge in approximating the optimal control policy precisely due to imperfect data and computational errors. In this paper, a novel data-driven robust policy iteration method is proposed to simultaneously solve the robust optimal control problems. Without relying on the knowledge of the system’s dynamics, the external disturbances or the complete state, the implementation of the proposed method only needs to access the input and partial state information. Based on the small-gain theorem, the notions of strong unboundedness observability and input-to-output stability, it is guaranteed that the learned robust optimal control gain is stabilizing and that the solution of the closed-loop system is uniformly ultimately bounded despite the existence of dynamic uncertainties and unknown external disturbances. The simulation results reveal the efficiency and practicality of the proposed data-driven control method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the use of reinforcement learning to improve the quality of designing adaptive optimal controllers for engineering applications. Adaptive dynamic programming methods, in particular policy iteration (PI), are widely used in solving optimal control problems. However, due to the iterative nature of PI, the approximated optimal control policy may be inaccurate and imprecise. Especially, when using imperfect system’s measurements instead of the modelling information. This can result in causing the learned control policy to deviate from the actual optimal policy. This becomes more challenging in the existence of dynamic uncertainties and unknown external disturbances which corrupt the measurements and result in imperfect data. This work investigates the conditions on the uncertainties such that the proposed novel data-driven PI algorithm is robust to system’s uncertainties, unknown external disturbances and imperfect measurements. The approximated robust optimal control policy performs robustly in the existences of imperfect data and uncertainties, and at the same time is close enough to the optimal control policy.

Determination of Gain Scheduling Parameters for Loss of a Feedwater Pump Transient Mitigation Using Neural Networks

Transient mitigation for nuclear power plants is essential for safe operation. The fourth industrial revolution brings with it the potential for data-based predictive maintenance and identifying remaining time of life for degrading components. An improvement to predictive maintenance would be to address continued operation with faulty components between the time of identification and eventual replacement. The ability to perform data analysis and contemporary digital control systems allows for data-driven control system actions. A methodology is developed herein to train a neural network that can map desired system performance and current plant component capability to control system settings. Simulations of plant transients were recorded and used to train a neural network. This neural network was tested with different target performance goals. The results show that the trained neural network recommended settings that affected the control system response so as to meet the target performance goals.

Control System Design

There are a number of tasks in the modern world related to the control and management of the product life cycle in industries, that are solved using PLM systems. The PLM systems consist of several systems or modules connected by a enterprise service bus. The ESB allows interaction between modules, including data exchange. However, the enterprise could implement a business process using its own information system, and it is necessary to solve the problem of smooth implementation of the PLM system and it’s use with the organization's one. This paper considers an approach based on using an adapter mechanism that contains not only an interface for interacting with an information system, but also implements control logic, that generates the patterns of system behavior as output data obtained using the data-driven control approach. The purpose of the article is to model the control system for integration with the PLM system. The general visions of the chalengies, the methods are used to build the adapter, the adapter’s space in the PLM are considered, the proposed approach and the further research notes are described.

Data-driven control of a five-bar parallel robot with compliant joints

This paper presents a data-driven control approach for a five-bar robot with compliant joints. The robot consists of a parallel mechanism with compliant elements that introduce uncertainties in modeling and control. To address this fact, it is implemented a model-less data-driven controller based on a Feedforward Neural Network Module (FNNM) that identifies the inverse dynamics of the robot. The FNNM is incorporated into a coordination of Feedforward Control Method (CFCM) to achieve precise trajectory tracking. Experiments compare the compliant joints robot to a bearing-joint robot performing pick-and-place tasks from 0.15 to 3.15 Hz. Results show the compliant robot maintaining trajectory tracking up to 1.25 Hz with a Root Mean Square Error (RMSE) of 9.02 mm.

Data-driven control of singularly perturbed hybrid systems with multi-rate sampling

This paper addresses the exponential stability problem of singularly perturbed hybrid systems with multi-rate sampling. Firstly, a hybrid model is established to describe a class of nonlinear singularly perturbed systems by designing a data-driven mechanism and multi-rate sampling controller. Secondly, a novel error function and several inequalities are proposed to obtain the properties of the error subsystems. Thirdly, we estimate the bounds of fast and slow sampling intervals, and analyze the effect of the perturbation parameter and fast-slow sampling ratio on the sampling intervals. Finally, a DC motor model is illustrated to demonstrate the effectiveness of the proposed results.

Data-Driven Controller for Drivers’ Steering-Wheel Operating Behaviour in Haptic Assistive Driving System

An advanced driver-assistance system (ADAS) is critical to driver–vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADASs. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving–vehicle–road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. These differences can lead to poor driving performance if not properly accounted for. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A backpropagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then, the genetic algorithm (GA) optimises the integral time absolute error (ITEA) function to produce the best multiple PID compensation parameters for various driving behaviours (such as speeding/braking, lane-keeping and turning), which are then utilised by the fuzzy logic to provide different driving commands. An experiment was conducted with five participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA proportional-integral-derivative (PID) offline, and the fuzzy-PID controller applied online. The third experiment was conducted to validate the proposed data-driven controller. The experiment and simulation results evaluated the ITAE of the lateral displacement and yaw angle during various driving behaviours. The results validated the proposed method by significantly enhancing the driving performance.

Correction: Ghnatios et al. A Regularized Real-Time Integrator for Data-Driven Control of Heating Channels. Computation 2022, 10, 176

There was an error in the original publication, section data availability, stating “Data is available upon request” [...]