Data-driven Control Research Articles

Data Driven Control and slip estimation for Agricultural Tracked Vehicles

This paper focusses on the path following problem for autonomous Unmanned Ground Vehicles developed for precision agriculture. Such vehicles typically face rough terrain and narrow navigating spaces, so reliability of odometry is largely reduced. In this context, slip estimation/accommodation becomes crucial for localization and navigation of vehicles. In this work, a control architecture is presented coupling MIMO Data-Driven Control and Sliding Mode Control, with the inclusion of a fault estimation mechanism aimed at mitigating the actuators’ loss of effectiveness associated to slipping. The supporting theoretical development makes use of previous results available in the Model Free Adaptive Control framework, which have been extended in order to deal with the path following problem for the vehicle. On the basis of extensive simulations, the proposed control policy is shown able to strengthen the recovery capability and robustness inherently owned by the original adaptive mechanism and control laws.

Adaptive Robust Data-Driven Building Control via Bilevel Reformulation: An Experimental Result

Data-driven control approaches for the minimization of energy consumption of buildings have the potential to significantly reduce deployment costs and increase the uptake of advanced control in this sector. A number of recent approaches based on the application of Willems’ fundamental lemma for data-driven controller design from input/output measurements are very promising for deterministic linear time-invariant (LTI) systems. This article proposes a systematic way to handle unknown measurement noise and measurable process noise, and extends these data-driven control schemes to adaptive building control via a robust bilevel formulation, whose upper level ensures robustness and whose lower level guarantees prediction quality. Corresponding numerical improvements and an active excitation mechanism are proposed to enable a computationally efficient reliable operation. The efficacy of the proposed scheme is validated by a multizone building simulation and a real-world experiment on a single-zone conference building on the École Polytechnique Fédérale de Lausanne (EPFL) campus. The real-world experiment includes a 20-day nonstop test, where, without extra modeling effort, our proposed controller improves 18.4% energy efficiency against an industry-standard controller while also robustly ensuring occupant comfort.

Data-Driven Self-Triggered Control via Trajectory Prediction

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, a majority of existing self-triggered control methods require explicit system models. An end-to-end control paradigm known as data-driven control designs control laws directly from data and offers a competing alternative to the routine system identification-then-control strategy. In this context, the present paper puts forth data-driven self-triggered control schemes for unknown linear systems using input-output data collected offline. Specifically, a data-driven model predictive control (MPC) scheme is proposed, which computes a sequence of control inputs while generating a predicted system trajectory. In addition, a data-driven self-triggering mechanism is designed, which determines the next triggering time using the solution of the data-driven MPC and the newly collected measurements. Finally, both feasibility and stability are established for the proposed self-triggered controller, which are validated using a numerical example.

Data-Driven Practical Cooperative Output Regulation Under Actuator Faults and DoS Attacks.

This article addresses the resilient practical cooperative output regulation problem (RPCORP) for multiagent systems subjected to both denial-of-service (DoS) attacks and actuator faults. Fundamentally different from the existing solutions to RPCORPs, the system parameters considered in this article are unknown to each agent, and a novel data-driven control approach is introduced to handle such an issue. The solution starts with developing resilient distributed observers for each follower in the presence of DoS attacks. Then, a resilient communication mechanism and a time-varying sampling period are introduced to, respectively, ensure the neighbor state is available as soon as attacks disappear and to avoid targeted attacks launched by intelligent attackers. Furthermore, a model-based fault-tolerant and resilient controller is designed based on the Lyapunov approach and the output regulation theory. In order to remove the reliance on system parameters, we leverage a new data-driven algorithm to learn controller parameters via the collected data. Rigorous analysis shows that the closed-loop system can resiliently achieve practical cooperative output regulation. Finally, a simulation example is given to illustrate the effectiveness of the achieved results.

Distributed Neighbor Selection in Multiagent Networks

Achieving consensus via nearest neighbor rules is an important prerequisite for multi-agent networks to accomplish collective tasks. A common assumption in consensus setup is that each agent interacts with all its neighbors. This paper examines whether network functionality and performance can be maintained-and even enhanced-when agents interact only with a subset of their respective (available) neighbors. As shown in the paper, the answer to this inquiry is affirmative. In this direction, we show that by exploring the monotonicity property of the Laplacian eigenvectors, a neighbor selection rule with guaranteed performance enhancements, can be realized for consensus-type networks. For distributed implementation, a quantitative connection between entries of Laplacian eigenvectors and the “relative rate of change” in the state between neighboring agents is further established; this connection facilitates a distributed algorithm for each agent to identify “favorable” neighbors to interact with. Multi-agent networks with and without external influence are examined, as well as extensions to signed networks. This paper underscores the utility of Laplacian eigenvectors in the context of distributed neighbor selection, providing novel insights into distributed data-driven control of multi-agent systems.

Design and Implementation of a Recursive Feedforward-Based Virtual Reference Feedback Tuning (VRFT) Controller for Temperature Uniformity Control Applications

Data-driven controller synthesis methods use input/output information to find the coefficients of a proposed control architecture. Virtual Reference Feedback Tuning (VRFT) is one of the most popular frameworks due to its simplicity and one-shoot synthesis style based on open-loop system response for classic regulators such as PI or PID. This paper presents a recursive VRFT framework to extend VRFT into high-order controllers with more complex structures. The framework first defines a reference model and controller structure, then uses the open-loop data to compute the virtual reference and error signals, and, finally, uses these to find the controller parameters via an optimization algorithm. Likewise, the recursive VRFT controller performance is improved by adding a model-based feedforward loop to improve reference signal tracking. The recursive method is tested to design a temperature uniformity control system. The obtained results show that the recursive VRFT with a feedforward improves the system response while allowing more complex controller synthesis.

Data-driven control of airborne infection risk and energy use in buildings

The global devastation of the COVID-19 pandemic has led to calls for a revolution in heating, ventilation, and air conditioning (HVAC) systems to improve indoor air quality (IAQ), due to the dominant role of airborne transmission in disease spread. While simple guidelines have recently been suggested to improve IAQ mainly by increasing ventilation and filtration, this goal must be achieved in an energy-efficient and economical manner and include all air cleaning mechanisms. Here, we develop a simple protocol to directly, quantitatively, and optimally control transmission risk while minimizing energy cost. We collect a large dataset of HVAC and IAQ measurements in buildings and show how models of infectious aerosol dynamics and HVAC operation can be combined with sensor data to predict transmission risk and energy consumption. Using this data, we also verify that a simple safety guideline is able to limit transmission risk in full data-driven simulations and thus may be used to guide public health policy. Our results provide a comprehensive framework for quantitative control of transmission risk using all available air cleaning mechanisms in an indoor space while minimizing energy costs to aid in the design and automated operation of healthy, energy-efficient buildings.

CRAWLING: a crowdsourcing algorithm on wheels for smart parking

We present the principled design of CRAWLING: a CRowdsourcing Algorithm on WheeLs for smart parkING. CRAWLING is an in-car service for the routing of connected cars. Specifically, cars equipped with our service are able to crowdsource data from third-parties, including other cars, pedestrians, smart sensors and social media, in order to fulfill a given routing task. CRAWLING relies on a solid control-theoretical formulation and the routes it computes are the solution of an optimal data-driven control problem where cars maximize a reward capturing environmental conditions while tracking some desired behavior. A key feature of our service is that it allows to consider stochastic behaviors, while taking into account streams of heterogeneous data. We propose a stand-alone, general-purpose, architecture of CRAWLING and we show its effectiveness on a set of scenarios aimed at illustrating all the key features of our service. Simulations show that, when cars are equipped with CRAWLING, the service effectively orchestrates the vehicles, making them able to react online to road conditions, minimizing their cost functions. The architecture implementing our service is openly available and modular with the supporting code enabling researchers to build on CRAWLING and to replicate the numerical results.

Data-Driven Control: Part One of Two: A Special Issue Sampling from a Vast and Dynamic Landscape

Data-Driven Control: Part One of Two: A Special Issue Sampling from a Vast and Dynamic Landscape

Powered Knee and Ankle Prosthesis Control for Adaptive Ambulation at Variable Speeds, Inclines, and Uneven Terrains.

Ambulation in everyday life requires walking at variable speeds, variable inclines, and variable terrains. Powered prostheses aim to provide this adaptability through control of the actuated joints. Some powered prosthesis controllers can adapt to discrete changes in speed and incline but require manual tuning to determine the control parameters, leading to poor clinical viability. Other data-driven controllers can continuously adapt to changes in speed and incline but do so by imposing the same non-amputee gait patterns for all amputee subjects, which does not consider subjective preferences and differing clinical needs of users. Here, we present a controller for powered knee and ankle prostheses that can continuously adapt to different walking speeds, inclines, and uneven terrains without enforcing a specific prosthesis position, impedance, or torque. A virtual biarticular muscle connection determines the knee flexion torque, which changes with both speed and slope. Adaptation to inclines and uneven terrains is based solely on the global shank orientation. Continuously variable damping allows for speed adaptation. Minimum-jerk programming defines the prosthesis swing trajectory at variable cadences. Experiments with one individual with an above-knee amputation suggest that the proposed controller can effectively adapt to different walking speeds, inclines, and rough terrains.

Data-Driven Control: Part One of Two [About this Issue

Data-Driven Control: Part One of Two [About this Issue

Learning Controllers From Data via Approximate Nonlinearity Cancellation

We introduce a method to deal with the data-driven control design of nonlinear systems. We derive conditions to design controllers via (approximate) nonlinearity cancellation. These conditions take the compact form of data-dependent semi-definite programs. The method returns controllers that can be certified to stabilize the system even when data are perturbed and disturbances affect the dynamics of the system during the execution of the control task, in which case an estimate of the robustly positively invariant set is provided.

Data-Driven Control Based on the Behavioral Approach: From Theory to Applications in Power Systems

Data-Driven Control Based on the Behavioral Approach: From Theory to Applications in Power Systems

On a Stochastic Fundamental Lemma and Its Use for Data-Driven Optimal Control

Data-driven control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise. However, besides measurement noise, stochastic disturbances might also directly affect the dynamics. In this paper, we leverage Polynomial Chaos Expansions (PCE) to extend the deterministic fundamental lemma towards stochastic systems. This extension allows to predict future statistical distributions of the inputs and outputs for stochastic LTI systems in data-driven fashion, i.e., based on the knowledge of previously recorded input-output-disturbance data and of the disturbance distribution we perform data-driven uncertainty propagation. Finally, we analyze data-driven stochastic optimal control problems and we propose a conceptual framework for data-driven stochastic predictive control. Numerical examples illustrate the efficacy of the proposed concepts.

Fractional robust data-driven control of nonlinear MEMS gyroscope

Fractional robust data-driven control of nonlinear MEMS gyroscope

Data-driven control of wave energy systems using random forests and deep neural networks

In the field of sustainable energy and alternatives to fossil fuels, wave energy is generating an increasing interest due to its untapped potential. However, the levelised cost of energy of wave energy systems is still not able to compete with other renewable technologies, mainly due to high costs associated with their conversion process. In this context, the development of energy-maximising control strategies plays an important role towards the economic viability of wave energy technology, by optimising the overall energy conversion, hence contributing towards minimising the associated cost of energy. State-of-the-art control systems for wave energy converters are mostly model-based, exploiting control-oriented models of the device to compute the applied control actions with a limited computational burden. Nevertheless, these representations of the system are simplified, and often based upon unrealistic assumptions, such as small motion around the zero equilibrium position, which are inherently invalidated during device operations and that can lead to large uncertainties, resulting in suboptimal power absorption. For these reasons, in this paper, a purely data-driven control strategy is developed. This strategy exploits random forests (RFs) and deep neural networks (DNNs) to gradually learn from real experiences towards an optimal proportional–integral control action. These structures are used as surrogate models (built upon the data coming from past experiences) to converge to the optimal control parameters in a surrogate-optimisation-like manner. To manage the exploration and exploitation needs of controllers based on this approach, a learning strategy is developed and presented. Some considerations are made on the choice of the input features of the surrogate structures, which deeply affect the control strategy learning results. To assess the performances of both the control and learning strategies, one year of operations has been simulated under control settings guided by the proposed data-driven approach, showing also the potential capabilities that the adoption of RFs and DNNs has in learning, even in sea conditions with a limited number of occurrences.

High-Linear Frequency-Swept Lasers with Data-Driven Control

The frequency-swept laser (FSL) is applied widely in various sensing systems in the scientific and industrial fields, especially in the light detection and ranging (Lidar) area. However, the inherent nonlinearity limits its performance in application systems, especially in the broadband frequency-swept condition. In this work, from the perspective of data-driven control, we adopt the reinforcement learning-based broadband frequency-swept linearization method (RL-FSL) to optimize the control policy and generate the modulation signals. The nonlinearity measurement system and the system simulator are established. Since the powerful learning ability of the reinforcement learning algorithm, the linearization policy is optimized off-line and the generated modulation signals reduce the nonlinearity almost 20 times, compared to the case without control. In the long-term operation, the regular updated modulation signals perform better than the traditional iteration results, demonstrating the efficiency of the proposed data-driven control method in application systems. Therefore, the RL-FSL method has the potential to be the candidate of optical system control.

DA Motor Kontrolünde Veri Güdümlü ve Model Tabanlı Yöntemlerin Ani Yük Değişimlerine Karşı Tepkilerinin Analizi

Doğru Akım (DA) motor hız denetleyicilerinde bozucu etkilere karşı direnç gösterme ve her türlü dış etki karşısında referans noktasını en az hata ile takip etmek kritik öneme sahiptir. DA motorlarda en sık karşılaşılan bozucu etki ani yük değişimleridir. Bu nedenle denetleyicilerin ani yük değişimlerine karşı hızlı ve etkili bir yanıt oluşturulması ve referans değerden en az sapmayı gerçekleştirmesi gerekir. Bu çalışmada DA motorlarda meydana gelebilecek ani yük değişimlerine karşı model tabanlı ve veri güdümlü yöntemlerin yanıtları analiz edilmiştir. Veri güdümlü kontrol (VGK), denetleyiciyi tasarlamak ve optimize etmek için toplanan giriş-çıkış verilerini kullanan öğrenme tabanlı bir kontrol yöntemidir. Model tabanlı kontrol (MTK) yönteminde ise, kontrol edilecek sistemin matematiksel modeli hesaplanır. Çalışma kapsamında model tabanlı yöntem olarak Oransal-İntegral-Türev (PID), veri güdümlü yöntemler olarak yapay sinir ağları (YSA) ve kontrol süreçlerinde zaman serilerini de dikkate alan dışsal girdili otoregresif sinir ağları (NARX) denetleyiciler incelenmiştir. Böylece DA motor hız kontrolünde model tabanlı, veri güdümlü ve veri güdümlü + zaman serili olmak üzere üç farklı yaklaşımın performansları incelenmiştir. Deneysel çalışmalarda simülasyon değil gerçek motorlar kullanılmış ve deneyler 100 rpm (DAM1) ve 300 rpm (DAM2) hızına sahip kalıcı mıknatıslı DA motorlar kullanılarak gerçek zamanlı olarak gerçekleştirilmiştir. Elde edilen sonuçlar, toplam normalize hata, yükselme zamanı ve maksimum yüzde aşma performans ölçütleri kullanılarak sunulmuş ve yöntemlerin başarılı ve başarısız yönleri tartışılmıştır.

Comparative Performance Analysis of Intelligent PID Controllers for a Mechatronic System

Abstract Applications of systems engineering in the automotive field have grown over the past two decades. This paper aims at a Model-Free Control (MFC) strategy for this type of application. It is a control method in the Data Driven Control (DDC) category that is suitable for systems with complex dynamics, uncertainties or dynamics that are difficult to model or with an unavailable model. Considering these reasons, MFC is an appropriate control method applicable to the complex dynamics of automotive systems. This paper presents a comparative analysis of two types of MFC controllers: intelligent proportional (iP) and intelligent proportional derivative (iPD) for a mechatronic system application. The system takes into account the longitudinal speed control of the vehicle. First, some authors results on the design of iP and iPD controllers are reviewed. Then, the intelligent controllers are tested in simulation considering the requirement to maintain the desired vehicle speed under different traffic conditions such as urban and extra-urban driving cycles. The control performances of the iP and iPD controllers are compared using different performance indices, with a classical PID controller added to the comparison.

Digital twin-enabled domain adaptation for zero-touch UAV networks: Survey and challenges

In existing wireless networks, the control programs have been designed manually and for certain predefined scenarios. This process is complicated and error-prone, and the resulting control programs are not resilient to disruptive changes. Data-driven control based on Artificial Intelligence and Machine Learning (AI/ML) has been envisioned as a key technique to automate the modeling, optimization and control of complex wireless systems. However, existing AI/ML techniques rely on sufficient well-labeled data and may suffer from slow convergence and poor generalizability. In this article, focusing on digital twin-assisted wireless unmanned aerial vehicle (UAV) systems, we provide a survey of emerging techniques that can enable fast-converging data-driven control of wireless systems with enhanced generalization capability to new environments. These include simultaneous localization and sensing (SLAM)-based sensing and network softwarization for digital twin construction, robust reinforcement learning and system identification for domain adaptation, and testing facility sharing and federation. The corresponding research opportunities are also discussed.