Enabling Autonomy in Command and Control via Game-Theoretic Models and Machine Learning with a Systems Perspective

Raz, Ali K.; Blasch, Erik; Cruise, Robert; Natarajan, Sriraam

doi:10.2514/6.2019-0381

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…In the control systems engineering area, students should develop autonomy to solve practical problems that were found in the industry, e.g., fluid level or flow control, temperature regulation, pressure control, among others. In the work that is presented in [11], a machine learning-based system that uses a game formulation for engineering collaboration to develop autonomy in control engineering was presented. While taking into account that remote control systems are in evidence in both academic and industrial areas [12], educational processes can be used to replicate complex systems that are frequently found in the industry.…”

Section: Control Engineering Learning Involving the Ball And Beam Promentioning

confidence: 99%

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A Generic WebLab Control Tuning Experience Using the Ball and Beam Process and Multiobjective Optimization Approach

et al. 2020

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In control engineering education, the possibility of using a real control system in the learning process motivates professors to improve both students’ knowledge and skills, thus avoiding an approach only based on control theory. While considering that control engineering laboratories are expensive, mainly because educational plants should reproduce classical problems that are found in the industry, the use of virtual laboratories appears as an interesting strategy for reducing costs and improving the diversity of experiments. In this research, remote experimentation was assumed regarding the ball and beam process as an alternative didactic methodology. While assuming a nonlinear and unstable open-loop process, this study presents how students should proceed to control the plant focusing on the topic that is associated with multiobjective optimization. Proportional-Integral-Derivative (PID) controller was tuned considering the Non-dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the WebLab learning procedures described in this research. The proposed strategy was compared to the Åström’s robust loop shaping method to emphasize the performance of the multiobjective optimization technique. Analyzing the feedback provided by the students, remote experimentation can be seen as an interesting approach for the future of engineering learning, once it can be directly associated with industry demand of connected machines and real-time information analysis.

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Section: Control Engineering Learning Involving the Ball And Beam Promentioning

confidence: 99%

“…Moreover, the velocity of the beam and its acceleration are also both considered to be small. In this way, the transfer function of the ball and beam process can be obtained while considering the full formulation associating the ball position (output of the system), and the beam angle (input of the system), see Equation (11).…”

Section: Full Modelmentioning

confidence: 99%

A Generic WebLab Control Tuning Experience Using the Ball and Beam Process and Multiobjective Optimization Approach

et al. 2020

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“…Similar to LAMs, in concept, Artificial Intelligence is expected to become more widely used in aids to decision making in Command-and-Control (C2) platforms. Again, because Space War is the battlefield is becoming increasingly complex, commanders are realizing that the human brain can no longer keep track of thousands or variables changing each second, here the military is utilizing game-theoretic models and Machine Learning with a systems perspective to effectively analyse complex systems for C2 [47]. Here the issue is that there is considerable mental fatigue and stress, however AI never gets tired and is always very alert [48].…”

Section: The Us Military Braces For Artificial Intelligence War In Spacementioning

confidence: 99%

Chinese & Iranian Artificial Intelligence in Low Earth Orbit to THAAD Space Wars

Lewis

2019

5th International Conference on Computer Science and Information Technology (CSTY 2019)

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This paper addresses what the role of artificial intelligence will be in low earth orbit (LEO) and space war is and specifically, what China and Iran's latest research in artificial intelligence on unmanned aerial vehicles (UAVs) for LEO space war domains has been, is, and strives to become. The author first presents testimony from scholars and space research scientists from many countries who all categorically state, without a trace of doubt, that all future space warfare will be in satellites and unmanned UAVs and how they in turn will rely heavily on artificial intelligence. This paper includes an analysis on China's strengths in space artificial intelligence and, Iran's systematic approach or arming its UAVs. The second portion of this research drills down into what are the specific mathematical theoretical research areas of artificial research for LEO space wars in various countries, including China. The author concludes with research strategies that will combat China's dominance of space wars.

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“…Current constructs include the system design as a cyberphysical system (CPS), as well as the devices operating at the edge to include the internet of things (IoT), that require consideration to mitigate cyber vulnerabilities. The continued use of unmanned aerial systems (UAS) increases the need for cyber awareness [28], [50], [70] for aeronautical informatics [33].…”

Section: Introductionmentioning

confidence: 99%

Blockchain Methods for Trusted Avionics Systems

Blasch

Chen

et al. 2019

2019 IEEE National Aerospace and Electronics Conference (NAECON)

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Blockchain is a popular method to ensure security for trusted systems. The benefits include an auditable method to provide decentralized security without a trusted third party, but the drawback is the large computational resources needed to process and store the ever-expanding chain of security blocks. The promise of blockchain for edge devices (e.g., internet of things) poses a variety of challenges and strategies before adoption. In this paper, we explore blockchain methods and examples, with experimental data to determine the merits of the capabilities. As for an aerospace example, we address a notional example for Automatic dependent surveillancebroadcast (ADSB) from Flight24 data (https://www.flightradar24.com/) to determine whether blockchain is feasible for avionics systems. The methods are incorporated into the Lightweight Internet of Things (IoT) based Smart Public Safety (LISPS) framework. By decoupling a complex system into independent sub-tasks, the LISPS system possesses high flexibility in the design process and online maintenance. The Blockchain-enabled decentralized avionics services provide a secured data sharing and access control mechanism. The experimental results demonstrate the feasibility of the approach.

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Enabling Autonomy in Command and Control via Game-Theoretic Models and Machine Learning with a Systems Perspective

Cited by 14 publications

References 12 publications

A Generic WebLab Control Tuning Experience Using the Ball and Beam Process and Multiobjective Optimization Approach

A Generic WebLab Control Tuning Experience Using the Ball and Beam Process and Multiobjective Optimization Approach

Chinese & Iranian Artificial Intelligence in Low Earth Orbit to THAAD Space Wars

Blockchain Methods for Trusted Avionics Systems

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