Model-free control algorithms for micro air vehicles with transitioning flight capabilities

International Journal of Micro Air Vehicles ◽

10.1177/1756829320914264 ◽

2020 ◽

Vol 12 ◽

pp. 175682932091426 ◽

Cited By ~ 1

Author(s):

Jacson MO Barth ◽

Jean-Philippe Condomines ◽

Murat Bronz ◽

Jean-Marc Moschetta ◽

Cédric Join ◽

...

Keyword(s):

Micro Air Vehicles ◽

Control Algorithms ◽

Model Free ◽

Model Free Control ◽

Air Vehicles

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Towards a Unified Model-Free Control Architecture for Tailsitter Micro Air Vehicles: Flight Simulation Analysis and Experimental Flights

AIAA Scitech 2020 Forum ◽

10.2514/6.2020-2075 ◽

2020 ◽

Author(s):

Jacson M. Olszanecki Barth ◽

Jean-Philippe Condomines ◽

Murat Bronz ◽

Gautier Hattenberger ◽

Jean-Marc Moschetta ◽

...

Keyword(s):

Simulation Analysis ◽

Micro Air Vehicles ◽

Unified Model ◽

Flight Simulation ◽

Control Architecture ◽

Model Free ◽

Model Free Control ◽

Air Vehicles

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Model-Free Optimized Tracking Control Heuristic

Robotics ◽

10.3390/robotics9030049 ◽

2020 ◽

Vol 9 (3) ◽

pp. 49 ◽

Cited By ~ 1

Author(s):

Ning Wang ◽

Mohammed Abouheaf ◽

Wail Gueaieb ◽

Nabil Nahas

Keyword(s):

Tracking Control ◽

Tracking Error ◽

Reference Signal ◽

Control Algorithms ◽

Learning Tools ◽

Control Effort ◽

Control Loops ◽

Model Free ◽

Architectural Framework ◽

Model Free Control

Many tracking control solutions proposed in the literature rely on various forms of tracking error signals at the expense of possibly overlooking other dynamic criteria, such as optimizing the control effort, overshoot, and settling time, for example. In this article, a model-free control architectural framework is presented to track reference signals while optimizing other criteria as per the designer’s preference. The control architecture is model-free in the sense that the plant’s dynamics do not have to be known in advance. To this end, we propose and compare four tracking control algorithms which synergistically integrate a few machine learning tools to compromise between tracking a reference signal and optimizing a user-defined dynamic cost function. This is accomplished via two orchestrated control loops, one for tracking and one for optimization. Two control algorithms are designed and compared for the tracking loop. The first is based on reinforcement learning while the second is based on nonlinear threshold accepting technique. The optimization control loop is implemented using an artificial neural network. Each controller is trained offline before being integrated in the aggregate control system. Simulation results of three scenarios with various complexities demonstrated the effectiveness of the proposed control schemes in forcing the tracking error to converge while minimizing a pre-defined system-wide objective function.

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Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

Machines ◽

10.3390/machines5040025 ◽

2017 ◽

Vol 5 (4) ◽

pp. 25 ◽

Cited By ~ 11

Author(s):

Raul-Cristian Roman ◽

Mircea-Bogdan Radac ◽

Radu-Emil Precup ◽

Emil Petriu

Keyword(s):

Control Algorithms ◽

Virtual Reference ◽

Servo Systems ◽

Model Free ◽

Model Free Control

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Data-Driven Model-Free Sliding Mode and Fuzzy Control with Experimental Validation

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2021.1.4076 ◽

2020 ◽

Vol 16 (1) ◽

Author(s):

Radu-Emil Precup ◽

Raul-Cristian Roman ◽

Elena-Lorena Hedrea ◽

Emil M. Petriu ◽

Claudia-Adina Bojan-Dragos

Keyword(s):

Fuzzy Control ◽

Sliding Mode Control ◽

Sliding Mode ◽

Data Driven ◽

Control Algorithms ◽

Control Law ◽

Control Structures ◽

Mode Control ◽

Model Free ◽

Model Free Control

The paper presents the combination of the model-free control technique with two popular nonlinear control techniques, sliding mode control and fuzzy control. Two data-driven model-free sliding mode control structures and one data-driven model-free fuzzy control structure are given. The data-driven model-free sliding mode control structures are built upon a model-free intelligent Proportional-Integral (iPI) control system structure, where an augmented control signal is inserted in the iPI control law to deal with the error dynamics in terms of sliding mode control. The data-driven model-free fuzzy control structure is developed by fuzzifying the PI component of the continuous-time iPI control law. The design approaches of the data-driven model-free control algorithms are offered. The data-driven model-free control algorithms are validated as controllers by real-time experiments conducted on 3D crane system laboratory equipment.

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