Intelligent Control: A Taxonomy

Wilson, Callum; Marchetti, Francesco; Carlo, Marilena Di; Riccardi, Annalisa; Minisci, Edmondo

doi:10.1109/icsc47195.2019.8950603

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…According to [39], machine learning techniques offer potential solutions to develop a versatile controller for a UAV with variable parameters in its environment. Ref.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

et al. 2023

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In recent years, multi-rotor UAVs have become valuable tools in several productive fields, from entertainment to agriculture and security. However, during their flight trajectory, they sometimes do not accurately perform a specific set of tasks, and the implementation of flight controllers in these vehicles is required to achieve a successful performance. Therefore, this research describes the design of a flight position controller based on Deep Neural Networks and subsequent implementation for a multi-rotor UAV. Five promising Neural Network architectures are developed based on a thorough literature review, incorporating LSTM, 1-D convolutional, pooling, and fully-connected layers. A dataset is then constructed using the performance data of a PID flight controller, encompassing diverse trajectories with transient and steady-state information such as position, speed, acceleration, and motor output signals. The tuning of hyperparameters for each type of architecture is performed by applying the Hyperband algorithm. The best model obtained (LSTMCNN) consists of a combination of LSTM and CNN layers in one dimension. This architecture is compared with the PID flight controller in different scenarios employing evaluation metrics such as rise time, overshoot, steady-state error, and control effort. The findings reveal that our best models demonstrate the successful generalization of flight control tasks. While our best model is able to work with a wider operational range than the PID controller and offers step responses in the Y and X axis with 97% and 98% similarity, respectively, within the PID’s operational range. This outcome opens up possibilities for efficient online training of flight controllers based on Neural Networks, enabling the development of adaptable controllers tailored to specific application domains.

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“…According to [39], machine learning techniques offer potential solutions to develop a versatile controller for a UAV with variable parameters in its environment. Ref.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

et al. 2023

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“…Such definition comes from both the work of Saridis [11] and Antsaklis [12], and can be summarized as follows: a controller can be defined intelligent if it can deal autonomously with unforeseen changes in the environment, in the control system or in the goals, by relaying on techniques pertaining to the fields of Artificial Intelligence, Operations Research and Automatic Control. According to this definition the authors also proposed a taxonomy [13], and according to such taxonomy the IC approach presented in this paper classifies as E3 C4 G0, which means that the proposed controller can autonomously deal with an Underlying physics of environment not well defined (E3), No known controller structure (C4), and with Goals entirely predetermined by designer (G0) at design time.…”

Section: Intelligent Controlmentioning

confidence: 99%

Towards Intelligent Control via Genetic Programming

Marchetti

Minisci

Riccardi

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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In this paper an initial approach to Intelligent Control (IC) using Genetic Programming (GP) for access to space applications is presented. GP can be employed successfully to design a controller even for complex systems, where classical controllers fail because of the high nonlinearity of the systems. The main property of GP, that is its ability to autonomously create explicit mathematical equations starting from a very poor knowledge of the considered plant, or just data, can be exploited for a vast range of applications. Here, GP has been used to design the control law in an Intelligent Control framework for a modified version of the Goddard Rocket problem in 3 different failure scenarios, where the approach to IC consists in an online re-evaluation of the control law using GP when a considerably big change in the environment or in the plant happens. The presented results are then used to highlight the potential benefits of the method, as well as aspects that will need further developments.

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“…IC methods are designed to deal with substantial uncertainties where traditional methods cannot. Combining the fields of automatic control, artificial intelligence, and operational research (Saridis 1979), these methods can adapt online using Artificial Intelligence (AI) techniques such as fuzzy logic, machine learning, and evolutionary computing (Wilson et al 2019). There are also classes of control methods which use AI methods that learn how to control a system offline and do not update in deployment.…”

Section: Introductionmentioning

confidence: 99%

Improving the efficiency of reinforcement learning for a spacecraft powered descent with Q-learning

Wilson

Riccardi

2021

Optim Eng

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Reinforcement learning entails many intuitive and useful approaches to solving various problems. Its main premise is to learn how to complete tasks by interacting with the environment and observing which actions are more optimal with respect to a reward signal. Methods from reinforcement learning have long been applied in aerospace and have more recently seen renewed interest in space applications. Problems in spacecraft control can benefit from the use of intelligent techniques when faced with significant uncertainties—as is common for space environments. Solving these control problems using reinforcement learning remains a challenge partly due to long training times and sensitivity in performance to hyperparameters which require careful tuning. In this work we seek to address both issues for a sample spacecraft control problem. To reduce training times compared to other approaches, we simplify the problem by discretising the action space and use a data-efficient algorithm to train the agent. Furthermore, we employ an automated approach to hyperparameter selection which optimises for a specified performance metric. Our approach is tested on a 3-DOF powered descent problem with uncertainties in the initial conditions. We run experiments with two different problem formulations—using a ‘shaped’ state representation to guide the agent and also a ‘raw’ state representation with unprocessed values of position, velocity and mass. The results show that an agent can learn a near-optimal policy efficiently by appropriately defining the action-space and state-space. Using the raw state representation led to ‘reward-hacking’ and poor performance, which highlights the importance of the problem and state-space formulation in successfully training reinforcement learning agents. In addition, we show that the optimal hyperparameters can vary significantly based on the choice of loss function. Using two sets of hyperparameters optimised for different loss functions, we demonstrate that in both cases the agent can find near-optimal policies with comparable performance to previously applied methods.

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Intelligent Control: A Taxonomy

Cited by 3 publications

References 45 publications

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

Towards Intelligent Control via Genetic Programming

Improving the efficiency of reinforcement learning for a spacecraft powered descent with Q-learning

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