Design of rules for in-flight non-parametric tuning of PID controllers for unmanned aerial vehicles

Chehadeh, Mohamad; Boiko, Igor

doi:10.1016/j.jfranklin.2018.10.015

Cited by 34 publications

(40 citation statements)

References 4 publications

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“…For completeness, we summarize the steps to obtain optimal non-parametric tuning rules as follows [27], [26], [41]:…”

Section: Methodology a Finding The Distinguishing Phasementioning

confidence: 99%

“…which includes a wide variety of multirotor UAV designs and sizes from racing quadrotors to larger multirotor UAVs having a takeoff weight of up to 50Kg. The selection of these parameter ranges was based both on experimental results and parameters from literature like the ones provided in [42], [27], [39], [40], in addition to modeling equations like those discussed in [27], [39], [38], [43]. The selection of parameter bounds can be safely expanded to include UAV designs beyond the specified ranges as they can be handled by the approach presented in this section.…”

Section: Methodology a Finding The Distinguishing Phasementioning

confidence: 99%

“…Section III describes the comprehensive identification approach proposed in this paper. In section IV, simulation and experimental results for the suggested approach are presented, discussed and compared against PEM based system identification and the non-parametric tuning rules of [27]. Finally, Section V summarizes the findings of this paper and provides concluding remarks.…”

Section: Systemmentioning

confidence: 98%

“…Inner system states are considered to be unobservable. We wish to find the mapping Γ : X →D whereD is a discretization of the subspace D. In this work, we limit the order of the LTI system to SOIPTD, which corresponds to multirotor attitude and altitude dynamics as presented in [27]:…”

Section: Problem Statementmentioning

confidence: 99%

“…In all these methods, the knowledge of the PUT response to a single excited frequency is sufficient to tune controller parameters. It was shown in recent work [27] that a near-optimal controller for quadcopter attitude dynamics can be designed using MRFT based tuning rules. However, non-parametric methods in the literature are limited to PID tuning and do not provide full insight into the system dynamics as they cannot be used to obtain model parameters.…”

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confidence: 99%

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Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

et al. 2020

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System identification is a key discipline within the field of automation that deals with inferring mathematical models of dynamic systems based on input-output measurements. Conventional identification methods require extensive data generation and are thus not suitable for real-time applications. In this paper, a novel real-time approach for the parametric identification of linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT) is proposed. The proposed approach requires only a single steady-state cycle of MRFT, and guarantees stability and performance in the identification and control phases. The MRFT output is passed to a trained DL model that identifies the underlying process parameters in milliseconds. A novel modification to the Softmax function is derived to better conform the DL model for the process identification task. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented approach. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.

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“…For completeness, we summarize the steps to obtain optimal non-parametric tuning rules as follows [27], [26], [41]:…”

Section: Methodology a Finding The Distinguishing Phasementioning

confidence: 99%