Adaptive Backstepping Control of a 2-DOF Helicopter

Schlanbusch, Siri Marte; Zhou, Jing

doi:10.1109/iccma46720.2019.8988761

Cited by 7 publications

(9 citation statements)

References 10 publications

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“…In this paper we extend the results from [22] and [14], where the adaptive backstepping control of a 2-DOF helicopter was considered in [22] and with input quantization in [14], to now deal with both input and state quantization for the same helicopter system. The helicopter is a nonlinear multiple-input and multiple-output (MIMO) system, with challenges in controller design due to its nonlinear behavior, its cross coupling effect between inputs and outputs, and with uncertainties both in the model and the parameters.…”

Section: D1 Introductionmentioning

confidence: 89%

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Adaptive Backstepping Control of a 2-DOF Helicopter System in the Presence of Quantization

Schlanbusch

Zhou

2021

2021 9th International Conference on Control, Mechatronics and Automation (ICCMA)

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This paper studies the attitude tracking control for an uncertain 2-degrees of freedom helicopter system where the inputs and the states are quantized. An adaptive backstepping based control scheme is proposed to handle the effect of quantization for tracking of reference angles for pitch and yaw. All closed-loop signals are ensured uniformly bounded and the tracking errors will converge to a compact set containing the origin. Experiments on the helicopter system illustrate the proposed control scheme.Paper D. Adaptive Backstepping Control of a 2-DOF Helicopter System in the Presence of Quantization utilize quantized states, and this problem is being addressed.• The attitude, i.e. orientation, of a MIMO 2-DOF helicopter system is to be controlled, where the system has challenges due to uncertain parameters, there is a coupling between the inputs and the outputs that makes control more complicated, and quantization of both the inputs and the states introduce errors that need to be handled in the control design and in the stability analysis.We propose an adaptive control algorithm using the backstepping technique to deal with these problems.The paper is organized as follows. In Section D.2, the system model, problem statement and the considered quantizer are presented. Section D.3 presents the adaptive control design based on backstepping technique. In Section D.4 a stability analysis is given, Section D.5 presents the results from experiment before a conclusion is given in Section D.6. D.2 Dynamical Model and Problem Formulation D.2.1 NotationsVectors are denoted by small bold letters and matrices with capitalized bold letters. λ max (•) and λ min (•) denotes the maximum and minimum eigenvalue of the matrix (•), and ∥•∥ denotes the L 2 -norm and induced L 2 -norm for vectors and matrices, respectively.

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Section: D1 Introductionmentioning

confidence: 89%

“…Thus, this is a MIMO system with 2 DOF, where each input will change both the pitch and the yaw angle. The helicopter model is considered as a rigid body and the equations of motion are derived using Euler-Lagrange equations as given in [22], where the system parameters are uncertain. The state variables are defined as…”

Section: D22 System Modelmentioning

confidence: 99%

Adaptive Backstepping Control of a 2-DOF Helicopter System in the Presence of Quantization

Schlanbusch

Zhou

2021

2021 9th International Conference on Control, Mechatronics and Automation (ICCMA)

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“…In the existing nonlinear control results dealing with the 2-DOF helicopter system [38][39][40][41], the input saturation problem was not considered even though input saturation practically occurs in the 2-DOF helicopter system (34). Besides, the previous results [38][39][40][41] did not consider both the state and input quantization problems and thus cannot be applied to the network-based state-quantized control problem. However, this paper considers the state quantization and the quantized input saturation effects.…”

Section: Design Of Quantized-states-based Adaptive Trackermentioning

confidence: 99%

Approximation-Based Quantized State Feedback Tracking of Uncertain Input-Saturated MIMO Nonlinear Systems with Application to 2-DOF Helicopter

Kim

Yoo

2021

Mathematics

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This paper addresses an approximation-based quantized state feedback tracking problem of multiple-input multiple-output (MIMO) nonlinear systems with quantized input saturation. A uniform quantizer is adopted to quantize state variables and control inputs of MIMO nonlinear systems. The primary features in the current development are that (i) an adaptive neural network tracker using quantized states is developed for MIMO nonlinear systems and (ii) a compensation mechanism of quantized input saturation is designed by constructing an auxiliary system. An adaptive neural tracker design with the compensation of quantized input saturation is developed by deriving an augmented error surface using quantized states. It is shown that closed-loop stability analysis and tracking error convergence are conducted based on Lyapunov theory. Finally, we give simulation and experimental results of the 2-degrees-of-freedom (2-DOF) helicopter system for verifying to the validity of the proposed methodology where the tracking performance of pitch and yaw angles is measured with the mean squared errors of 0.1044 and 0.0435 for simulation results, and those of 0.0656 and 0.0523 for experimental results.

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“…For example, a backstepping controller has been designed by incorporating nested saturation feedback functions for tracking control of helicopters in [14]. In a relevant study [28], to independently track the yaw and pitch position references, a backstepping adaptive nonlinear controller has been designed. The authors in [16] implemented an optimized fractional-order sliding mode controller (SMC) on the 2-DOF Quanser AERO helicopter testbed where they chose the sliding surface in a fractional-order hyperplane in order to reduce the chattering.…”

Section: Introductionmentioning

confidence: 99%

Simple Learning-Based Robust Trajectory Tracking Control of a 2-DOF Helicopter System

2022

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Stabilization and tracking control of Unmanned Aircraft Systems (UASs) such as helicopters in a complex environment with system uncertainties, unknown disturbances, and noise is a challenging task; therefore, to compensate for system uncertainties and unknown disturbances, this paper presents a trajectory tracking control strategy for a 2-DOF (degree of freedom) helicopter system testbed by employing a gradient descent-based simple learning control law that minimizes the cost function corresponding to desired closed-loop error dynamics of the nonlinear system under control. In addition, to ensure the stability of the closed-loop nonlinear system, further analysis is provided. The learning capability of the designed controller makes it suitable to take system uncertainties and unknown disturbances into account. The results of computer simulations and real-time experiment using the Quanser AERO helicopter are included to demonstrate the effectiveness of the designed control strategy.

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Adaptive Backstepping Control of a 2-DOF Helicopter

Cited by 7 publications

References 10 publications

Adaptive Backstepping Control of a 2-DOF Helicopter System in the Presence of Quantization

Adaptive Backstepping Control of a 2-DOF Helicopter System in the Presence of Quantization

Approximation-Based Quantized State Feedback Tracking of Uncertain Input-Saturated MIMO Nonlinear Systems with Application to 2-DOF Helicopter

Simple Learning-Based Robust Trajectory Tracking Control of a 2-DOF Helicopter System

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