Adaptive MPC Using a Dual Fast Orthogonal Kalman Filter: Application to Quadcopter Altitude Control

Jardine, Peter T.; Givigi, Sidney N.; Yousefi, Shahram; Korenberg, Michael J.

doi:10.1109/jsyst.2017.2774819

Cited by 11 publications

(7 citation statements)

References 20 publications

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“…In most applications on attitude and altitude stabilization of the practical quadrotor UAVs, conventional feedback control methods are employed. [4][5][6][7][8] These methods are easy to design and realize control algorithm, but do not provide sufficient robustness to external atmospheric disturbances and parameter variations. In order to overcome this, traditional control plus DOB method is used to improve disturbance suppression performance.…”

Section: Robust Attitude Control Using Dobmentioning

confidence: 99%

“…Quadrotor UAVs have been one of the most popular research platforms in aspects of unmanned aerial vehicles (UAV) in recent years. [1][2][3][4][5][6] The most important thing in the flight control of such aerial vehicle is attitude control because all the flight operations such as hovering and trajectory tracking can be performed based on it. On the other hand, in the attitude control, there arise the requirements of providing the control performance with rapid response and sufficient stability margins as well as overcoming the influence of external disturbance, delay of digital processing and uncertainty of physical parameter variation.…”

Section: Introductionmentioning

confidence: 99%

“…For the attitude stabilization control, the linear control methods such as PID controller 7,8 and LQ state feedback regulator 9 are employed. Also, nonlinear control methods such as feedback linearization, 10 sliding mode control, 11 back-stepping 12 and adaptive control 13 are widely used to attenuate uncertainties of model and disturbances, while fault detection and tolerance methods for guaranteeing safety flight of quadrotor 14 and attitude control method based on model predictive control 6 are also studied. On the other hand, the control method using disturbance observer (DOB) has also been widely used, because it can enhance robustness to parameter variations and disturbances by compensating the lumped equivalent disturbance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust attitude control of a quadrotor unmanned aerial vehicles using optimized disturbance observer

Kim¹,

Paek²,

Yong‐Sim³

et al. 2022

Optim Control Appl Methods

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This article proposes a method of realizing robust attitude stabilization control of a quadrotor UAV using the disturbance observer (DOB), where Q-filter is optimally designed considering performances of attenuating disturbance and estimation noise, and robust stability against parameter variations and sampling duration delay due to discrete time control in microcontroller. Disturbance rejection performance mainly depends on the bandwidth determined by cut-off frequency of DOB's Q-filter. However, extension of the Q-filter's bandwidth must be restricted due to the requirement of robust stability against parameter uncertainty and sampling duration delay in onboard microcontroller for flight control of quadrotor. In order to optimize the disturbance rejection performance under the restriction of bandwidth, an H ∞ norm minimization problem is defined considering the frequency bands of disturbance and noise. The optimization problem is transformed into standard H ∞ control design problem by pseudo loop factorization method so as to resolve the structural constraint of Q-filter. Then, stability of the discrete time system configured by digital controller is tested in relation with the time constant of the designed DOB's Q-filter under different inertia moment values. The comparisons between simulation results by different control methods validate that the robust control method using proposed DOB provides a better performance of disturbance attenuation than that using conventional DOB.

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Section: Robust Attitude Control Using Dobmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust attitude control of a quadrotor unmanned aerial vehicles using optimized disturbance observer

Kim¹,

Paek²,

Yong‐Sim³

et al. 2022

Optim Control Appl Methods

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show abstract

“…It is mostly used for basic functions such as attitude and position control, while the MPC is used to optimize the control parameters by predicting the future state of the UAV. The MPC is deployed with both basic functions (e.g., precision Dentler et al, 2016 ; Feng et al, 2018 ; Jardine et al, 2019 , environmental disturbance compensation Chikasha and Dube, 2017 ), and advanced functions (e.g., obstacle avoidance and navigation, Bareiss et al, 2017 ; Yang et al, 2019 ; Dai et al, 2020 ). In addition, it is used in trajectory tracking to control the UAV, ensuring that it follows the planned path (Baca et al, 2018 ; Tordesillas et al, 2019 ; Invernizzi et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Neural Control and Online Learning for Speed Adaptation of Unmanned Aerial Vehicles

Jaiton

Rothomphiwat

Ebeid

et al. 2022

Front. Neural Circuits

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Unmanned aerial vehicles (UAVs) are involved in critical tasks such as inspection and exploration. Thus, they have to perform several intelligent functions. Various control approaches have been proposed to implement these functions. Most classical UAV control approaches, such as model predictive control, require a dynamic model to determine the optimal control parameters. Other control approaches use machine learning techniques that require multiple learning trials to obtain the proper control parameters. All these approaches are computationally expensive. Our goal is to develop an efficient control system for UAVs that does not require a dynamic model and allows them to learn control parameters online with only a few trials and inexpensive computations. To achieve this, we developed a neural control method with fast online learning. Neural control is based on a three-neuron network, whereas the online learning algorithm is derived from a neural correlation-based learning principle with predictive and reflexive sensory information. This neural control technique is used here for the speed adaptation of the UAV. The control technique relies on a simple input signal from a compact optical distance measurement sensor that can be converted into predictive and reflexive sensory information for the learning algorithm. Such speed adaptation is a fundamental function that can be used as part of other complex control functions, such as obstacle avoidance. The proposed technique was implemented on a real UAV system. Consequently, the UAV can quickly learn within 3–4 trials to proactively adapt its flying speed to brake at a safe distance from the obstacle or target in the horizontal and vertical planes. This speed adaptation is also robust against wind perturbation. We also demonstrated a combination of speed adaptation and obstacle avoidance for UAV navigations, which is an important intelligent function toward inspection and exploration.

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“…A solution is to apply adaptive MPC, which relays on parameter estimation algorithms, such as Kalman filter observer. Jardine et al 16 has already accomplished the combination of dual Kalman filter and adaptive MPC on quadcopter altitude controller.…”

Section: Introductionmentioning

confidence: 99%

Mode-switch model predictive controller with “pre-contact” method for alleviating driveline vibration of electric vehicles considering backlash

Chai

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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The backlash between engaging components in a driveline is inevitable and contributes to the nonlinearity of the driveline. The existing motor controllers of an electric vehicle usually ignore the backlash, which often brings impacts and vibration. This paper proposes an active driveline vibration controller for electric vehicles. A nonlinear driveline model considering backlash and wheel slip ratio is established in MATLAB/Simulink, and the results of bench test proved that the model could effectively reflect the transient dynamics of the electric driveline. Based on this model, a dual extended Kalman filter observer is designed to estimate both the system state variables and vehicle mass, which are essential information for the controller design. Then, a mode-switch model predictive controller based on two linearized models is proposed to alleviate the impacts and vibration caused by the transient change of motor torque. The proposed controller would identify whether the driveline is operating in “contact mode” or “backlash mode” and thus generates an optimal motor torque by solving a Quadratic Programing. Note that the control targets and model structures in two modes are different. Furthermore, a “pre-contact” method is proposed as an additional part to handle the condition when motor command torque is zero. Simulation results demonstrate that the proposed controller can effectively alleviate the impacts and vibration in the electric driveline while keeping the torque delay negligible. Moreover, the robustness of the proposed controller against estimation errors and system noises are discussed.

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Adaptive MPC Using a Dual Fast Orthogonal Kalman Filter: Application to Quadcopter Altitude Control

Cited by 11 publications

References 20 publications

Robust attitude control of a quadrotor unmanned aerial vehicles using optimized disturbance observer

Robust attitude control of a quadrotor unmanned aerial vehicles using optimized disturbance observer

Neural Control and Online Learning for Speed Adaptation of Unmanned Aerial Vehicles

Mode-switch model predictive controller with “pre-contact” method for alleviating driveline vibration of electric vehicles considering backlash

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