Nonlinear control based on H-infinity theory for autonomous aerial vehicle

Garcia, Gonzalo; Kashmiri, Shawn; Shukla, Daksh

doi:10.1109/icuas.2017.7991395

Cited by 10 publications

(12 citation statements)

References 23 publications

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“…x T (A T K P + P A k )x + x T P Be res + e estėest ≤ x T P Be res + e estėest = 0 (13) becauseė est =ḋ under the assumption thatḋ = 0; e est = −e res and from equation (11). The equilibrium states are x = 0 and e est = 0.V (t) is always negative for all the states except for the equilibrium states.…”

Section: Novel Dob-based Control a Novel Basic Dobmentioning

confidence: 97%

“…Designing a DOB-based controller is conceptually easy since the disturbance decoupling process is different from nominal control processes in that they can be designed by assuming that the disturbance is completely decoupled; this is done by using the estimated disturbance obtained from the DOB [6]- [8]. This implies that the DOB can be used with any other control method, such as sliding mode control (SMC) or H ∞ control [9]- [13], to improve its robustness by enabling the direct cancellation of the disturbance [14] [15]. Achieving robust performance mainly depends on the estimation performance of the DOB.…”

Section: Introductionmentioning

confidence: 99%

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State Space Disturbance Observer Considering Residual Disturbance

Park

Yoon

2020

IEEE Access

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This paper presents a novel disturbance observer (DOB) for overcoming the limitation of uniformly bounded stability in existing DOB-based control systems and to attain asymptotic stability. The DOB is derived by considering the residual disturbance remaining after disturbance decoupling is performed. It focuses on the elimination of the residual disturbance as well as minimization of the estimation error. By considering the system state and the residual disturbance simultaneously in a Lyapunov stability function, the proposed DOB cancels the residual disturbance term and attains asymptotic stability. In contrast, existing DOBs consider these factors separately in different Lyapunov functions. A basic novel DOB is initially derived for achieving asymptotic stability and is later combined with the existing DOB. This forms a novel extended DOB that achieves a better estimation rate and better performance with regard to DOB-based control than those of existing DOBs.

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Section: Novel Dob-based Control a Novel Basic Dobmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

State Space Disturbance Observer Considering Residual Disturbance

Park

Yoon

2020

IEEE Access

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“…A flexible spacecraft body consists of a rigid hub and flexible appendages. In the literature, the spacecraft hub attitude angles are often selected as the attitude dynamic model's output [4,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. In this approach, the flexible appendages' vibration effect is considered as an unknown vibrational disturbance torque and the attitude controller should overcome, in addition to the model uncertainties and the environmental disturbances.…”

Section: Introductionmentioning

confidence: 99%

A novel output redefinition method for nonlinear H_∞attitude stabilization of a flexible spacecraft

Majid Esmaeilzadeh,

Sadegh Zeyghami

2023

Asian Journal of Control

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This paper addresses a novel output redefinition method for the nonlinear attitude stabilization problem of a flexible spacecraft. As an idea, the flexible appendage tip‐point can be selected as attitude output which may improve attitude pointing accuracy. However, it makes the system non‐minimum phase. The output redefinition method is an effective method to make the system minimum phase. In this paper, a novel output redefinition is proposed to overcome the non‐minimum phase property such that it improves the attitude control system performance. Then, a nonlinear controller is designed based on back‐stepping approach and a modal observer. The simulation results show the effectiveness of the proposed method in the presence of model uncertainties and environmental disturbances.

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“…This motivates the use of state-of-the-art nonlinear control algorithms. Examples of nonlinear control methods applied to UAVs include gain scheduling [3], linear parameter varying (LPV) control [4], dynamic inversion (feedback linearization) [5], adaptive backstepping [6], sliding mode control [7], nonlinear model predictive control [8], nonlinear H-infinity control [9], dynamic inversion combined with mu-synthesis [10], model reference adaptive control [11] and L1 adaptive control [12]. Automated agile and aerobatic maneuvering is treated in [13] and [14].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy optimization

Bøhn

Coates

Moe

et al. 2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

154

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Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far more limited in their flight envelope as compared to experienced human pilots, thereby restricting the conditions UAVs can operate in and the types of missions they can accomplish autonomously. This paper proposes a deep reinforcement learning (DRL) controller to handle the nonlinear attitude control problem, enabling extended flight envelopes for fixed-wing UAVs. A proof-of-concept controller using the proximal policy optimization (PPO) algorithm is developed, and is shown to be capable of stabilizing a fixed-wing UAV from a large set of initial conditions to reference roll, pitch and airspeed values. The training process is outlined and key factors for its progression rate are considered, with the most important factor found to be limiting the number of variables in the observation vector, and including values for several previous time steps for these variables. The trained reinforcement learning (RL) controller is compared to a proportional-integralderivative (PID) controller, and is found to converge in more cases than the PID controller, with comparable performance. Furthermore, the RL controller is shown to generalize well to unseen disturbances in the form of wind and turbulence, even in severe disturbance conditions.

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Nonlinear control based on H-infinity theory for autonomous aerial vehicle

Cited by 10 publications

References 23 publications

State Space Disturbance Observer Considering Residual Disturbance

State Space Disturbance Observer Considering Residual Disturbance

A novel output redefinition method for nonlinear H_∞attitude stabilization of a flexible spacecraft

Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy optimization

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Nonlinear control based on H-infinity theory for autonomous aerial vehicle

Cited by 10 publications

References 23 publications

State Space Disturbance Observer Considering Residual Disturbance

State Space Disturbance Observer Considering Residual Disturbance

A novel output redefinition method for nonlinear H∞attitude stabilization of a flexible spacecraft

Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy optimization

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Product

Resources

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A novel output redefinition method for nonlinear H_∞attitude stabilization of a flexible spacecraft