Conditional disturbance negation based active disturbance rejection control for hypersonic vehicles

Sun, Jinlin; Pu, Zhiqiang; Yi, Jianqiang

doi:10.1016/j.conengprac.2018.11.016

Cited by 35 publications

(25 citation statements)

References 47 publications

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“…Assuming that the that cruise speed u is a constant andu is zero, the X equation can be exenterated. After that, the linearized equation can be shown as (16).…”

Section: Horizontal Equation Of Motionmentioning

confidence: 99%

“…For the linear-state error feedback with the error combined by linear function, a control variable is transmitted to the controlled system [16].The linear feedback error law is given as:…”

Section: Extended-state Observermentioning

confidence: 99%

“…For the powered parafoil, Luo et al proposed an accurate flight-path-tracking control approach combining ADRC and wind feedforward compensation, which can achieve better tracking performance and robustness against the variable wind disturbance [15]. For flexible air-breathing hypersonic vehicles (FAHVs), a conditional disturbance negation (CDN)-based ADRC scheme was used for velocity and altitude tracking control in the presence of various uncertainties and disturbances [16]. The algorithm combined reinforcement learning with ADRC and was compared with classical ADRC by simulation based on the dynamic model of the OUC-III underwater glider [17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Yin

et al. 2019

Electronics

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The control issue of Autonomous Underwater Vehicles (AUV) is very challenging since the precise mathematical model of AUV is hard to establish due to its strong coupling and time-varying features. Meanwhile, AUV movement is easily interfered with by ocean currents and waves, causing anti-interference performance of traditional Proportional-Integral-Derivative (PID) control to be unsatisfactory. Aiming to solve those problems, an algorithm of fuzzy optimized model-free adaptive control (MFAC) based on auto-disturbance rejection control (ADRC) was proposed and used in AUV heading control. The MFAC is used to overcome the difficulty with establishing a precise mathematical model, and the ADRC is introduced to handle the interference of currents and waves. In this paper, MFAC and ADRC are combined. First, the MFAC is performed based only on the I/O data of the controlled object, which is simple to implement with low calculation complexity and strong robustness. Then, a tracking differentiator (TD) is employed to track the input signal to overcome the antinomy of rapidity and hypertonicity in MFAC. After that, an extended-state observer (ESO) is added to control the variables of MFAC to estimate all the disturbances, which can greatly improve the anti-interference ability of the system. Due to the complexity and diversity of the marine environment, a fuzzy optimized MFAC based on ADRC is proposed to improve the adaptability of AUV to the marine environment. Simulations and experiments were carried out to verify the control effect of this algorithm in complex sea conditions.

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“…Assuming that the that cruise speed u is a constant andu is zero, the X equation can be exenterated. After that, the linearized equation can be shown as (16).…”

Section: Horizontal Equation Of Motionmentioning

confidence: 99%

“…For the linear-state error feedback with the error combined by linear function, a control variable is transmitted to the controlled system [16].The linear feedback error law is given as:…”

Section: Extended-state Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Yin

et al. 2019

Electronics

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show abstract

“…According to the cascade control theory, the lateral-directional control of full-wing UAVs can be divided into the lateral-directional attitude control and the lateral-directional trajectory control. For the former, Han [3] and Gao [4] proposed the theory of active disturbance rejection control, which is a control theory with a strong robust ability and easy engineering realization [5], and it is very suitable for flight control design [6][7][8]. Nonlinear dynamic inversion control (NDI) is widely used in flight control research [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Lateral-Directional Control Method for High Aspect Ratio Full-Wing UAV and Flight Tests

Zhu

Zhou

et al. 2019

Applied Sciences

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To solve the lateral-directional control problem of the high aspect ratio full-wing unmanned aerial vehicle (UAV) without an aileron and rudder, a control method is proposed that uses the differential thrust of propellers as the control output and the yaw angle as the controlled attitude angle. Meanwhile, simulation analysis and experimental verification are carried out. First, a lateral-direction mathematical model and a differential thrust of propeller model of the full-wing drone are established. The influence of the aerodynamic derivative C Y β on the lateral-direction mode is analyzed. Second, based on nonlinear dynamic inversion (NDI) and active disturbance rejection control (ADRC) theories, a yaw angle controller that uses the differential thrust of propellers as the control output is designed. Finally, the vector field (VF) method is improved to obtain the straight-line trajectory tracking method satisfying different speeds, and the logic of waypoint switching is given. The research shows that C Y β has a great influence on the dutch roll damping of the drone. For the full-wing configuration, it is feasible to use the yaw angle as the controlled attitude angle without considering the roll angle. The simulation and experimental results show that the designed lateral-directional control method for the high aspect ratio full-wing UAV has a good control effect and disturbance rejection ability. Meanwhile, the control method has less parameters to adjust and less calculation, which is very suitable for engineering applications.

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“…On the other side, hypersonic aerial vehicles are more difficult to be withheld by aerial defense and protection systems, thus giving also a significant advantage in airforce operations [9][10][11][12][13]. The control of hypersonic aerial vehicles is a non-trivial problem due to the multi-variable and strongly nonlinear model of such aircrafts [14][15][16][17][18]. Besides, such systems are underactuated because of having more degrees of freedom than control inputs, and this imposes additional difficulty in achieving flight stabilization and control [19][20][21][22].…”

mentioning

confidence: 99%

Nonlinear optimal control for autonomous hypersonic vehicles

Rigatos

Wira

Abbaszadeh

et al. 2019

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The article proposes a nonlinear optimal (H-infinity) control method for a hypersonic aerial vehicle (HSV). The dynamic model of the hypersonic vehicle undergoes approximate linearization around a temporary operating point which is recomputed at each iteration of the control method. This operating point consists of the present value of the system's state vector and of the last value of the control inputs vector that was applied on the HSV. The linearization relies on Taylor series expansion and on the computation of the associated Jacobian matrices. For the approximately linearized model of the hypersonic aerial vehicle, an optimal (H-infinity) feedback controller was designed. To compute the controller's feedback gains, an algebraic Riccati equation had to be repetitively solved at each iteration of the control algorithm. The global asymptotic stability of the control method is proven through Lyapunov analysis. The control scheme remains robust against model uncertainties an external perturbations.

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Conditional disturbance negation based active disturbance rejection control for hypersonic vehicles

Cited by 35 publications

References 47 publications

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

A Lateral-Directional Control Method for High Aspect Ratio Full-Wing UAV and Flight Tests

Nonlinear optimal control for autonomous hypersonic vehicles

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