Adaptive Estimation for Control of Uncertain Nonlinear Systems with Applications to Target Intercept

Madyastha, Venkatesh; Calise, Anthony J.

doi:10.2514/6.2006-6699

Cited by 4 publications

(9 citation statements)

References 32 publications

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“…The relationship between the discrete model noise matrix Q k À 1 and the continuous model noise matrix Q(t) is given by Eq. (353) in Madyastha (2005).…”

Section: Problem Formulationmentioning

confidence: 96%

“…(1) and (2) under Assumption (i), it can be obtained that (see Eqs. (343) and (344) of Madyastha (2005) for details)…”

Section: The State Model For the Implementation Of Apn Guidance Lawmentioning

confidence: 99%

“…Finally, the output signal a L will be fed into the EKF-UI-WDF block as the known control input u together with the measurements from the missile-target engagement system. The true parameters and initial values for simulation of the closed-loop missile-target interception control system are given by ( unknown inputs is chosen to be 0.05 s. The random maneuver case described in Madyastha (2005) will be discussed. The target acceleration a T is implemented as the first order Markov process driven by a random square wave d(t):…”

Section: Simulation Validationmentioning

confidence: 99%

“…1 (i.e., Fig. 60 of Madyastha, 2005). The real dynamics of the interception scenario are described by the following differential equations (for simplicity, the term ''(t)'' is dropped for the timedependent variables in this section):…”

Section: Article In Pressmentioning

confidence: 99%

“…The traditional extended Kalman filter (EKF) or other nonlinear filtering approaches (Shkolnikov, Shtessel, Zarchan, & Lianos, 2001;Zhou et al, 1999) are usually used to estimate the closing velocity and the line-of-sight (LOS) rate for the PN guidance law in the homing interceptor guidance loop. However, when the target acceleration with unknown dynamics is treated as a state in the nonlinear missile-target engagement system, unless subjective assumptions about its kinetics are used to build up the model equation (for instance, a constant maneuver with known amplitude (Shkolnikov et al, 2001), a first order Markov process with estimated time constant (Madyastha, 2005), etc. ), the regular nonlinear filtering approaches used for the PN guidance law are not possible.…”

Section: Introductionmentioning

confidence: 99%

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Applying a novel extended Kalman filter to missile–target interception with APN guidance law: A benchmark case study

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“…The relationship between the discrete model noise matrix Q k À 1 and the continuous model noise matrix Q(t) is given by Eq. (353) in Madyastha (2005).…”

Section: Problem Formulationmentioning

confidence: 96%

“…(1) and (2) under Assumption (i), it can be obtained that (see Eqs. (343) and (344) of Madyastha (2005) for details)…”

Section: The State Model For the Implementation Of Apn Guidance Lawmentioning

confidence: 99%

Section: Simulation Validationmentioning

confidence: 99%

Section: Article In Pressmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applying a novel extended Kalman filter to missile–target interception with APN guidance law: A benchmark case study

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Estimation of the corvette SIGMA motion in missile firing mission

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et al. 2017

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Research on RBV Control Strategy of Large Angle Maneuver

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Yu³

et al. 2014

Abstract and Applied Analysis

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Considering the hypersonic aerospace vehicle, with high dynamic, strong varying parameters, strong nonlinear, strong coupling, and the complicated flight environment, conventional flight control methods based on linear system may become invalid. To the high precision and reliable control problem of this vehicle, nonlinear flight control strategy based on neural network robust adaptive dynamic inversion is proposed. Firstly, considering the nonlinear characteristics of aerodynamic coefficients varying with Mach numbers, attack angle, and sideslip angle, the complete nonlinear 6-DOF model of RBV is established. Secondly, based on the time-scale separation, using the nonlinear dynamic inversion control strategy achieves the pseudolinear decoupling of RBV. And then, using the neural network with single hidden layer approximates the dynamic inversion error for system model uncertainty. Next, the external disturbance and network approximating error are suppressed by robust adaptive control. Finally, using Lyapunov’s theory proves that all error signals of closed loop system are uniformly bounded finally under this control strategy. Nonlinear simulation verifies the feasibility and validity of this control strategy to the RBV control system.

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Adaptive Estimation for Control of Uncertain Nonlinear Systems with Applications to Target Intercept

Cited by 4 publications

References 32 publications

Applying a novel extended Kalman filter to missile–target interception with APN guidance law: A benchmark case study

Applying a novel extended Kalman filter to missile–target interception with APN guidance law: A benchmark case study

Estimation of the corvette SIGMA motion in missile firing mission

Research on RBV Control Strategy of Large Angle Maneuver

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