Advances In Missile Guidance Control And Estimation.pdf
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How Advances In Missile Guidance Control And Estimation Can Improve Weapon Systems Performance
Missile guidance, control, and estimation are the three major components that determine the accuracy, cost, and reliability of guided weapon systems. These components are also subject to stringent demands and challenges in modern warfare scenarios, such as counter-measures, low-observable targets, and complex environments. Therefore, there is a need for new approaches and techniques to enhance the missile design process and achieve better performance.
In this article, we will review some of the latest developments in missile guidance, control, and estimation, as well as their practical implementation issues. We will also discuss how these advances can contribute to the overall missile design process and improve weapon systems performance. This article is based on the book Advances in Missile Guidance, Control, and Estimation, edited by S.N. Balakrishnan, A. Tsourdos, and B.A. White [^1^].
Missile Guidance
Missile guidance is the process of generating commands to steer the missile towards the target while satisfying certain constraints and objectives. There are various techniques applied to the missile guidance problem, such as classical guidance, sliding mode-based guidance, and differential game-based guidance.
Classical guidance techniques are based on simple geometric or kinematic relationships between the missile and the target, such as proportional navigation or augmented proportional navigation. These techniques are easy to implement and robust to uncertainties, but they may not be optimal or adaptive to different scenarios.
Sliding mode-based guidance techniques are based on nonlinear control theory and use a switching logic to drive the missile to a desired sliding surface. These techniques can achieve high accuracy and robustness to uncertainties and disturbances, but they may suffer from chattering effects or require high control effort.
Differential game-based guidance techniques are based on optimal control theory and use a game-theoretic framework to model the interaction between the missile and the target. These techniques can achieve optimal or near-optimal performance and account for the target maneuverability and counter-measures, but they may require high computational complexity or rely on assumptions about the target behavior.
Missile Control
Missile control is the process of implementing the commands generated by the guidance system using the missile actuators, such as fins or thrusters. The missile control system consists of an autopilot that stabilizes the missile attitude and a servo system that actuates the missile fins or thrusters.
The design of autopilots is usually based on linear control theory and uses classical methods such as PID controllers or state feedback controllers. However, these methods may not be adequate for nonlinear systems or systems with uncertainties or constraints. Therefore, new nonlinear control techniques have been developed to improve the autopilot performance over a wide range of flight conditions.
Some of these nonlinear control techniques include feedback linearization, backstepping, adaptive control, sliding mode control, fuzzy logic control, neural network control, and model predictive control. These techniques can handle nonlinearities, uncertainties, constraints, and disturbances in a more effective way than linear control techniques, but they may also have some drawbacks such as complexity, stability issues, or tuning difficulties.
Missile Estimation
Missile estimation is the process of estimating the state of the missile or the target using measurements from sensors such as radar, infrared, or GPS. The state estimation problem can be formulated as a filtering problem that involves updating a prior estimate based on new measurements.
The most common filtering technique used for missile estimation is the Kalman filter, which is an optimal estimator for linear systems with Gaussian noise. However, the Kalman filter may not be suitable for nonlinear systems or systems with non-Gaussian noise. Therefore, new filtering techniques have been developed to deal with these challenges.
Some of these filtering techniques include extended Kalman filter, unscented Kalman filter,
particle filter,
and multiple model adaptive estimation. These techniques can handle nonlinearities,
non-Gaussian noise,
model uncertainties,
and multiple hypotheses in a more flexible way than
the Kalman filter,
but they may also have some drawbacks such as
computational cost,
divergence issues,
or initialization difficulties.
Conclusion
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