Abstract
A new suboptimal control method is proposed in this
study to effectively design an integrated guidance and control
system for missiles. Optimal formulations allow designers to
bring together concerns about guidance law performance and
autopilot responses under one unified framework. They lead to
a natural integration of these different functions. By modifying
the appropriate cost functions, different responses, control saturations
(autopilot related), miss distance (guidance related), etc.,
which are of primary concern to a missile system designer, can
be easily studied. A new suboptimal control method, called the
- method, is employed to obtain an approximate closed-form
solution to this nonlinear guidance problem based on approximations
to the Hamilton–Jacobi–Bellman equation. Missile guidance
law and autopilot design are formulated into a single unified
state space framework. The cost function is chosen to reflect both
guidance and control concerns. The ultimate control input is the
missile fin deflections. A nonlinear six-degree-of-freedom (6-DOF)
missile simulation is used to demonstrate the potential of this new
integrated guidance and control approach.
Index Terms—Missile integrated guidance and control, nonlinear
systems, optimal control.
I. INTRODUCTION
INTEGRATED guidance and control (IGC) design is an
emerging trend in missile technology. This is a response
to the need for improving the accuracy of interceptors and
extending their kill envelope. Current and past practices in
industry have been to design guidance and control systems
separately and then integrate them into the missile. These
subsystems typically had different bandwidths. Despite the
fact that this paradigm has been applied successfully on many
systems, it can be argued that it is not truly optimized; therefore,
the overall system performance can be improved. Hit-to-kill
capabilities required in the next generation missile system will
demand an integrated approach in order to exploit synergism
between various missile subsystems and thereby improve the
total system performance. An IGC design can be formulated
as a single optimization problem, thus providing a unified
approach to interceptor performance optimization.
first addressed the application of an IGC
scheme to a homing missile. An optimal controllerwas designed
to combine the conventionally separated guidance law and autopilot
design into one framework by minimizing a quadratic
cost functional subject to intercept dynamics. The advantages
gained in this optimal control law were minimization of the
root-mean-square (rms) miss, the terminal angle of attack, the
pitch rate, and the control surface “flapping” rate in the presence
of unmodeled errors. However, this paper only dealt with
a nonmaneuvering target. Evers et al. [2] extended the concepts
presented in [1] to include a target acceleration model as a firstorder
Markov process. The resulting IGC law was expected to
be less sensitive to the errors in estimating the current target acceleration.
Menon and Ohlmeyer [3] employed the feedback linearization
method in conjunction with the linear quadratic regulator
(LQR) technique to design a nonlinear integrated guidance
and control laws for homing missiles. The IGC design was presented
in three formulations which were based upon three different
guidance objectives. A six-degrees-of-freedom (6-DOF)
nonlinear dynamic model of an air-to-air homing missile was
simulated and each of the three IGC schemes achieved a similar
favorable performance. Menon et al. [4] employed the feedback
linearization technique to the IGC of a moving-mass actuated
kinetic warhead. A 9-DOF simulation demonstrated good
results for interception of nonmaneuvering and weaving targets
in both endo-atmospheric and exo–atmospheric conditions. Although
feedback linearization is a powerful tool, it could cancel
beneficial nonlinearities and result in a large control. Also, it
is only applicable to systems which satisfy some conditions of
feedback linearizability [5].
Other IGC schemes that have been developed incorporate
various control theories. Shkolnikov et al. [6] developed an IGC
design using sliding-mode control. They divide their controller
development into inner and outer loop objectives. Menon and
Ohlmeyer [7] employed the state dependent Riccati equation
(SDRE) technique [8] to deal with a more comprehensive model
that is nonlinear with motion in three dimensions. The design
was evaluated based on a 6-DOF nonlinear missile model
with two types of target models, nonmaneuvering targets and
weaving maneuvering targets. The numerical results demonstrated
the feasibility of designing integrated guidance/control
systems for the next generation high-performance missile
systems. Palumbo and Jackson [9] formulated the IGC problem
as a single nonlinear minmax optimization problem, which is
to find a controller that minimizes the final miss distance and
control energy under worst case target maneuver and worst case
process and measurement disturbances. The state dependent
Riccati difference equation (SDRDE) technique was employed
to handle this finite-time horizon nonlinear problem

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