Design and Instruction Solution of an Optimal Guidance Law for Ship-Air Missile beyond Visual Range

2012 ◽  
Vol 433-440 ◽  
pp. 3831-3836
Author(s):  
Yong Tao Zhao ◽  
Yun An Hu
Keyword(s):  
Control Variable ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Guidance Law ◽  
The Earth ◽  
Optimal Guidance ◽  
Quadratic Optimal Control ◽  
Simulation Results ◽  
Visual Range ◽  
Terminal Guidance

For the case of ship-air missile intercepting the low target beyond visual range by ship-ship coordination, the instruction solution model was presented and an optimal guidance law was designed considering the effect of the curvature of the earth. In the midcourse and terminal guidance segment, the optimal guidance law was designed through applying the concept of the pseudo control variable and the theory of the linear quadratic optimal control. The information of the target was described in the launch coordinates through coordinate transformation to realize the instruction solution for the designed guidance law. The simulation results show that the model of the instruction solution is correct and the designed guidance law is feasible.

A missile guidance law tolerant to unestimated evasive maneuvers

2017 ◽  
Vol 89 (2) ◽  
pp. 314-319
Author(s):  
Julien Marzat
Keyword(s):  
Minimum Energy ◽  
Closed Form Expression ◽  
Trivial Extension ◽  
Linear Quadratic ◽  
Proportional Navigation ◽  
Linear Quadratic Optimal Control ◽  
Content Type ◽  
Guidance Law ◽  
Quadratic Optimal Control ◽  
Terminal Guidance

Purpose This note aims to introduce a terminal guidance law that is able to compensate for evasive target maneuvers without estimating their acceleration. Design/methodology/approach The new guidance law is derived in the framework of linear-quadratic optimal control to ensure interception with minimum energy even in the presence of a target maneuver. Findings An explicit closed-form expression for the missile acceleration command is provided, which turns out to be a non-trivial extension of proportional navigation guidance. Simulation results against evasive maneuvers of various intensities are provided to compare the new law to classical ones and thus show the benefits of the proposed approach. Originality/value The proposed guidance law was not reported so far in the literature and provides a simple way to deal with evasive maneuvers.

scholarly journals Maximum Principle for Delayed Stochastic Linear-Quadratic Control Problem with State Constraint

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Feng Zhang
Keyword(s):  
Maximum Principle ◽  
State Constraints ◽  
Optimal Control Problems ◽  
Necessary Conditions ◽  
Control Variable ◽  
Sufficient Conditions ◽  
State Constraint ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Quadratic Optimal Control

This paper is concerned with one kind of delayed stochastic linear-quadratic optimal control problems with state constraints. The control domain is not necessarily convex and the control variable does not enter the diffusion coefficient. Necessary conditions in the form of maximum principle as well as sufficient conditions are established.

scholarly journals The Delayed Doubly Stochastic Linear Quadratic Optimal Control Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yan Chen ◽  
Jie Xu
Keyword(s):  
Optimal Control ◽  
Control System ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Riccati Equation ◽  
Control Variable ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Doubly Stochastic ◽  
Quadratic Optimal Control

In this paper, the delayed doubly stochastic linear quadratic optimal control problem is discussed. It deduces the expression of the optimal control for the general delayed doubly stochastic control system which contained time delay both in the state variable and in the control variable at the same time and proves its uniqueness by using the classical parallelogram rule. The paper is concerned with the generalized matrix value Riccati equation for a special delayed doubly stochastic linear quadratic control system and aims to give the expression of optimal control and value function by the solution of the Riccati equation.

scholarly journals Convergence results for an averaged LQR problem with applications to reinforcement learning

2021 ◽  
Author(s):  
Andrea Pesare ◽  
Michele Palladino ◽  
Maurizio Falcone
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Current System ◽  
Numerical Test ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Lqr Problem ◽  
Quadratic Optimal Control

AbstractIn this paper, we will deal with a linear quadratic optimal control problem with unknown dynamics. As a modeling assumption, we will suppose that the knowledge that an agent has on the current system is represented by a probability distribution $$\pi $$ π on the space of matrices. Furthermore, we will assume that such a probability measure is opportunely updated to take into account the increased experience that the agent obtains while exploring the environment, approximating with increasing accuracy the underlying dynamics. Under these assumptions, we will show that the optimal control obtained by solving the “average” linear quadratic optimal control problem with respect to a certain $$\pi $$ π converges to the optimal control driven related to the linear quadratic optimal control problem governed by the actual, underlying dynamics. This approach is closely related to model-based reinforcement learning algorithms where prior and posterior probability distributions describing the knowledge on the uncertain system are recursively updated. In the last section, we will show a numerical test that confirms the theoretical results.

scholarly journals Two Inverse Problems Solution by Feedback Tracking Control

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 137
Author(s):  
Vladimir Turetsky
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Feedback Linearization ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Linear Quadratic Tracking ◽  
Ill Posed ◽  
Quadratic Optimal Control ◽  
Quadratic Tracking

Two inverse ill-posed problems are considered. The first problem is an input restoration of a linear system. The second one is a restoration of time-dependent coefficients of a linear ordinary differential equation. Both problems are reformulated as auxiliary optimal control problems with regularizing cost functional. For the coefficients restoration problem, two control models are proposed. In the first model, the control coefficients are approximated by the output and the estimates of its derivatives. This model yields an approximating linear-quadratic optimal control problem having a known explicit solution. The derivatives are also obtained as auxiliary linear-quadratic tracking controls. The second control model is accurate and leads to a bilinear-quadratic optimal control problem. The latter is tackled in two ways: by an iterative procedure and by a feedback linearization. Simulation results show that a bilinear model provides more accurate coefficients estimates.

scholarly journals A Hida–Malliavin white noise calculus approach to optimal control

2018 ◽  
Vol 21 (03) ◽  
pp. 1850014
Author(s):  
Nacira Agram ◽  
Bernt Øksendal
Keyword(s):  
Optimal Control ◽  
Diffusion Coefficient ◽  
White Noise ◽  
Backward Stochastic Differential Equation ◽  
Second Order ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Alternative Approach ◽  
Quadratic Optimal Control ◽  
White Noise Calculus

The classical maximum principle for optimal stochastic control states that if a control [Formula: see text] is optimal, then the corresponding Hamiltonian has a maximum at [Formula: see text]. The first proofs for this result assumed that the control did not enter the diffusion coefficient. Moreover, it was assumed that there were no jumps in the system. Subsequently, it was discovered by Shige Peng (still assuming no jumps) that one could also allow the diffusion coefficient to depend on the control, provided that the corresponding adjoint backward stochastic differential equation (BSDE) for the first-order derivative was extended to include an extra BSDE for the second-order derivatives. In this paper, we present an alternative approach based on Hida–Malliavin calculus and white noise theory. This enables us to handle the general case with jumps, allowing both the diffusion coefficient and the jump coefficient to depend on the control, and we do not need the extra BSDE with second-order derivatives. The result is illustrated by an example of a constrained linear-quadratic optimal control.

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