Generalized Formulation of Linear Nonquadratic Weighted Optimal Error Shaping Guidance Laws

Abstract

This study presents a novel extension to the theory of optimal guidance laws represented by the non-traditional class of performance indices; non-quadratic-type signal L -norm for the input weighted by an arbitrary positive function. Various missile guidance problems are generally formulated into a scalar terminal control problem based on the understanding of the predictor-corrector nature. Then, a new approach to derive the optimal feedback law minimising the non-quadratic performance index is proposed by utilising the Hölderian inequality. The proposed extension allows a more general family of formulations for the design of closed-form feedback solutions to various guidance problems to be treated in a unified framework. The equivalence between the proposed approach and other design methodologies is investigated. In general, the type of input norm mainly determines the variability of input during the engagement while trading off against the rate of error convergence. The analytic solution derived in this study is verified by comparison with the solution from numerical optimisation, and the effect of the exponent in the performance index on trajectory and command is demonstrated by numerical simulations. * Senior Researcher, The 1st R&D Institute,

Range-Aware Impact Angle Guidance Law With Deep Reinforcement Meta-Learning

“…A range-aware hyperbolic tangent function is designed in this work to tackle this problem. Recent work in [20] also use error shaping to trade off acceleration against rate of error convergence. Our method inspired by this tradeoff but differs by the range-aware weighting function that is adaptive respect to different stage of engagement, which benefit guidance performance.…”

“…where is the Gaussian noise vector. Thus this is equivalent to the solution of the path integral approach in (20) and the resulting control command is the solution to the stochastic HJB equation, which can also be expressed in (26), which is also shown in [39]. If we choose the value function as the stochastic control Lyapunov function (SCLF) [36], according to proof of Lemma 3.14 in A.1.4 in [40], is positive definite in Lyapunov sense such that (0, ) = 0, ( , ) ≥ (| |) ∀ > 0, ∈ .…”

Design of Three-Dimensional Intelligent Guidance Law for Intercepting Highly Maneuvering Target

“…In [19], a finite-time dual-layer guidance law considering the second-order autopilot dynamics was designed, and an extended state observers are used to estimate the unknown target maneuver and acceleration derivative of the missile. with the development of artificial intelligence technology, neural networks [20][21][22] and deep reinforcement learning technique [23][24][25][26][27][28] have also begun to be applied for the design of guidance laws to estimate the uncertain dynamics of maneuvering targets. Moreover, in order to reduce the computational cost of the neural networks, a non-fragile quantitative prescribed performance control method was developed in [29], a simplified finite-time fuzzy neural controller has been introduced in [30], an adaptive critic design-based fuzzy neural controller was proposed in [31].…”