In recent years, the strapdown system has become the significant trend of the development of precision guided weapons. In order to effectively hit the lightly armored targets, researchers have done a series of studies on the impact angle control. However, most of them ignored the field-of-view constraint of the strapdown seeker. The existing impact angle control guidance laws with the field-of-view constraint have obvious drawbacks to some extent. For example, most of them are only applicable against nonmaneuvering targets and some of them suffer sudden hopping of guidance command due to the use of switching logic. Taking the strapdown seeker missile as the research object, this paper studies the field-of-view constrained impact angle control guidance problem against stationary targets, constant speed targets and maneuvering targets. According to the sliding mode control theory, two kinds of sliding surfaces are designed to make the missile achieve high precision attack under a desired impact angle within the field-of-view constraint. A quadratic barrier Lyapunov function and a logarithm barrier Lyapunov function are used to guarantee that the sliding surfaces designed for the impact angle constraint and the field-of-view constraint can converge to zero in finite time respectively. Then, a nonlinear extended state observer is proposed to estimate the unmeasurable disturbance of the system and alleviate the undesirable chattering by compensating it with the estimation of the disturbance. Finally, the feasibility of the proposed law is verified by numerical simulations.

Traditional grain designs, which identify the best combination of geometrical parameters to improve the grain performance and meet the flight-mission requirements, are often performed manually. In this article, an integrated framework is presented to perform the design optimization of solid rocket motor propellant grains. In the proposed framework, the level set method is adapted to solid propellant burnback analysis and this technique does not have any restriction on the grain configuration and is capable of handling grains of multi-stage and various burning forms. Along with the level set method, a dedicated algorithm is developed using application programming interfaces of commercial computer-aided design software to transform the initial grain shape into a special data file that can be fed to the level set codes to activate the burnback analysis. Moreover, a hybrid optimization method incorporating genetic algorithm and sequential quadratic programming is exploited to improve the grain design efficiency. Finally, two case studies have been performed to verify the feasibility and general-purpose characteristics of the proposed grain design optimization environment. The results obtained show that the proposed design framework facilitates the grain optimization process and various grain design requirements can be met. The design cycle has been remarkably reduced because of the introduction of hybrid optimization method.

Conventional space-filling experimental design provides uniform coverage of a hypercube design space. When constraints are imposed, the results may contain many infeasible points. Simply omitting these points leads to fewer feasible points than desired and a design of experiments that is not optimally distributed. In this research, an adaptive method is developed to create space-filling points in arbitrarily constrained spaces. First, a design space reconstruction method is developed to reduce the invalid exploration space and enhance the efficiency of experimental designs. Then, a synthetic criterion of uniformity and feasibility is proposed and optimized by the enhanced stochastic evolutionary method to obtain the initial sampling combination. Finally, an adaptive adjustment strategy of design levels is constructed to obtain the required number of feasible points. Various test cases with convex and non-convex, connected and non-connected design spaces are implemented to verify the efficacy of the proposed method.

Solid rocket motor design matching the thrust requirement of the overall vehicle has become an exigent task. In the present research, an efficient performance matching optimization approach is proposed and applied to the solid rocket motor design. To obtain reliable solid rocket motor designs, refined performance evaluation is accomplished successfully based on the coupling of the level set grain burnback analysis and one-dimensional ballistic simulation by the time-stepping algorithm. Due to the heavy computing burden of the coupled performance solver, a novel progressive matching optimization approach is proposed. This approach depends on partial but not full-burn thrust profile to eliminate inferior candidates, predict superior candidates, advance the simulation and drive the candidates converging to the specified performance. Validation of the proposed solid rocket motor design approach is carried out by two test cases possessing different thrust requirements, and results indicate that the proposed approach is practical and efficient.