| 摘要: |
| The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn (STT) missile model with uncertainties and unknown dynamic equations. A predictive control for the STT missile is designed based on nonlinear model predictive control (NMPC) using Taylor series expansion, after which, via a neural network (NN), unknown functions are approximated. The present study also evaluates an adaptive optimal observer of a new strategy-based nonlinear system. Specifically, to estimate the missile states such as normal acceleration and its derivatives for the future, originally the Taylor series states expansion was gained to any specified order, based on their receding horizons. To address the problem of prediction error, an analytic solution was prepared that led to a closed form regarding the nonlinear optimal observer. Out of the gains resulting from the analytic solution, as developed for the problem of prediction error, the selection of the proposed observer gain was optimally conducted to meet the stability condition. Thus, combining the adaptive predictive autopilot and the adaptive optimal observer scheme was implemented to secure the performance, which needed only estimated normal acceleration and its derivatives. Meanwhile, no angular velocity measurement or wind angle estimation was required. Ultimately, the proposed technique was found effective, as confirmed by the qualitative simulation results. |
| 关键词: Missile autopilot · Nonlinear systems · State prediction · Predictive control · Uncertainty · Optimal observer |
| DOI：https://doi.org/10.1007/s11768-025-00282-6 |
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| 基金项目: |
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| A new design of adaptive predictive autopilot for skid-to-turn missile with uncertain dynamics through state prediction |
| Saeed Kashefi1,Majid Hajatipour1 |
| (Faculty of Electrical and Computer Engineering, University of Kashan, Kashan, Iran) |
| Abstract: |
| The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn (STT) missile model with uncertainties and unknown dynamic equations. A predictive control for the STT missile is designed based on nonlinear model predictive control (NMPC) using Taylor series expansion, after which, via a neural network (NN), unknown functions are approximated. The present study also evaluates an adaptive optimal observer of a new strategy-based nonlinear system. Specifically, to estimate the missile states such as normal acceleration and its derivatives for the future, originally the Taylor series states expansion was gained to any specified order, based on their receding horizons. To address the problem of prediction error, an analytic solution was prepared that led to a closed form regarding the nonlinear optimal observer. Out of the gains resulting from the analytic solution, as developed for the problem of prediction error, the selection of the proposed observer gain was optimally conducted to meet the stability condition. Thus, combining the adaptive predictive autopilot and the adaptive optimal observer scheme was implemented to secure the performance, which needed only estimated normal acceleration and its derivatives. Meanwhile, no angular velocity measurement or wind angle estimation was required. Ultimately, the proposed technique was found effective, as confirmed by the qualitative simulation results. |
| Key words: Missile autopilot · Nonlinear systems · State prediction · Predictive control · Uncertainty · Optimal observer |
