Time-optimal reorientation maneuvers for a combat aircraft

A composite framework for co-optimization of spacecraft trajectory and propulsion system

The task of achieving a safe and short landing for a flying-wing unmanned aircraft with a three-bearing-swivel thrust vector is highly challenging. The process is further complicated due to the need to switch between multiple control modes, while also ensuring the protection of the flight boundaries from environmental disturbances and model uncertainties to ensure flight safety. To address this challenge, this paper proposes a short-landing strategy that employs mixed control using lift fans, thrust vectors, and aerodynamic control surfaces. The extended state observer (ESO) is integrated into the inner angular rate control and outer sink rate control to account for environmental disturbances and model uncertainties. To ensure flight safety, the attainable linear and angular acceleration is calculated through a trim analysis to determine the command value of velocity and angle of attack during a short landing. Additionally, a flight boundary protection method is employed which includes an additional command value of the angle of attack, resulting in a higher probability of a successful landing. This paper provides a detailed description of the short-landing strategy, including the control objectives for each phase. Finally, a Monte Carlo simulation is conducted to evaluate the effectiveness and robustness of the short-landing strategy, and the landing accuracy is assessed using the circular error probability metric.

Thrust vector control (TVC) might be used to control aircraft at large altitudes and in post-stall conditions when aerodynamic control surfaces are ineffective. This study demonstrated that the implementation of the TVC on high-speed aircraft is a reasonable solution and might be an alternative when compared to the complicated reaction control system or large aerodynamic control surfaces. The numerical flight dynamics model of the X-15 experimental aircraft was developed and implemented in MATLAB/Simulink and then used to investigate the proposed solution. The obtained results indicate that the aircraft, equipped with full 3D thrust vectoring and two independent horizontal stabilizers to control the roll angle, was able to achieve flight along the path that was defined by a set of waypoints. This paper also highlights the potential benefits and challenges of using TVC as a control method for aircraft. The results of this study contribute to the growing body of research on aircraft control and simulation. Future work can explore the use of TVC for other aircraft with unique configurations and low maneuverability features.

Some necessary conditions for optimality for a class of optimal control problems which are linear in the control variable

Exploring the impact of vector thrust on aircraft maneuverability utilizing bypass dual throat nozzle technology

The Thrust vector control (TVC) is a method for controlling the angular velocity and attitude of aerial vehicles (AV) by manipulating the thrust direction of propulsion. This technology enhances maneuverability and allows for dynamic aerobatics at low speeds and near-zero airspeeds without stalling at high angles of attack (AOA). As the aerodynamic control surfaces are ineffective for vehicles operating outside the atmosphere, TVC is a suitable technique for these applications. To design a control system for AVs utilizing TVC, an accurate mathematical model is essential to simulate flight parameters and optimize the control gains. This work presents a complete six degrees-of-freedom (6-DOF) high-fidelity simulation model of a thrust vector control aerial vehicle (TVC-AV). The nonlinear model is developed by dividing the mathematical representation into five submodules, including the geometrical model, the actuation model that was experimentally identified, and an aerodynamic model that was validated through semi-empirical techniques, computational fluid dynamics (CFD), and wind-tunnel experiments; in addition, the propulsion model’s characteristics are identified through experimentation, and the atmospheric model is based on International Standard Atmosphere (ISA) values. The integrated model was implemented in MATLAB[Formula: see text] (Simulink) that provides a foundation for designing effective flight controllers and guidance systems.

ABSTRACTIn general, an airship is equipped with hybrid-heterogeneous actuators: the aerodynamic surfaces, the vectored propellers and the buoyant ballonets. The aerodynamic surfaces have high efficiency in attitude control at high speed. However, vectored propellers are also introduced here for attitude control under the special working condition of low airspeed. Due to the lower thrust-to-weight ratio, the composite control of hybrid-heterogeneous actuators is the primary object in controller design for an airship. In composite attitude control, first the attitude moment allocation between aerodynamic control surfaces and vectored propellers is designed according to different dynamic airspeed, to achieve the smooth motion transition from low to high airspeed, then the weighted generalised inverse (WGI) is used to design the reconfigurable actuator allocation among the homogeneous multi-actuators, where the authority of every actuator can be decided by setting the corresponding value of the weight matrix, thus the control law is unchanged under different actuator configurations. Taking the mid-altitude airship as an example, the simulations of position control, trace tracking and altitude control are provided. Simulation results demonstrate that the attitude moments allocation obtains moment distribution between the aerodynamic surfaces and the vectored propellers under different airspeeds; the reconfigurable actuator allocation achieves a good distribution and reconfiguration among homogeneous actuators, thereby enhancing the reliability of the control system.

Launcher guidance and control (G&C) design is a very demanding process, most notably because mission requirements are known to compete against each other and strong couplings between different disciplines exist. These effects become even more impactful when it comes to reusable launch vehicles, many of which are currently under development. The main task of the G&C system is to generate and follow a trajectory to fulfil mission objectives. This is achieved using various actuator types, which typically include the main engine(s), thrust vector control (TVC), aerodynamic control surfaces and cold-gas thrusters of a reaction control system‎(RCS). The motion of these actuators introduces excitations of flexible modes and perturbations through the launchers’ structure which cannot be easily modelled since the mechanical properties are highly time-varying due to the propellant being burnt to generate thrust. External perturbations from aerodynamics or wind reinforce these effects. Furthermore, actuators and other subsystems are subject to faults and failures having multiple potential origins. All of these perturbations must be handled by the G&C system. In the current industrial approach, these multi-physics effects are tackled by separate teams using their own tools while G&C design is usually based on low-fidelity models that simplify the interactions between disciplines. The adoption of multi-physics acausal modelling approaches such as MODELICA and MATLAB Simscape is believed to enable a more efficient and accurate modelling of the multi-disciplinary interactions. This motivates the development of a dedicated multi-physics simulator for multi-actuated vertical take-off vertical landing (VTVL) vehicles based on MathWorks’ Simulink/Simscape. Setting up simulators is a crucial and time-consuming activity in GNC design. The openly distributable Rapid Reusable Launcher Simulation via Multi-physics Modeling (R2M2) tool is a multi-physics simulator framework that aims at reducing this modeling effort and enabling users to quickly and easily set up simulations of VTVL vehicles by only defining the required model parameters. This helps users to save time and resources and enables them to focus their research and development efforts on the GNC system. On the other hand, R2M2 provides the simulation environment to rapidly evaluate GNC approaches. In addition to simplifying the setup process, the R2M2 tool is also highly adaptable and able to simulate a wide range of VTVL mission scenarios, including different vehicle and actuator configurations and environmental conditions. Automated Monte Carlo routines are also provided to facilitate the investigation of the effects of wind disturbances and uncertain parameters. The tool is capable of modeling external influences such as gravitational and aerodynamic effects, as well as internal dynamics from time-varying and moving masses. Modeling of variable-mass dynamics is an essential component for launch vehicle simulations. Mass properties of launch vehicles are changing mainly due to the propulsion causing the ejection of combustion products through the engines’ nozzles. The varying mass influences not only the vehicle’s properties such as the center of mass and moments of inertia but also generates additional forces and moments for instance due to Coriolis effects [R1] [R2]. In addition, the tool can consider common actuator failure cases including the jamming of TVC actuators and aerodynamic control surfaces and degraded engine performance. Future implementations may extend the tool to being capable to consider sloshing effects and structural flexibility. This paper describes the design of the R2M2 tool and provides an overview of its functionalities and applications. The implementation within MathWorks’ physical simulation environments Simscape and Simscape Multibody is shown for all relevant subsystems and physical effects. Challenges and drawbacks using Simscape for flight mechanics modeling are addressed. These main challenges arise from limited Simscape functionalities and its closed-source nature when the interaction of variable mass dynamics and physical effects specific to aerospace applications, such as aerodynamic forces and moments, have to be modeled. Possible solutions and work-arounds, as implemented in the tool, are shown. A benchmark scenario of a two staged launch vehicle ascent is implemented to verify the desired functionality of the R2M2 tool. To enable trajectory tracking the benchmark vehicle is configured with an engine cluster of five throtteable main engines, thrust vector control (TVC), aerodynamic control surfaces (fins) and a reaction control system‎(RCS). A preliminary controller and control allocation design is shown which enables stable flight on the vehicle’s ascent trajectory. The benchmark implementation offers the potential for synthesis and assessment of more sophisticated RLV guidance and control algorithms to improve the overall system performance and robustness. [R1] F. O. Eke; Dynamics of Variable Mass Systems. Technical Report CR-1998-208246, National Aeronautics and Space Administration (NASA); 1999 [R2] B. Gäßler, L.E. Briese, P. Acquatella, P. Simplício, S. Bennani, M. Casasco; Variable-Mass Dynamics Implementation in Multi-Physics Environment for Reusable Launcher Simulations; 9th EUROPEAN CONFERENCE FOR AERONAUTICS AND AEROSPACE SCIENCES (EUCASS), 2022, Lille

In the present work a typical section aeroelastic model of a vertical fin is used to evaluate two different active control strategies to attenuate buffeting response at high angle of attack, namely the classic aerodynamic surface control approach using the rudder, and a new `smart' structures strain actuation approach. The three-degree-of-freedom typical section model including plunge, airfoil and flap rotation is augmented to incorporate the unsteady airloads due to motion, and an internal `buffet mode' to simulate the buffering excitation observed in wind tunnel tests of the fin at a given range of reduced frequencies. The two control strategies are studied using the Linear Quadratic Gaussian method of the Optimum Control Theory. Numerical results are presented in terms of Bode plots of the structure aeroelastic response, and root-loci of the aeroelastic modes at a representative flight condition.

For Meyer type optimal control problems the principle of optimality states that every subarc of an optimal solution that extends to the final time furnishes again a solution to the same optimal control problem, only with different initial conditions. Through a simple counter example it is shown in this paper that this principle is not satisfied, in general, for solutions obtained from a standard collocation approach. Introduction The principle of optimality [1] is one of the most intuitive and yet most fundamental theorems in the theory of optimal control. Its basic statement is that for Meyer type optimal control problems every subarc of an optimal solution that extends to the final time furnishes again a solution to the same optimal control problem, only with different initial conditions. This theorem has significant implications. For example, it is the starting point for the derivation of the Hamilton-Jacobi-Bellman equations [2] and it is the underlying principle in Bellman's dynamic programming approach [1], [3]. The present paper shows that the principle of optimality is not satisfied, in general, for solutions obtained from a standard collocation approach[4]. For a simple example whose collocation solution can be generated analytically it is shown that further improvements are possible along an optimal collocation solution if the design parameters along a number of leading nodes are frozen and the remainder of the trajectory is re-optimized. I. Continuous Optimal Control Problem In this section of the paper we introduce a simple class of optimal control problems without state and control constraints. In Section 1.2 we present the associated necessary conditions for optimality [2], [5]. For ease of notation we assume that the right-hand side of the 'Supervising Engineer, Member AIAA, t Senior Engineer, Member AIAA, *17 Research Drive, Hampton, VA 23666. ^Graduate Student, Member AIAA, Department, 304 Town Engineering, Ames, IA 50011 equations of motion does not depend explicitly on time t. This does not represent a loss of generality, as it is always possible to transform explicit time dependence away through the introduction of an additional, timelike state variable. 1. Problem Formulation Let us consider the following class of optimal control problems: min «e(Pwc[*o,*/]), (1) subject to the conditions ±(t) = /(*(*),«(*)), tf.(z(*oMo) = 0, (2)

In this paper, we investigate optimal control problems for Allen-Cahn variational inequalities with a dynamic boundary condition involving double obstacle potentials and the Laplace-Beltrami operator. The approach covers both the cases of distributed controls and of boundary controls. The cost functional is of standard tracking type, and box constraints for the controls are prescribed. We prove existence of optimal controls and derive first-order necessary conditions of optimality. The general strategy is the following: we use the results that were recently established by two of the authors for the case of (differentiable) logarithmic potentials and perform a so-called "deep quench limit". Using compactness and monotonicity arguments, it is shown that this strategy leads to the desired first-order necessary optimality conditions for the case of (non-differentiable) double obstacle potentials.

This chapter is devoted upon optimality, a topic in which the central result is the Pontryagin’s minimum principle. This important result is briefly approached from the classical calculus of variation. We show that the classical calculus of variation has some major limitations to modern control problems and motivate Pontryagin’s minimum principle. The Hamilton–Jacobi–Bellman method is discussed as an alternative approach to gain first-order necessary conditions for optimality. It is shown that both approaches correspond to each other under restrictive assumptions. The original Pontryagin’s minimum principle for continuous optimal control problems is not suitable for hybrid optimal control problems. However, a quite natural reformulation of the hybrid optimal control problem admits the classical theory for deduction of first-order necessary conditions in the sense of Pontryagin. The charm of this methodology is its comprehensible derivation.

Three convergent procedures are presented. These determine a minimizing point of a relaxed optimal control problem defined by delay-differential equations with the control restricted to a compact convex subset of ℝ m In the first of these procedures, a strong variational algorithm due to Virk (1985 a) is extended to relaxed controls. The second uses a steepest descent approach. It is shown that both these procedures generate accumulation controls satisfying first-order necessary conditions of optimality. In the third procedure, the relaxed controls generated at each iteration are approximated using ordinary controls. When this is done, we show that limit points satisfy optimality conditions to within delta. Numerical performances of all three algorithms are also presented.

This work aims to extend the techniques used for the optimal control of the NavierStokes systems to control a steady multi-scale FSI system. In particular, we consider a multiscale fluid-structure interaction problem where the structure obeys a membrane model derived from the Koiter shell equations. With this approach, the thickness of the solid wall can be neglected, with a meaningful reduction of the computational cost of the numerical problem. The fluid-structure simulation is then reduced to the fluid equations on a moving mesh together with a Robin boundary condition imposed on the moving solid surface. The inverse problem is formulated to control the velocity on a boundary to obtain a desired displacement of the solid membrane. For this purpose, we use an optimization method that relies on the Lagrange multiplier formalism to obtain the first-order necessary conditions for optimality. The arising optimality system is discretized in a finite element framework and solved with an iterative steepest descent algorithm, used to reduce the computational cost of the numerical simulations.

The paper describes a new approach to the optimal-power-flow problem based on Newton's method which it operates with an augmented Lagrangian function associated with the original problem. The function aggregates all the equality and inequality constraints. The first-order necessary conditions for optimality are reached by Newton's method, and by updating the dual variables and the penalty terms associated with the inequality constraints. The proposed approach does not have to identify the set of binding constraints and can be utilised for an infeasible starting point. The sparsity of the Hessian matrix of the augmented Lagrangian is completely exploited in the computational implementation. Tests results are presented to show the good performance of this approach.