Reusable Launch Vehicle Reentry Research Articles

Three-Degree-of-Freedom Hypersonic Reentry Trajectory Optimization Using an Advanced Indirect Method

This study investigates a complicated reentry trajectory optimization problem for a reusable launch vehicle (RLV) in the presence of two control constraints and three state path constraints. Upon using traditional indirect methods, the considered RLV reentry trajectory optimization problem is converted into a complicated 12-point boundary-value problem that requires a priori knowledge about the structure of control and state constraints. Instead, a recently developed indirect method, the uniform trigonometrization method (UTM) is used, which leads to a two-point boundary-value problem, making the solution procedure notably easier. New features are introduced into the UTM framework in order to incorporate mixed state-control path constraints. Two scenarios are considered in which the control and state path constraints are 1) inactive and 2) active, respectively. A numerical continuation strategy involving backward-in-time propagation of the dynamics from the final point to the initial point with sequential implementation of the path constraints is proposed. The results are validated with a direct pseudospectral method. This study is the first instance of applying the UTM to a three-degree-of-freedom RLV reentry trajectory optimization problem in the presence of multiple linear and polynomial-form control inputs and mixed state-control path constraints.

A hybrid hyper-heuristic whale optimization algorithm for reusable launch vehicle reentry trajectory optimization

Trajectory optimization is essentially an optimal control problem (OCP) with highly nonlinear dynamic properties and complex constraints, and a critical part of spacecraft design. In this paper, a hybrid algorithm is proposed for automatic reentry trajectory optimization of reusable launch vehicle (RLV) without providing user-specified initial guesses and a priori knowledge about the optimal trajectory. The method combines the strong robustness and global optimization properties of hyper-heuristic whale optimization algorithm (HHWOA) with the efficient and accurate features of the Gauss pseudospectral method (GPM). HHWOA works as the first-stage optimizer aims to obtain an approximate solution to provide a high-quality initial guess for the GPM, while the GPM works as the second-stage optimizer aims to accelerate the search of the optimum neighborhood to obtain an accurate optimal solution. Additionally, to enhance the progress during the evolutionary process, HHWOA is equipped with opposition-based learning, differential evolution operators, chaotic map sequences and smoothing technique strategies. The utilization of such strategies can potentially smooth the flight trajectory and improve the global convergence of the algorithm, while the three OCPs have shown their superiority in HHWOA. In order to evaluate the performance of the hybrid algorithm, complex constrained RLV maximum cross-range reentry problems with three different path constraint scenarios are investigated. Furthermore, more discussion and experiments are likewise conducted to investigate the impact of the parameters on the performance of the algorithm. The results show that the proposed hybrid algorithm can be very effective in addressing RLV reentry trajectory optimization problems.

Correction to: A hybrid parallel Harris hawks optimization algorithm for reusable launch vehicle reentry trajectory optimization with no-fly zones

Correction to: A hybrid parallel Harris hawks optimization algorithm for reusable launch vehicle reentry trajectory optimization with no-fly zones

A hybrid parallel Harris hawks optimization algorithm for reusable launch vehicle reentry trajectory optimization with no-fly zones

Reentry trajectory optimization is a critical optimal control problem for reusable launch vehicle (RLV) with highly nonlinear dynamic characteristics and complex constraints. In this paper, a hybrid parallel Harris hawks optimization (HPHHO) algorithm is proposed to address the problem. HPHHO aims to enhance the performance of existing Harris hawks optimization (HHO) algorithm by three strategies including oppositional learning, smoothing technique and parallel optimization mechanism. At the beginning of each iteration, the opposite population is calculated from the current population by the oppositional learning strategy. Following that, the individuals in the two populations are arranged in ascending order on the basis of the fitness function values, and the top half of the resulting population is selected as the initial population. The selected initial population is divided into two equal subpopulations which are assigned to the differential evolution and the HHO algorithm, respectively. The both algorithms operate in parallel to search and update the solutions of each subpopulation simultaneously. Then the solutions are smoothed for each iteration by the smoothing technique to reduce fluctuations. As a result, the optimal solution obtained by the parallel optimization mechanism avoids falling into local optima. The performance of HPHHO is evaluated by 4 CEC 2005 benchmark functions and 3 constrained continuous optimal control problems, showing better efficiency and robustness in terms of performance metrics, convergence rate and stability. Finally, the simulation results show that the proposed algorithm is very effective, practical and feasible in solving the RLV reentry trajectory optimization problem.

Hybrid multi-objective control allocation strategy for reusable launch vehicle in re-entry phase

The reentry of the reusable launch vehicle (RLV) normally requires redundant and heterogeneous actuators to assist attitude control, so the control allocation (CA) technology for the over-actuated system is a key research nowadays. This paper not only discusses how to allocate the control command between two different kinds of actuators, the reaction control system (RCS) and control surfaces, but also presents a hybrid multi-objective control allocation strategy based on the analytic hierarchy process (AHP) method to achieve the pareto solutions to RLV's allocation problems. Firstly, both actuators' dynamic models with corresponding constraints, and RLV's nonlinear model are built as the basis. Secondly, this paper considers a series of optimization functions, such as the minimum of control allocation error, the minimum of the fuel consumption, the maximum of actuators' control efficiency, which makes RLV's allocation problem multi-objective and multi-constraint. Meanwhile, to achieve the pareto solution or the integrated optimal one from all the feasible solutions, a weighted combination approach is introduced in. Then, this multi-objective allocation problem is transformed into a more easily solved, single-objective one, in which each optimization function is multiplied with its corresponding dynamic weight value based on the preference matrix of AHP. Finally, this designed strategy is demonstrated in RLV's re-entry phase, which not only verifies its effectiveness and fault-tolerant capability, but also shows its advantages of achieving better comprehensive allocation performance by comparisons with the single-objective control or the other two traditional daisy-chaining and proportional allocation methods.

Mid-Sea and Land Based Wind Measurement for Technology Demonstrator

This paper presents the requirement and importance of wind data for launch vehicle re-entry missions and describes how the wind measurement and wind profile generation was done effectively for India's first reusable launch vehicle re-entry mission (RLV TD HEX01). The details of ISRO's Pisharoty sonde system, which was used for wind measurement for this mission is also presented. The system provided timely and quality wind data for the DOL-WB of RLV-TD HEX01 mission as per the ascent and descent phase requirements. The scheme for the generation of final wind profile from sonde data, high altitude balloon ascent data, and sounding rocket data, which has to be loaded to the reentry vehicle before liftoff is also described in detail. The augmented scheme with extra ship-based measurements for wind profile generation and backup plan for DOL-WB during both ascent and descent phase proved highly effective. The pressure, temperature, and humidity data collected using the sonde system from both ship-based and SITAR-based measurements, which was vital in atmospheric modeling and prediction during the campaign is also presented in this paper.

Finite-Time Super-Twisting Controller Based on SESO Design for RLV Re-Entry Phase

In this paper, an improved extended state observer (ESO) based on sigmoid function and a finite-time convergence attitude controller are designed for reusable launch vehicle (RLV) in the re-entry phase. First, a control-oriented model (COM) of the RLV is established. According to the singular perturbation theory, the RLV control system is divided into an outer-loop and inner-loop subsystems. Second, a sigmoid function ESO (SESO) is proposed to estimate the model uncertainties and external disturbance caused by the large attitude maneuver and complicated external environment during the RLV re-entry phase. The continuous differentiable sigmoid function has the significant ability in noise suppression. By selecting the proper Lyapunov function, the stability of the SESO is proved. Then, based on the sliding mode control (SMC) theory, an improved multivariable super-twisting high-order sliding mode controller is designed. The finite-time convergence for the whole system is proven by the Lyapunov function technology. Finally, a 6-degree-of-freedom (6-DOF) RLV model is utilized to simulate to verify the effectiveness and robustness of the proposed control scheme.

Reentry attitude control for a reusable launch vehicle with aeroservoelastic model using type‐2 adaptive fuzzy sliding mode control

SummaryUsing a type‐2 adaptive fuzzy sliding mode control approach, a nonlinear reentry attitude control scheme is presented in this paper for a reusable launch vehicle (RLV) with aeroservoelastic model that assures reliable tracking of guidance commands. The mutual interactions between aerodynamics, structural dynamics, and the flight control system may lead to the aeroservoelasticity problem. Thus, based on the six‐degree‐of‐freedom geometry model of a RLV, the aeroservoelastic model is firstly established via hypothetical modal method and Galerkin method. Then, the overall attitude control strategy is carried out in two‐loop subsystem controller by using backstepping method. For each loop subsystem, a sliding mode controller is developed to assure tracking of guidance commands, and the interval type‐2 fuzzy logic systems combined with adaptive technique are employed to approximate the nonlinear parts to improve the reentry attitude tracking performance. Furthermore, the virtual control is introduced to the control strategy to attenuate the effect of control saltation. Theoretical analysis based on Lyapunov approach illustrates that the closed‐loop system under the designed control method is uniformly stable, and the attitude tracking error converges to a small neighborhood around origin. Finally, the six‐degree‐of‐freedom flight simulation results are provided to demonstrate the effectiveness of the proposed control scheme and aeroservoelastic characteristic of reentry RLV.

Finite-time attitude tracking control design for reusable launch vehicle in reentry phase based on disturbance observer

For the attitude tracking control problem of reentry reusable launch vehicle in reentry phase, a disturbance observer–based sliding mode controller is proposed in this article. At first, attitude m...

Adaptive disturbance observer‐based finite‐time continuous fault‐tolerant control for reentry RLV

SummaryThe control effectors of reusable launch vehicle (RLV) can produce significant perturbations and faults in reentry phase. Such a challenge imposes tight requirements to enhance the robustness of vehicle autopilot. Focusing on this problem, a novel finite‐time fault‐tolerant control strategy is proposed for reentry RLV in this paper. The key of this strategy is to design an adaptive‐gain multivariable finite‐time disturbance observer (FDO) to estimate the synthetical perturbation with unknown bounds, which is composed of model uncertainty, external disturbance, and actuator fault considered as the partial loss of actuator effectiveness in this work. Then, combined with the finite‐time high‐order observer and differentiator, a continuous homogeneous second‐order sliding mode controller based on the terminal sliding mode and super‐twisting algorithm is designed to achieve a fast and accurate RLV attitude tracking with chattering attenuation. The main features of the integrated control strategy are that the adaptation algorithm of FDO can achieve non‐overestimating values of the observer gains and the second‐order super‐twisting sliding mode approach can obtain a more elegant solution in finite time. Finally, simulation results of classical RLV (X‐33) are provided to verify the effectiveness and robustness of the proposed fault‐tolerant controller in tracking the guidance commands. Copyright © 2017 John Wiley & Sons, Ltd.

Data-driven RLV multi-objective reentry trajectory optimization based on new QABC algorithm

In recent years, big data and cloud technology have been widely used in information extraction and optimization decision. An improved artificial bees colony (ABC) algorithm called QABC is proposed for optimum design of the reusable launch vehicle (RLV) reentry trajectory. Because of poor convergence property of classical ABC algorithm in solving constrained nonlinear optimization problems (CNOPs), several modifications are carried out in this paper. The modifications include a quantum delta potential well model, two dynamic tolerance mechanisms, and a general generation mechanism of selection probability which is associated with the fitness of food source. In this paper, taking RLV three-dimension reentry trajectory design as an application example, a single-objective/multi-constraints optimization model was established with physical programming (PP) method and static penalty function method, in which four objectives (maximum range, minimum heat load, minimum heat flux (MHF), minimum oscillation) and five constraints (dynamic pressure, overload, heat flow, terminal altitude, and terminal velocity) were taken into account. Four single objective trajectory designs and two typical multi-objective trajectory designs with different preference structures were resolved, and the results showed that the optimization model founded by PP method was effective and flexible to reflect the designers preference. The improved algorithm, QABC, show excellent performance in solving RLV reentry trajectory optimization problem and a good prospect in other engineering applications.

Robust adaptive constrained backstepping flight controller design for re-entry reusable launch vehicle under input constraint

A nonlinear constrained controller is designed for a reusable launch vehicle during re-entry phase in the presence of model uncertainty, external disturbance, and input constraint, via combining sliding mode control and adaptive backstepping control. Since the complex coupling between the translational and rotational dynamics of reusable launch vehicle, a control-oriented model derived from rotational dynamic is used for controller design. During the virtual control input design procedure, a dynamic robust term is utilized to compensate for the uncertainty. In addition, a filter is applied to handle “explosion of terms” problem during the actual control input design. To reduce the computational burden, adaptive law is used to evaluate the unknown norm bound of the lumped uncertainty. An auxiliary system is constructed to compensate for the input constraint effect. The stability of the closed-loop system is analyzed based on Lyapunov theory. Simulation results demonstrate the validity of the developed controller in providing stable tracking of the guidance command by numerical simulation on the 6-degree-of-freedom model of reusable launch vehicle.

Design of Optimal Attack-Angle for RLV Reentry Based on Quantum Particle Swarm Optimization

The attack-angle optimization is a key problem for reentry trajectory design of a gliding type reusable launch vehicle (RLV). In order to solve such a problem, the equations of motion are derived first. A physical programming (PP) method is briefly presented and the preference function is reflected in mathematical representation. The attack-angle optimization problem with four criteria (i.e., downrange, total heat, heat rate, and trajectory oscillation) is converted into a single-objective optimization problem based on the PP method. A winged gliding reentry RLV is chosen as a simulation example and the transformed single-objective problem is solved by the quantum-behaved particle swarm optimization (QPSO) algorithm based on two types of preference structures, longer range preference and smaller total heat preference. The constraints of maximizing heating rate, normal load factor, and dynamic pressure and minimizing terminal velocity are handled by a penalty function method. The simulation results demonstrate the efficiency of these methods. The physical causation of the optimal solution and the typical profiles are presented, which reflect the designer's preference. At last, the feasibility and advantages of QPSO are revealed by comparison with the results of genetic algorithm (GA) and standard particle swarm optimization (PSO) algorithm on this optimization problem.

Adaptive Backstepping Finite Time Attitude Control of Reentry RLV with Input Constraint

This paper presents the finite time attitude tracking control problem of reusable launch vehicle (RLV) in reentry phase under input constraint, model uncertainty, and external disturbance. A control-oriented model of rotational dynamics is developed and used for controller design for the complex coupling of the translational and rotational dynamics. Firstly, fast terminal sliding mode control is incorporated into backstepping control to design controller considering model uncertainty and external disturbance. The “explosion of terms” problem inherent in backstepping control is eliminated by a robust second order filter. Secondly, the control problem in the presence of input constraint is further considered, and a constrained adaptive backstepping fast terminal sliding mode control scheme is developed. At the control design level, adaptive law is employed to estimate the unknown norm bound of lumped uncertainty with the reduction of computational burden. The Lyapunov-based stability analysis of the closed-loop system is carried out. The control performance is presented via the simulation of six-degree-of-freedom (6-DOF) model of RLV.