Trajectory Optimization and Analysis for Near Spacecraft’s Unpowered Gliding Phase

In this paper, an L1 penalized sequential convex programming (LPSCP) method for trajectory optimization is proposed. Sequential convex methods dramatically reduce computation time for nonlinear trajectory optimizations, based on the desirable properties of convex optimization algorithms. Due to its fast convergence speed, the sequential convex method is considered as a near-future candidate for real-time optimal online guidance. However, the generic sequential convex method seldom suffers from poor robustness to crude the initial trajectory estimates and constraint perturbations, which limits its application to real-world systems. Suggested LPSCP method resolves the robustness issue of generic sequential convex methods, by addressing the convex subproblem infeasibility. While improving its robustness, proposed LPSCP method maintains its fast convergence property. Consequently, LPSCP method enhances overall versatility of sequential convex method for trajectory optimization, which includes ameliorated robustness toward initial trajectory estimates, and improved stability to state and constraint perturbations. Throughout the paper, details of LPSCP method are outlined along with an application example to missile trajectory optimization problems. Desirable properties of LPSCP method are demonstrated through several simulated examples. Optimized trajectories are compared with results from the pseudospectral method. Numerical simulation and Monte Carlo analysis show that LPSCP method accelerates optimization process dramatically (−98.2% on average) while producing the same optimal trajectory.

A System for Collaborative Design, Analysis, and Optimization of Spacecraft Trajectories

As an increasing number of asteroids are being discovered, detecting them using limited propulsion resources and time has become an urgent problem in the aerospace field. However, there is no universal fast asteroid sequence selection method that finds the trajectories for multiple low-thrust spacecraft for detecting a large number of asteroids. Furthermore, the calculation efficiency of the traditional trajectory optimization method is low, and it requires a large number of iterations. Therefore, this study combines Monte Carlo tree search (MCTS) with spacecraft trajectory optimization. A fast MCTS pruning algorithm is proposed, which can quickly complete asteroid sequence selection and trajectory generation for multispacecraft exploration of multiple asteroids. By combining the Bezier shape-based (SB) method and MCTS, this study realizes the fast search of the exploration sequence and the efficient optimization of the continuous transfer trajectories. In the simulation example, compared with the traversal algorithm, the MCTS pruning algorithm obtained the global optimal detection sequence of the search tree in a very short time. Under the same conditions, the Bezier SB method obtained the transfer trajectory with a better performance index faster than the finite Fourier series SB method. Performances of the proposed method are illustrated through a complex asteroid multiflyby mission design.

This paper provides an extension to an optimal control problem using the negative logarithm of deterioration and spoilage function as total cost. This function must be minimized at the end of planning period depending on the alternative quadratic exponential form. The co-state variable has negative values along the optimal trajectory according to the Pontryagin Minimum Principle (PMP). The different values of this co-state variable are investigated using initial values for the optimal control rates, separately. The controlled system according to each value is presented. Studying the behavior of optimal inventory levels, the optimal production rates, and the optimal spoilage function, it is our optimal solution along the optimal trajectory. The effectiveness of increasing and decreasing the co-state values on the optimal trajectory especially at the end of planning period is investigated. Also, the sensitivity analysis that reflects the effect of changes of different parameters (the deterioration and spoilage parameters values, and the initial values of inventory levels and production rates) on the optimal solution is explained with many different cases. Finally, we compared, numerically, the results for using these different co-state values with the results obtained when this value is negative.

In this paper, the application of direct parameter optimization method to reentry vehicle three-dimension skipping trajectory optimization is introduced. Firstly, the model of trajectory optimization control problem to reentry vehicle skipping trajectory is established, while motion equation of the state variables is a three degree-of-freedom model. Performance is selected to minimize the sum of heat, overload and dynamic pressure. The control variables are angle-of-attack and bank angle. During the flight, reentry vehicle is subjected to the heat rate and overload constraints. Terminal state variables constraints are path angle, altitude and latitude constraints. Then, the optimal control problem is discretized into nonlinear programming problem (NLP) using direct parameter optimization method. The control variables are selected as optimal parameters at all nodes. Parameter optimization problem is solved using POWELL algorithm. The simulation result shows that the optimal solutions of skipping trajectory optimization problem are in real-time using direct parameter optimization method. Therefore, direct parameter optimization method is a viable approach to the reentry vehicle skipping trajectory optimization problem.

The objective of this paper is to investigate a reliable method to generate optimal ascent trajectory for hypersonic air-breathing vehicles. When solving optimal trajectories in endo-atmospheric flight, most of traditional indirect methods suffer from difficulties in finding an appropriate initial guess and getting a convergent solution for the two point boundary value problem (TPBVP). With improvements on on-board computer performance, direct methods such as pseudospectral method show promising potential for real-time optimal guidance. It removes the need for computing analytical gradients of aerodynamic coefficients, and remains high accurate solution similar to indirect methods. In this work, optimal ascent trajectory generation problem was formulated as a fuel-optimal control problem. Gauss pseudospectral method (GPM) was presented to generate the optimal ascent trajectories. Optimal solutions from GPM were compared with an indirect method based on finite difference method (FDM). Numerical simulations were studied with various initial conditions to investigate the optimal trajectory characteristics for hypersonic air-breathing vehicles. The results verified the validity and accuracy of GPM for ascent trajectory optimization.

This paper describes a recently developed method for the determination of optimal, e g minimum-fuel or minimum-time, trajectories for spacecraft. The method will be introduced after a brief discussion of methods previously used by researchers for trajectory optimization. However, the objective here is not to discuss all such methods or provide a comprehensive description of them but rather to provide sufficient information regarding extant methods that what is novel and beneficial about the solution method we have developed may be recognized. Several example optimal trajectories for spacecraft using lowthrust propulsion will be presented.KeywordsOptimal Control ProblemOptimal TrajectoryPrime VectorAdjoint VariableSystem Differential EquationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The paper is dedicated to the problem of finding optimal spacecraft trajectories. The equations of spacecraft motion are written in quaternion form. The spacecraft moves on its orbit under acceleration from the limited in magnitude jet thrust. It is necessary to minimize the energy costs for the process of reorientation of the spacecraft orbital plane. The equations of spacecraft motion are written in orbital coordinate system. It is assumed that spacecraft orbit is circular and control has constant value on each part of active spacecraft motion. In this case the lengths of the sections of the spacecraft motion are unknown. We need to find the length of each section, their quantity and value of control on each section. The equations of the problem were written in dimensionless form. It simplifies the numerical investigation of the obtained problem. There is a characteristic dimensionless parameter in the phase equations of the problem. This parameter is a combination of dimension variables describing the spacecraft and its orbit. Usually the problems of spaceflight mechanic are solved with the maximum principle. And we have to solve boundary value problems with some kind of shooting method (Newton’s method, gradient descent method etc.) Each shooting method requires initial values of conjugate variables, but we have no analytical formulas to find them. In this paper spacecraft flight trajectories were found with new genetic algorithm. Each gene contains additional parameter which equals to " True" , if the gene forms the control and equals to "False" otherwise. It helps us determine the quantity of spacecraft active motion parts. The input of proposed algorithm does not contain information about conjugate variables. It is well-known that the differential equations of the problem have a partial solution when the spacecraft orbit is circular and control is constant. The genetic algorithm involves this partial solution and its speed is increased. Numerical examples were constructed for two cases: when the difference between angular variables for start and final orientations of the spacecraft orbital plane equals to a few (or tens of) degrees. Final orientation of the spacecraft plane of orbit coincides with GLONASS orbital plane. The graphs of components of the quaternion of orientation of the orbital coordinate system, the longitude of the ascending node, the orbit inclination and optimal control are drawn. Tables were constructed showing the dependence of the value of the quality functional and the time spent on the reorientation of the orbital plane on the maximum length of the active section of motion.

A EROSPACE engineering applications greatly stimulated the development of optimal control theory during the 1950s and 1960s, where the objective was to drive the system states in such a way that some defined cost was minimized. This turned out to have very useful applications in the design of regulators (where some steady state is to be maintained) and in tracking control strategies (where some predetermined state trajectory is to be followed). Among such applications was the problem of optimal flight trajectories for aircraft and space vehicles. Linear optimal control theory in particular has been very well documented and widely applied, where the plant that is controlled is assumed linear and the feedback controller is constrained to be linear with respect to its input. However, the availability of powerful low-cost microprocessors has spurred great advantages in the theory and applications of nonlinear control. The competitive era of rapid technological change, particularly in aerospace exploration, now demands stringent accuracy and cost requirements in nonlinear control systems. This has motivated the rapid development of nonlinear control theory for application to challenging, complex, dynamical real-world problems, particularly those that bear major practical significance in aerospace, marine, and defense industries. Infinite-time horizon nonlinear optimal control (ITHNOC) presents a viable option for synthesizing stabilizing controllers for nonlinear systems by making a state-input tradeoff, where the objective is to minimize the cost given by a performance index. The original theory of nonlinear optimal control dates from the 1960s. Various theoretical and practical aspects of the problem have been addressed in the literature over the decades since. In particular, the continuous-time nonlinear deterministic optimal control problem associated with autonomous (time-invariant) nonlinear regulator systems that are affine (linear) in the controls has been studied by many authors. The long-established theory of optimal control offers quite mature and well-documented techniques for solving this control-affine nonlinear optimization problem, based on dynamic programming or calculus of variations, but their application is generally a very tedious task. Bellman’s dynamic programming approach reduces to solving a nonlinear first-order partial differential equation (PDE), expressed by the Hamilton–Jacobi–Bellman (HJB) equation. The solution to the HJB equation gives the optimal performance/cost value (or storage) function and determines an optimal control in feedback form under some smoothness assumptions. Alternatively, in the classical calculus of variations, optimal control problems can be characterized locally in terms of the Hamiltonian dynamics arising from Pontryagin’s minimum principle. These are the characteristic equations of the HJB PDE, which result in a nonlinear, constrained two-point boundary value problem (TPBVP) that, in general, can only be solved by successive approximation of the optimal control input using iterative numerical techniques for each set of initial conditions. Numerically, even though the nonlinear TPBVP is somewhat easier to solve than the HJBPDE, control signals can only be determined offline and are thus best suited for feedforward control of plants for which the state trajectories are known a priori. Therefore, contrary to the dynamic programming approach, the resultant control law is not generally in feedback form. Open-loop control, however, is sensitive to random disturbances and requires that the initial state be on the optimal trajectory. In contrast, nonlinear optimal feedback has inherent robustness properties (inherent in the sense that it is obtained by ignoring uncertainty and disturbances). The potential difficulty with the HJB approach is that no efficient algorithm is available to solve the PDE when it is nonlinear and the problem dimension is high, making it impossible to derive exact expressions for optimal controls for most nontrivial problems of interest. The optimal can only be computed in special cases, such as linear dynamics and quadratic cost, or very low-dimensional systems. In particular, if the plant is linear time invariant (LTI) and the (infinite-time) performance index is quadratic, then the corresponding HJB equation for this infamous linear-quadratic regulator (LQR) problem reduces to an algebraic Riccati equation (ARE). Contrary to the well-developed and widely applied theory and computational tools for theRiccati equation (for example, see [1]), theHJB equation is difficult, if not impossible, to solve for most practical applications. The exact solution for the optimal control policies is very complex

The integration of a reusable scramjet vehicle as the second stage of a multistage space launch system has the potential to reduce the cost of small-payload orbital launches. This paper determines the maximum payload to orbit trajectory of a multistage rocket–scramjet–rocket system. This trajectory is calculated by formulating the problem as an optimal control problem, and then solving it using the pseudospectral method. Using this method, it is determined that the optimal trajectory for the scramjet stage involves an initial decrease in dynamic pressure, followed by constant-dynamic-pressure flight, and finally a pullup maneuver. This optimal trajectory results in an 8.35% improvement in payload mass to orbit when compared to a constant-dynamic-pressure trajectory with minimum pullup. Furthermore, the optimal pullup maneuver decreases the maximum dynamic pressure experienced by the final rocket stage by 20.3%. The sensitivity of the trajectory is tested by varying the maximum allowable dynamic pressure and the drag produced by the vehicle. A maximum dynamic pressure variation of is shown to produce only and variations in the payload mass. A drag increase of 10% is shown to produce a similar optimal trajectory shape, indicating robustness with variation of the vehicle aerodynamics.

The Trajectory Analysis and Optimization System (TAOS) is software that simulates point--mass trajectories for multiple vehicles. It expands upon the capabilities of the Trajectory Simulation and Analysis program (TAP) developed previously at Sandia National Laboratories. TAOS is designed to be a comprehensive analysis tool capable of analyzing nearly any type of three degree-of-freedom, point-mass trajectory. Trajectories are broken into segments, and within each segment, guidance rules provided by the user control how the trajectory is computed. Parametric optimization provides a powerful method for satisfying mission-planning constraints. Althrough TAOS is not interactive, its input and output files have been designed for ease of use. When compared to TAP, the capability to analyze trajectories for more than one vehicle is the primary enhancement, although numerous other small improvements have been made. This report documents the methods used in TAOS as well as the input and output file formats.

Low-thrust interplanetary spacecraft trajectory optimization poses a uniquely difficult problem to solve because of the inherent nonlinearities of the dynamics and constraints as well as the large size of the search space of possible solutions. Tools currently exist that optimize low-thrust interplanetary trajectories, but these tools are rarely openly available to the public, and when they are available they require multiple interfaces between multiple different packages. The goal of this work is to present a new piece of low-thrust interplanetary spacecraft trajectory optimization software that is open-source and entirely self-contained so that more people can have access to the ability to design interplanetary trajectories. To achieve this goal, a gradient-descent based nonlinear programming method, called the interior point method, was used. The nonlinear programming method was chosen so that results from this work could be compared and contrasted with results from Spacecraft Trajectory Optimization Suite (STOpS), which uses heuristics to iterate towards a solution. Interior point methods are popular because of their ability to handle large amounts of equality and inequality constraints, which is a characteristic that is valuable for low-thrust interplanetary spacecraft trajectories. The software developed, Interior Point Optimizer (IP Optimizer), was then validated against test cases with known solutions to ensure that the software delivered the intended results. Lastly, a constraint satisfaction, a minimum-time, and a maximum-final-mass optimization problem were solved and compared with literature to illustrate the advantages of IP Optimizer and the methods it employs. For the constraint satisfaction problem, IP Optimizer was able to find a solution that exactly satisfied the desired terminal constraints whereas STOpS had an error of 2.29 percent. In this case, IP Optimizer had a reduced runtime of 15 percent compared to STOpS as well. When minimizing time for a spacecraft transfer, IP Optimizer improved upon the solution found by STOpS by 5.3 percent. The speed of convergence for IP Optimizer was almost twice as fast as STOpS for this case. These results show that IP Optimizer is faster than STOpS at converging on a solution and the solution it converges to has a better objective value and more accurately satisfies the terminal constraints than STOpS. Lastly, the maximum-final-mass problem resulted in an objective value that was only 0.5 percent lower than the value found in literature.

Trajectory optimization is an important problem in the field of aircraft design. Effective trajectory optimization is critical to the tactical flight of an aircraft. Based on previous research, this paper further studies trajectory optimization of an unpowered gliding vehicle. The goal is to maximize its range and to analyze the influence of different initial conditions on trajectory optimization. In this paper, the trajectory optimization model involving all kinds of state constraints and control constraints was established and aerodynamic coefficient was calculated by CFD software. Finally Gauss Pseudo-spectral Method program was run to calculate the flight path of the gliding vehicle and to seek trajectory with maximum range. The simulation data shows that results of Gauss Pseudo-spectral Method are realistic and prove its practical operability. The use of Gauss Pseudo-spectral Method for trajectory optimization has a good effect.

The development of fully reusable launch systems has been the topic of many studies since the 1960s. Over the years, several aspects of both so-called single- and two-stage-to-orbit space planes have provided many interesting research topics. Amongst others, the constrained trajectory optimisation has proven to be a challenging subject. In this chapter, an inverse-dynamics approach is combined with trajectory optimisation and analysis, by discretising a representative (vertical-plane) ascent trajectory into a number of flight segments, and by parametrising the guidance in terms of flight-path angle as a function of altitude. When the individual guidance parameters are varied, the effect on performance indices payload mass and integrated heat load can be analysed. This can subsequently lead to a refinement of the trajectory. To do so with limited effort, design-of-experiment techniques are used. It is shown that with this relatively simple simulation scheme, combined with variance analysis and response-surface methodology, the insight in the trajectory dynamics can be increased. Alternatively, this method can be used as refinement to an otherwise (local) optimum trajectory. It is stressed, though, that the application of design of experiments to the ascent-trajectory problem cannot replace numerical optimisation. Finally, the impact of using thrust-vector control as a means to (partially) trim the vehicle shows significant fuel savings and should therefore be included in the optimisation process.

Purpose The purpose of this paper is to improve the positional accuracy, smoothness on motion and productivity of industrial robot through the proposed optimal joint trajectory planning method. Also a new improved algorithm, i.e. non-dominated sorting genetic algorithm-II (NSGA-II) with achievement scalarizing function (ASF) has been proposed to obtain better optimal results compared to previously used optimization methods. Design/methodology/approach The end effector positional errors can be reduced by limiting the uncertainties of dynamic parameter variations like torque rate of joints. The jerk induced in robot joints due to acceleration variations are need to be minimized which otherwise induces vibrations in the manipulator that causes deviation in the encoders. But these lead to a vast increase in total travel time which affects the cost function of trajectory planning. Therefore, these three objectives need to be minimized individually so that an optimal trajectory path can be achieved with minimum positional error. Findings The simulation results have been obtained by running the proposed hybrid NSGA-II with ASF in MATLAB R2017a software. The optimal time intervals have been used to calculate jerk, acceleration and torque values for consecutive points on the trajectory path. From the simulation and experimental results, it can be concluded that the optimization technique could be used effectively for the trajectory planning of six-axis industrial manipulator in the joint space on the basis of minimum time-jerk-torque rate criteria. Originality/value In this paper, a new approach based on hybrid multi-objective optimization technique by combining NSGA-II with ASF has been applied to find the minimal time-jerk- torque rate joint trajectory of a six-axis industrial robot for obtaining higher positional accuracy. The results obtained from the execution of algorithm have been validated through experimentation using Kawasaki RS06L industrial robot for a particular defined path.