Zero-propellant Maneuver Research Articles

Integration planning of gimbal angle and attitude motion for zero propellant maneuver under attitude and control moment gyroscope constraints

Zero propellant maneuver is an advanced concept of large spacecraft attitude control that has recently been applied to International Space Station. The objective of this paper is the zero propellant maneuver path planning of space station under attitude and control moment gyroscope constraints. First, a multi-constraint model of zero propellant maneuver with single gimbal control moment gyroscopes is established. Then, an integrated planning algorithm of gimbal angle and attitude motion is proposed: Local Control Differential Evolution. The algorithm can simultaneously handle attitude constraints and control moment gyroscope constraints to obtain an attitude path that satisfies the attitude constraints and a gimbal angle path that is far from singularity and saturation. In addition, the algorithm combines the local control algorithm with the global differential evolution algorithm. The local control algorithm based on the potential function idea can better deal with local attitude obstacle constraints, and the global optimization algorithm based on the improved differential evolution algorithm can perform global gimbal angle optimization. Finally, simulation results show that the method is effective in dealing with both attitude and control moment gyroscope constraints.

Rapid Path Planning for Zero Propellant Maneuvers Based on Flatness

A rapid path planning (RPP) method that fast generates suboptimal zero propellant maneuver (ZPM) paths was proposed previously, and to further enhance the planning performance, the differential flatness of the model is studied in this paper. For the crucial issue of singularity in flatness planning, the equivalence between the state transformation singularity and the control transformation singularity is proved for the feedback-linearizable multi-input multi-output (MIMO) affine system, which simplifies the singularity investigation of the ZPM problem. The question of searching suitable flat outputs for specific types of large-angle maneuvers is raised before path planning. Four sets of flat output candidates, which are biased to different types of maneuvers, are examined, and it is shown that only the large-angle yaw maneuver is achievable. The essentially singular attitudes (ESAs), for which no outputs could linearize the model, are presented, and it is found that the large-angle pitch or roll maneuver path planning easily encounters singularity in the flat output space no matter what output functions are constructed. To the regular mission of large-angle yaw maneuvers, the flatness is utilized in the RPP method. A simultaneous reconstruction method is proposed to solve the large amount of implicit equations in the planning, and rapid planning for the ZPM path is achieved.

Rapid Path Planning for Zero-Propellant Maneuvers

AbstractThe zero-propellant maneuver (ZPM) is an advanced space station large-angle attitude maneuver technique using control momentum gyroscopes (CMGs), and rapid path planning (RPP) enhances its performance. This paper proposes a novel trajectory optimization technique—the RPP method—to generate the ZPM path. The core of the RPP method is the reconstruction of a continuous-time feasible solution upon the rapid computation of the nonlinear programming (NLP) problem. The method maintains the sparsity of the transcribed NLP and achieves the continuous-time feasibility of the solution with a small number of discretization points. Thus, rapid acquisition of a suboptimal solution may be achieved. The performance of the RPP method is demonstrated by showing that continuous-time feasible ZPM paths with high performance index may be obtained rapidly.

Multi-objective optimization of zero propellant maneuver using hybrid programming

Zero-propellant maneuver is an advanced concept of attitude control. The multi-objective optimization of zero-propellant maneuver using single gimbal control momentum gyroscopes is investigated in this paper. First, the multi-objective optimization model of zero-propellant maneuver is established. Then, a hybrid approach combining pseudospectral method and physical programming is proposed. Finally, the effectiveness of this proposed hybrid method has been validated. It is shown that, using the hybrid programming method, the indexes of the trade-off solution are all desirable. Moreover, the pareto-solution set can be obtained when changing the structure of the preference functions of physical programming, which is examined by 90° and 180° attitude maneuver mission examples. This hybrid programming method provides an effective approach for multi-objective optimal large angle attitude maneuver mission to meet different engineering requirement.

Technical Note: Space Station Zero Propellant Maneuver Path Planning Considering SGCMG Saturation and Singularity

The zero-propellant maneuver is an advanced concept of the spacecraft attitude control. The problem of zero-propellant maneuver using single gimbal control moment gyroscopes is researched in this paper. The saturation and singularity problems of single gimbal control moment gyroscopes are analyzed, and the performance indexes to measure saturation and singularity are proposed. The path planning model considering the gimbal steering law is established using modified rodrigues parameter. Then, a hybrid approach combining the pseudospectral method and direct shooting method is proposed to solve the nonlinear path planning problem with complicated constraints. Finally, the numerical simulation is executed to validate the effectiveness of the proposed method. It is shown that an optimized attitude maneuver path satisfying the saturation and singularity constraints can be obtained and the zero-propellant maneuver using single gimbal control moment gyroscopes can be successfully achieved.

Multi-objective Optimization of Zero Propellant Spacecraft Attitude Maneuvers

The zero propellant maneuver (ZPM) is an advanced space station, large angle attitude maneuver technique, using only control momentum gyroscopes (CMGs). Path planning is the key to success, and this paper studies the associated multi-objective optimization problem. Three types of maneuver optimal control problem are formulated: (i) momentum-optimal, (ii) time-optimal, and (iii) energy-optimal. A sensitivity analysis approach is used to study the Pareto optimal front and allows the tradeoffs between the performance indices to be investigated. For example, it is proved that the minimum peak momentum decreases as the maneuver time increases, and the minimum maneuver energy decreases if a larger momentum is available from the CMGs. The analysis is verified and complemented by the numerical computations. Among the three types of ZPM paths, the momentum-optimal solution and the time-optimal solution generally possess the same structure, and they are singular. The energy-optimal solution saves significant energy, while generally maintaining a smooth control profile.

Pseudospectral Motion Planning for Autonomous Vehicles

A COMMON task for autonomous vehicles is motion planning. Discipline-based design of motion planning algorithms have led to the development and evolution of different techniques to solve specific problems. For instance, the artificial–potential–function technique [1] is a popular method for the motion planning of unmanned ground vehicles (UGV) and robotic manipulators [1–3]. Although this technique has been used for over 30 years, it suffers from the possibility of the vehicle not achieving its goal and difficulties in accommodating various environmental constraints [4]. To overcome such issues, recent developments in motion planning algorithms place heavy emphasis on so-called sampling-based planning techniques such as probabilistic road maps, rapidly exploring random trees, and expansive space trees to name a few [5– 9]. These methods use probabilistic means of connecting the initial configuration to thefinal configuration thereby enabling an improved capacity to achieve the goal and a capability to generate initial feasible paths. In all these techniques, the initial feasible paths do not automatically incorporate vehicle dynamics; hence, path-following control techniques are needed to satisfy the physics of themotion [9]. This is one reason why nonholonomic constraints play such a crucial role in constrained control techniques that are designed to serve pathfollowing systems. There is no doubt that optimal control theory is the most natural framework for solving motion planning problems; however, solving optimal control problems has historically been considered difficult due to the twin curses of dimensionality and complexity. These difficulties are exacerbated in the presence of state constraints; hence, an obstacle-cluttered environment becomes a substantially more difficult problem under the framework of optimal control theory [10,11]. Nonetheless, it is possible to solve simplified motion planning problems wherein the cost function is quadratic or the environment is obstacle free. Because the motion planning techniques developed in robotics applications are unsuitable for flight vehicles, such as launch and reentry, for example, aerospace problems have motivated the development of efficient optimal control algorithms. In aerospace applications, satisfaction of the dynamical constraints is exceedingly important; hence, trajectory and control become fundamentally intertwined. On the other hand, aerospace problems do not have the vast number of path constraints that are common in an obstacle-cluttered environment. In recent years, paradigm-changing advancements have taken place in computational optimal control that challenge conventional wisdom. For instance, onboard the International Space Station, Bedrossian et al. [12] discovered and implemented a revolutionary momentum-dumping approach that they call a zero-propellant maneuver. This discovery was made possible by an application of pseudospectral methods to solve a real-life challenging optimal control problem [13]. Other applications of such advancements are discussed in [14,15] and include the ground test of a revolutionary attitude control concept for the NPSAT1 spacecraft. Motivated by these advancements, we apply pseudospectral methods to develop motion planning algorithms for autonomous vehicles characterized by nonlinear dynamical constraints, an obstacle-cluttered environment, and a need to generate solutions in real time. We consider the problem of generating optimal trajectories for generic autonomous vehicles. Shapes of arbitrary number, size, and configuration are modeled in the form of path constraints in the resulting optimal control problem. The method is tested under various obstacle environments on different platforms such as sea surface vehicles, ground vehicles, and aerial vehicles. The optimality of the computed trajectories is verified by way of the necessary conditions. We show that it is possible to do motion planning for different problems under the unified framework of optimal control and pseudospectral methods [16–19].