Convex Model Predictive Control for Rocket Vertical Landing

Wang, Cong; Song, Zhengyu

doi:10.23919/chicc.2018.8483147

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Since MPC can handle the constraints of the landing mission, it is widely used in the trajectory of unmanned aerial vehicles (UAVs) [39,40], rockets [41,42] and robots [43,44]. Among them, [44] designed an MPC to solve the problem of point-to-point trajectory planning, thereby controlling the manipulator to catch a thrown tennis ball.…”

Section: Mpc Design For Autonomous Landing Of a Quadrotormentioning

confidence: 99%

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Guo

Tang²,

Wang

et al. 2022

Aerospace

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Landing on a moving platform is an essential requirement to achieve high-performance autonomous flight with various vehicles, including quadrotors. We propose an efficient and reliable autonomous landing system, based on model predictive control, which can accurately land in the presence of external disturbances. To detect and track the landing marker, a fast two-stage algorithm is introduced in the gimbaled camera, while a model predictive controller with variable sampling time is used to predict and calculate the entire landing trajectory based on the estimated platform information. As the quadrotor approaches the target platform, the sampling time is gradually shortened to feed a re-planning process that perfects the landing trajectory continuously and rapidly, improving the overall accuracy and computing efficiency. At the same time, a cascade incremental nonlinear dynamic inversion control method is adopted to track the planned trajectory and improve robustness against external disturbances. We carried out both simulations and outdoor flight experiments to demonstrate the effectiveness of the proposed landing system. The results show that the quadrotor can land rapidly and accurately even under external disturbance and that the terminal position, speed and attitude satisfy the requirements of a smooth landing mission.

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Section: Mpc Design For Autonomous Landing Of a Quadrotormentioning

confidence: 99%

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Guo

Tang²,

Wang

et al. 2022

Aerospace

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“…Using t = (t f − t 0 )τ + t 0 , the time is mapped to the interval [0, 1]. Taking τ as a new independent variable and t f as an augmented control variable is an effective way to solve the problem of free terminal time when using the SCP algorithm (Wang C and Song, 2018a). The algorithm is also applicable to the nonlinear equality constraints of the angular velocity and quaternion (Eq.…”

Section: Online Trajectory Planning Based On Convex Optimizationmentioning

confidence: 99%

“…The accuracy and sparsity of the discretization method are also key factors that ensure the accuracy and effectiveness of the algorithm. The discretization methods for differential equations of motion include mainly: (1) the trapezoidal method (TM) (Wang C and Song, 2018a), where the estimation of the state variable differential at each discrete point is related to only the state and control variables of two adjacent discrete points; (2) the state transition method (STM) (Açıkmeşe and Ploen, 2007), where the estimation is related to the state and control variables of all previous discrete points; (3) the pseudospectral method (PM) (Sagliano and Mooij, 2018;Sagliano, 2018aSagliano, , 2018bWenzel et al, 2018;Malyuta et al, 2019), where the estimation is related to the control and state variables at all other discrete points. From the approximation accuracy of the discrete problem to the original problem, more information means higher accuracy, so the sequence is PM > STM > TM.…”

Section: Online Trajectory Planning Based On Convex Optimizationmentioning

confidence: 99%

“…At the same time, the mass of the rocket during landing significantly changes, the larger slenderness ratio demands a stringent attitude constraint, and the terminal landing accuracy is within several meters of the landing site. Today, studies of AGMs in terms of the recovery of rockets have focused mainly on the convex optimization algorithm during the PD phase (Wang JB and Cui, 2018;Wang C and Song, 2018a;Liu, 2019), where the remaining time is relatively short (around 10 s) and the ability to adjust is limited. In summary, the multi-phase simultaneous planning AGM is a key direction of future research (Ma et al, 2018b).…”

Section: Introductionmentioning

confidence: 99%

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Survey of autonomous guidance methods for powered planetary landing

Song

Theil

Seelbinder

et al. 2020

Front Inform Technol Electron Eng

Self Cite

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This paper summarizes the autonomous guidance methods (AGMs) for pinpoint soft landing on celestial surfaces. We first review the development of powered descent guidance methods, focusing on their contributions for dealing with constraints and enhancing computational efficiency. With the increasing demand for reusable launchers and more scientific returns from space exploration, pinpoint soft landing has become a basic requirement. Unlike the kilometer-level precision for previous activities, the position accuracy of future planetary landers is within tens of meters of a target respecting all constraints of velocity and attitude, which is a very difficult task and arouses renewed interest in AGMs. This paper states the generalized three-and six-degree-of-freedom optimization problems in the powered descent phase and compares the features of three typical scenarios, i.e., the lunar, Mars, and Earth landing. On this basis, the paper details the characteristics and adaptability of AGMs by comparing aspects of analytical guidance methods, numerical optimization algorithms, and learning-based methods, and discusses the convexification treatment and solution strategies for non-convex problems. Three key issues related to AGM application, including physical feasibility, model accuracy, and real-time performance, are presented afterward for discussion. Many space organizations, such as those in the United States, China, France, Germany, and Japan, have also developed free-flying demonstrators to carry out related research. The guidance methods which have been tested on these demonstrators are briefly introduced at the end of the paper.

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“…For the online guidance problem of the rocket vertical landing phase, a successive convexification + MPC guidance algorithm is proposed by Ref. [33]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Guidance of Rocket Vertical Landing Phase Based on MPC Framework

Long

et al. 2022

International Journal of Aerospace Engineering

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For the vertical landing process of reusable rockets, the landing accuracy is likely to be affected by disturbances and faults during flight. In this paper, a fault-tolerant guidance method based on the MPC framework is put forward. First, we propose a piecewise guidance algorithm that combines a trajectory optimization algorithm based on convex optimization with the MPC framework. With the fast trajectory optimization algorithm and the MPC framework that recursively introduces the real-time state, this algorithm forms a robust closed loop. Then, we design an integrated guidance, navigation, and control (GNC) system to enhance the fault tolerance and robustness of the guidance method. Simulation experiments verify that this method is fault-tolerant to various fault conditions including navigation system failures, control system failures, drag coefficient deviations, and atmospheric density deviations. This guidance method is robust enough to overcome disturbances and faults, and it has great potential for online use.

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Convex Model Predictive Control for Rocket Vertical Landing

Cited by 6 publications

References 12 publications

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Survey of autonomous guidance methods for powered planetary landing

Fault-Tolerant Guidance of Rocket Vertical Landing Phase Based on MPC Framework

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