CACLA-Based Trajectory Tracking Guidance for RLV in Terminal Area Energy Management Phase

This paper focuses on the trajectory tracking guidance problem for the Terminal Area Energy Management (TAEM) phase of the Reusable Launch Vehicle (RLV). Considering the continuous state and action space of this guidance problem, the Continuous Actor–Critic Learning Automata (CACLA) is applied to construct the guidance strategy of RLV. Two three-layer neuron networks are used to model the critic and actor of CACLA, respectively. The weight vectors of the critic are updated by the model-free Temporal Difference (TD) learning algorithm, which is improved by eligibility trace and momentum factor. The weight vectors of the actor are updated based on the sign of TD error, and a Gauss exploration is carried out in the actor. Finally, a Monte Carlo simulation and a comparison simulation are performed to show the effectiveness of the CACLA-based guidance strategy.

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Trajectory tracking for RLV terminal area energy management phase based on LQR

Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference ◽

10.1109/cgncc.2014.7007564 ◽

2014 ◽

Author(s):

Bowen Zheng ◽

Zixuan Liang ◽

Qingdong Li ◽

Zhang Ren

Keyword(s):

Energy Management ◽

Trajectory Tracking ◽

Terminal Area

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Determination of guidance boundaries for terminal area energy management system of tran-atmospheric vehicle

Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788) ◽

10.1109/wcica.2004.1343755 ◽

2004 ◽

Author(s):

Hu Meng-Quan ◽

Zhang Mei-Zhong

Keyword(s):

Energy Management ◽

Management System ◽

Energy Management System ◽

Terminal Area

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Intelligent Energy Management Strategy Based on an Improved Reinforcement Learning Algorithm With Exploration Factor for a Plug-in PHEV

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2021.3085710 ◽

2021 ◽

pp. 1-11

Author(s):

Xinyou Lin ◽

Kuncheng Zhou ◽

Liping Mo ◽

Hailin Li

Keyword(s):

Reinforcement Learning ◽

Energy Management ◽

Management Strategy ◽

Learning Algorithm ◽

Energy Management Strategy ◽

Reinforcement Learning Algorithm

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An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) ◽

10.1109/ddcls49620.2020.9275050 ◽

2020 ◽

Author(s):

Dongdong Yuan ◽

Yankai Wang

Keyword(s):

Adaptive Control ◽

Trajectory Tracking ◽

Control Algorithm ◽

Tracking Method ◽

Unmanned Vehicle ◽

Model Free ◽

Vehicle Trajectory ◽

Improved Model ◽

Adaptive Control Algorithm

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Research of onboard trajectory generation technology of Terminal Area Energy Management for RLVs

2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics ◽

10.1109/isscaa.2008.4776400 ◽

2008 ◽

Author(s):

Jun Zhang ◽

Yimin Huang ◽

Liaoni Wu

Keyword(s):

Energy Management ◽

Trajectory Generation ◽

Terminal Area ◽

Generation Technology

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Adaptive PD Sliding Mode Control For 4 DOF SCARA Variant

10.32920/ryerson.14656593.v1 ◽

2021 ◽

Author(s):

Manjeet Tummalapalli

Keyword(s):

Sliding Mode Control ◽

Trajectory Tracking ◽

High Speed ◽

Sliding Mode ◽

Adaptive Controller ◽

Degree Of Freedom ◽

Tracking Performance ◽

Advantages And Disadvantages ◽

Model Free ◽

Adaptive Law

This project proposes a new SCARA variant with 4 degree of freedom. The proposed variant is achieved by swapping joint 2 and joint 3 of the standard SCARA robots. An adaptive controller is defined based on the advantages and disadvantages of PD, and SMC controllers.The purpose of the project is to understand the dynamics of the variant and to track the performance for trajectories. Simulations for tracking performance are carried under linear and circular trajectories. The variant is studied over the three controllers; PD, PD-SMC and A-PD-SMC. The variant under the adaptive controller is most efficient in terms of tracking performance and the control inputs to the system. The system is simulated under high speed and with the influence of friction at the joints. The control gains are held constant for both the trajectories and hence the controller is able to perform good under changing trajectories. Due to the use of the adaptive law, the system is at the ease of implementation and since no priori knowledge if the system is needed, it is model free. Therefore, the proposed adaptive PD-SMC has proven to provide good, robust trajectory tracking.

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Model-Free Trajectory Tracking Control of Two-Particle Magnetic Microrobot

IEEE Transactions on Nanotechnology ◽

10.1109/tnano.2018.2815978 ◽

2018 ◽

Vol 17 (4) ◽

pp. 697-700 ◽

Cited By ~ 20

Author(s):

Lidong Yang ◽

Qianqian Wang ◽

Li Zhang

Keyword(s):

Trajectory Tracking ◽

Tracking Control ◽

Trajectory Tracking Control ◽

Model Free

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Event-triggered discrete extended state observer–based model-free controller for quadrotor position and attitude trajectory tracking

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/09596518211055362 ◽

2021 ◽

pp. 095965182110553

Author(s):

Dingxin He ◽

Haoping Wang ◽

Yang Tian ◽

Konstantin Zimenko

Keyword(s):

Trajectory Tracking ◽

Signal Transmission ◽

State Observer ◽

Extended State Observer ◽

Extended State ◽

Model Free ◽

Model Free Controller ◽

Quadrotor System ◽

Static Threshold ◽

Event Triggered

In this article, an event-triggered discrete extended state observer–based model-free controller is developed for the position and attitude trajectory tracking of a quadrotor with uncertainties and external disturbances. The referred event-triggered discrete extended state observer–based model-free controller is composed of two event-triggered mechanisms, ultra-local model-based discrete extended state observer and proportional-derivative sub-controller. To reduce system output signal transmission, the event-triggered mechanism of output signal which owns dynamic and static threshold is designed. Based on event-triggered output signals, the discrete extended state observer is constructed to obtain the estimations of state values which are utilized as controller’s variables and to compensate for the lumped disturbances. The proportional-derivative sub-controller is adopted to guarantee the convergence of trajectory tracking error. To decrease control input signal transmission, the event-triggered mechanism of input signal that processes static threshold is constructed. Moreover, the stability analysis of overall quadrotor system with the proposed control strategy is investigated using Lyapunov theorem and the Zeno behavior is avoided. Finally, corresponding control scheme for quadrotor system is structured and the numerical comparative simulation and co-simulation experiment are given to demonstrate the effectiveness and performance of the proposed approach.

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