Least Square Policy Iteration for IBVS based Dynamic Target Tracking

Srivastava, Raunak; Lima, Rolif; Das, Kaushik; Maity, Arnab

doi:10.1109/icuas.2019.8798242

Cited by 16 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors have not discussed the quantization of the velocity and the position in their work. In the work of [37], image-based visual serving has been proposed using Kalman filtering and RL. Their work has shown the importance of using velocity error in the reward function and the effectiveness of asymmetric rewards.…”

Section: Literature Surveymentioning

confidence: 99%

Twin Delayed Deep Deterministic Policy Gradient-Based Target Tracking for Unmanned Aerial Vehicle With Achievement Rewarding and Multistage Training

et al. 2022

View full text Add to dashboard Cite

show abstract

Section: Literature Surveymentioning

confidence: 99%

Twin Delayed Deep Deterministic Policy Gradient-Based Target Tracking for Unmanned Aerial Vehicle With Achievement Rewarding and Multistage Training

et al. 2022

View full text Add to dashboard Cite

show abstract

“…PI solution evaluates and improves a given strategy in an iterative manner [25]- [27]. The evaluation of the policy can be done using approaches such as least squares (LS) and recursive LS (RLS) [27], [28]. An off-policy RL approach is considered to solve the Algebraic Riccati Equation (ARE) in [29].…”

Section: Introductionmentioning

confidence: 99%

An Online Model-Following Projection Mechanism Using Reinforcement Learning

Abouheaf,

Hashim,

Mayyas

et al. 2023

Preprint

View full text Add to dashboard Cite

In this paper, we propose a model-free adaptive learning solution for a model-following control problem. This approach employs policy iteration, to find an optimal adaptive control solution. It utilizes a moving finite-horizon of modelfollowing error measurements. In addition, the control strategy is designed by using a projection mechanism that employs Lagrange dynamics. It allows for real-time tuning of derived actor-critic structures to find the optimal model-following strategy and sustain optimized adaptation performance. Finally, the efficacy of the proposed framework is emphasized through a comparison with sliding mode and high-order model-free adaptive control approaches.

show abstract

“…An adaptive Fuzzy-RL mechanism is adopted to control flocking motion of a swarm of robots in [42]. Regression models such as iterative and batch least squares are employed to implement the PI solutions [37,43]. The adaptive approaches are adopted to control underactuated vehicles and distributed generation sources [44,45].…”

Section: Introductionmentioning

confidence: 99%