Data-Driven Robust Approximate Optimal Tracking Control for Unknown General Nonlinear Systems Using Adaptive Dynamic Programming Method

By Huaguang Zhang, Lili Cui, Xin Zhang, Yanhong Luo. IEEE Transactions on Neural Networks, 2011

583 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/6ea90d78b129bec70f61329a69545562dfb6fb64

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Derong Liu
2. Ding Wang
3. Qinglai Wei
4. Frank L. Lewis
5. Hamidreza Modares
6. Haibo He
7. Biao Luo
8. Huaguang Zhang

In Computational Theory and Mathematics

1. Derong Liu
2. Ding Wang
3. Qinglai Wei
4. Hamidreza Modares
5. Frank L. Lewis
6. Haibo He
7. Biao Luo
8. Huaguang Zhang

In Control and Systems Engineering

1. Derong Liu
2. Ding Wang
3. Hamidreza Modares
4. Qinglai Wei
5. Frank L. Lewis
6. Haibo He
7. Huaguang Zhang
8. Biao Luo

Citations in the corpus

• Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning (2014, methodology): “The existing RL solutions to the optimal tracking [17]–[22] employs the dynamic inversion concept to obtain the feedforward control term a priori and then find the optimal feedback control term using RL techniques.”
• Adaptive Critic Nonlinear Robust Control: A Survey (2017, methodology): “How to combine data-based approach [46], [54], [55], [76], [78], [80], [81], [93], [105]–[109] with event-triggered”
• Reinforcement learning for control: Performance, stability, and deep approximators (2018, background): “…(e.g., certainty equivalence) in order to infer stability in an appropriate sense, such as local, set, asymptotic, exponential or semi-global stability, as well as uniform ultimate boundedness (Zhang et al., 2011; Lewis et al., 2012; Yang et al., 2014; G¨orges, 2017; Pos-toyan et al., 2017).”
• Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning (2014, methodology): “In order to attain the required knowledge of the system dynamics, in Zhang et al. (2011), a plant model was first identified and then an RLbased optimal tracking controller was synthesized using the identified model.”
• Data-Based Techniques Focused on Modern Industry: An Overview (2015, methodology): “For nonlinear control issue, a data-driven robust approximate optimal tracking controller is proposed via adaptive dynamic programming method [55].”
• Leader-Based Optimal Coordination Control for the Consensus Problem of Multiagent Differential Games via Fuzzy Adaptive Dynamic Programming (2015, methodology): “Therefore, our scheme has greater advantage for multiagent systems, compared with the dual network method in [23], [39], and [44].”
• A Clustering-Based Graph Laplacian Framework for Value Function Approximation in Reinforcement Learning (2014, background): “critic designs (ACDs) [14]–[16] have received lots of research”
• Model-Free Optimal Tracking Control via Critic-Only Q-Learning (2016, methodology): “In [49], a prior model identification procedure was conducted for nonlinear continuous-time systems, and then model-based adaptive methods were used for optimal tracking control design.”
• Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems (2014, methodology): “Actually, for all the policy iteration algorithms of ADP, including [17] and [43]–[47], the initial admissible control law is necessary to implement their algorithms.”
• Adaptive Dynamic Programming and Adaptive Optimal Output Regulation of Linear Systems (2016, background): “The ADP-based optimal trackers are also designed in [16], [45], [46], wherein the feedforward controllers are obtained by dynamic inversion, which requires not only the invertibility of input matrix but knowing or firstly identifying the complete system dynamics.”