Leader-Based Optimal Coordination Control for the Consensus Problem of Multiagent Differential Games via Fuzzy Adaptive Dynamic Programming

By Huaguang Zhang, Jilie Zhang, Guanghong Yang, Yanhong Luo. IEEE transactions on fuzzy systems, 2015

472 citations recorded by the source corpus.

In this paper, a new online scheme is presented to design the optimal coordination control for the consensus problem of multiagent differential games by fuzzy adaptive dynamic programming, which brings together game theory, generalized fuzzy hyperbolic model (GFHM), and adaptive dynamic programming. In general, the optimal coordination control for multiagent differential games is the solution of the coupled Hamilton-Jacobi (HJ) equations. Here, for the first time, GFHMs are used to approximate the solutions (value functions) of the coupled HJ equations, based on policy iteration algorithm. Namely, for each agent, GFHM is used to capture the mapping between the local consensus error and local value function. Since our scheme uses the single-network architecture for each agent (which eliminates the action network model compared with dual-network architecture), it is a more reasonable architecture for multiagent systems. Furthermore, the approximation solution is utilized to obtain the optimal coordination control. Finally, we give the stability analysis for our scheme, and prove the weight estimation error and the local consensus error are uniformly ultimately bounded. Further, the control node trajectory is proven to be cooperative uniformly ultimately bounded.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d43ff3655729e13527934c3a387115581affa4ab

How researchers see this paper

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

In Artificial Intelligence

1. Derong Liu
2. Ding Wang
3. Haibo He
4. Yanjun Liu
5. Yanhong Luo
6. Huaguang Zhang
7. He Jiang
8. Geyang Xiao

In Computational Theory and Mathematics

1. Derong Liu
2. Ding Wang
3. Shaocheng Tong
4. Haibo He
5. Yanjun Liu
6. Yanhong Luo
7. Huaguang Zhang
8. He Jiang

In Electrical and Electronic Engineering

1. Derong Liu
2. Shaocheng Tong
3. Ding Wang
4. Yanjun Liu
5. Yanhong Luo
6. Huaguang Zhang
7. He Jiang
8. Geyang Xiao

Citations in the corpus

• A Survey on Energy Internet: Architecture, Approach, and Emerging Technologies (2018, background): “Therefore, under the circumstance, operating distributed coordinated control and applying multiagent consensus algorithm [76]–[78] are necessary to deal with these influences.”
• Adaptive Critic Nonlinear Robust Control: A Survey (2017, background): “Some improved structures are also proposed, such as the goal representation ADP [41]–[44] and fuzzy ADP [44], [45].”
• Data-Driven Optimal Consensus Control for Discrete-Time Multi-Agent Systems With Unknown Dynamics Using Reinforcement Learning Method (2017, methodology): “Following the idea of the previous work in [38], [40], and [42], the local performance indices, also called local cost functions, are defined as follows:”
• Neural Networks-Based Adaptive Finite-Time Fault-Tolerant Control for a Class of Strict-Feedback Switched Nonlinear Systems (2019, methodology): “The function uncertainties are often managed by neural networks (NNs) [47]–[55], FLSs [56]–[60], or generalized fuzzy hyperbolic models [61], [62], and the robust methods are usually employed to cope with the structure uncertainties.”
• Distributed Cooperative Optimal Control for Multiagent Systems on Directed Graphs: An Inverse Optimal Approach (2015, background): “In [30], consensus problem of multiagent differential games of nonlinear systems based on the optimal coordination control was solved via fuzzy adaptive dynamic programming approach [31].”
• A Multiagent-Based Consensus Algorithm for Distributed Coordinated Control of Distributed Generators in the Energy Internet (2015, background): “has drawn much attention due to its flexibility and computational efficiency [14], [15].”