Multi-Agent Cooperative Control Consensus: A Comparative Review
Abstract
:1. Introduction
2. Preliminaries
3. Theoretical Progress in Consensus
3.1. Convergence Analysis for Time-Invariant Topology
3.2. Convergence Analysis for Complex Dynamic Systems
3.2.1. Switching Network
3.2.2. Synchronization Network
3.3. Convergence Speed in Finite Time
3.4. Heterogeneous Agents
4. Convergence Constraints due to Practical Limitations
4.1. Computation, Execution, Control and Communication Delays
4.2. Quantization & Sample-Data Consensus
5. Application of Consensus in Multi-Agent Network
- Multi-agent flocking means to achieve some common group objectives by interacting with each other.
- Swarm is an approach of multi-agent system that takes inspiration from social animals that exhibit a self-organized behavior. Through local interactions and simple rules, swarm agents focus flexible, scalable and robust collective behaviors for the coordination of multi-agent system.
5.1. Rendezvous
5.2. Formation Control
- In position-based control, agents sense their own position with respect to the global coordinate system.
- In displacement-based control, agents sense the relative position of its neighbor with respect to the global coordinate system.
- In distance-based control, agents sense the relative position of its neighbor with respect to own local coordinate system.
6. Discussion
6.1. Consensus
- Existing control strategies for multi-agent consensus and formation focus on simple system dynamics using basic connectivity assumption and Laplacian matrix, so higher order dynamics or nonlinear dynamics is still needed to be investigated.
- Using heterogeneous agents, more work is required in the field of consensus for more complicated nonlinear dynamic so that each agent can choose the best responses based on its own objectives.
6.2. Formation
- Global stability properties for general rigid and persistent formation are yet to be investigated (Only triangle formation is done so far).
- For formation producing the network topology is assumed to be undirected which is not applicable to many practical applications.
- More research efforts on distance-based formation with moving leader is needed.
6.3. Obstacle Avoidance
- Multi-agent consensus for complex obstacles is still to be investigated, where the task connectivity preservation and collision avoidance issues are important.
7. Conclusions
Conflicts of Interest
References
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Continuous | Discrete | |
---|---|---|
Dynamics | ||
Key Matrix | ||
Eigen Value |
Position-Based | Displacement-Based | Distance-Based | |
---|---|---|---|
Sensed Variables | Positions of agents | Relative positions of neighbors | Relative positions of neighbors |
Controller Variables | Positions of agents | Relative positions of neighbors | Inter-agent distance |
Coordinate System | A global coordinate system | Orientation aligned local coordinate systems | Local Coordinate systems |
Interaction Topology | Usually not required | Connectedness or existence of spanning tree | Rigidity or persistence |
Sensing Capabilities Formation Control | Strengths | Bottlenecks | References |
---|---|---|---|
Position-based | • Easy to implement • Simple mechanism | Costly because GPS system is required | [86,87,88] |
Displacement-based | • Spanning tree required for directed network • Connectedness required for undirected network | Orientation aligned local coordinate system | [20,89,90,92] |
Distance-based | • Less sensing capabilities required • Less global information required | Complicated because system is non-linear | [93,94] |
Time-Varying Formation Control | Control Objective | Methodologies | References |
---|---|---|---|
Formation Producing | Pre-specified pattern without a group reference | Matrix theory, Lyapunov, Graph rigidity, Receding horizon approach, Leaderless flocking, Inverse agreement problem, Circulation formation | [96,97,98,99] |
Formation Tracking | Pre-specified pattern with a particular group reference | Matrix theory, Lyapunov, Gradient-based function, Variable structural based law | [100,101,102,103] |
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Gulzar, M.M.; Rizvi, S.T.H.; Javed, M.Y.; Munir, U.; Asif, H. Multi-Agent Cooperative Control Consensus: A Comparative Review. Electronics 2018, 7, 22. https://doi.org/10.3390/electronics7020022
Gulzar MM, Rizvi STH, Javed MY, Munir U, Asif H. Multi-Agent Cooperative Control Consensus: A Comparative Review. Electronics. 2018; 7(2):22. https://doi.org/10.3390/electronics7020022
Chicago/Turabian StyleGulzar, Muhammad Majid, Syed Tahir Hussain Rizvi, Muhammad Yaqoob Javed, Umer Munir, and Haleema Asif. 2018. "Multi-Agent Cooperative Control Consensus: A Comparative Review" Electronics 7, no. 2: 22. https://doi.org/10.3390/electronics7020022
APA StyleGulzar, M. M., Rizvi, S. T. H., Javed, M. Y., Munir, U., & Asif, H. (2018). Multi-Agent Cooperative Control Consensus: A Comparative Review. Electronics, 7(2), 22. https://doi.org/10.3390/electronics7020022