Control design based on dead-zone and leakage adaptive laws for artificial swarm mechanical systems

ABSTRACT We consider the control design of artificial swarm systems with emphasis on four characteristics. First, the agent is made of mechanical components. As a result, the motion of each agent is subject to physical laws that govern mechanical systems. Second, both nonlinearity and uncertainty of the mechanical system are taken into consideration. Third, the ideal agent kinematic performance is treated as a desired d’Alembert constraint. This in turn suggests a creative way of embedding the constraint into the control design. Fourth, two types of adaptive robust control schemes are designed. They both contain leakage and dead-zone. However, one design suggests a trade-off between the amount of leakage and the size of dead-zone, in exchange for a simplified dead-zone structure.

[1]  M. Corless,et al.  Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems , 1981 .

[2]  M. Tomizuka,et al.  Appropriate Sensor Placement for Fault-Tolerant Lane-Keeping Control of Automated Vehicles , 2007, IEEE/ASME Transactions on Mechatronics.

[3]  Steven V. Viscido,et al.  Self-Organized Fish Schools: An Examination of Emergent Properties , 2002, The Biological Bulletin.

[4]  Swagatam Das,et al.  Stability and chaos analysis of a novel swarm dynamics with applications to multi-agent systems , 2014, Eng. Appl. Artif. Intell..

[5]  Yisheng Zhong,et al.  Containment analysis and design for high-order linear time-invariant singular swarm systems with time delays , 2012, Proceedings of the 31st Chinese Control Conference.

[6]  X. Dong,et al.  Time-varying formation control for high-order linear swarm systems with switching interaction topologies , 2014 .

[7]  Masashi Shiraishi,et al.  Collective Patterns of Swarm Dynamics and the Lyapunov Analysis of Individual Behaviors , 2015 .

[8]  W. E. Schmitendorf,et al.  Analytical dynamics of discrete systems , 1977 .

[9]  P. Olver Nonlinear Systems , 2013 .

[10]  Dewey H. Hodges,et al.  Inverse Dynamics of Servo-Constraints Based on the Generalized Inverse , 2005 .

[11]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[12]  Kevin M. Passino,et al.  Swarm Stability and Optimization , 2011 .

[13]  A. Mogilner,et al.  Mathematical Biology Mutual Interactions, Potentials, and Individual Distance in a Social Aggregation , 2003 .

[14]  K. Passino,et al.  A class of attractions/repulsion functions for stable swarm aggregations , 2004 .

[15]  Andry Tanoto,et al.  Analysis and design of human-robot swarm interaction in firefighting , 2008, RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication.

[16]  Ye-Hwa Chen,et al.  Artificial Swarm System: Boundedness, Convergence, and Control , 2008 .

[17]  R. Kalaba,et al.  Analytical Dynamics: A New Approach , 1996 .