מושגי ליבה
The author presents a two-layer distributed learning control scheme for multi-robot systems with uncertain dynamics, achieving synchronization and learning of nonlinear dynamics in a decentralized manner.
תקציר
The paper introduces a novel approach to composite synchronization and learning control in multi-agent robotic manipulator systems. It addresses challenges of uncertain dynamics, proposing a two-layer strategy for estimation and control. The method is environment-independent, applicable to various settings like underwater or space. The stability and convergence of the system are rigorously analyzed using the Lyapunov method. Numerical simulations validate the effectiveness of the proposed scheme. The identified nonlinear dynamics can be saved and reused when the system restarts.
סטטיסטיקה
Mi11 = mi1l2ic1 + mi2(l2i1 + l2ic2 + 2li1lic2 cos(qi2)) + Ii1 + Ii2,
Mi12 = mi2(l2ic2 + li1lic2 cos(qi2)) + Ii2,
Mi21 = mi2(lic2 + li1lic2 cos(qi2)) + Ii2,
Mi22 = mi2l2ic2 + Ii2,
Ci11 = -mi2li1lic2 ˙qi^sin(qi^),
Ci12 = -mi^li^lic^sin(q^) (where i represents subscript),
Ci21 = mi^li^lic^sin(q^),
Ci22 = 0,
gi11 = (mi1lic^+mi^li)gcos(q)+mi^licgcos(q+q).
ציטוטים
"The proposed distributed learning control scheme fills a gap in existing literature by achieving both synchronization and accurate identification/learning of completely nonlinear uncertain dynamics."
"Our control architecture is environment-independent, adaptable to various settings like underwater or space where system dynamics are typically uncertain."
"The stability and parameter convergence of the closed-loop system are rigorously analyzed using the Lyapunov method."