Distributed adaptive-neural-network continuous tracking control method of multi-robot system
A multi-robot and neural network technology, applied in the field of multi-robot system distributed adaptive neural network continuous tracking control, can solve the problems of multi-robot system parameter uncertainty, external interference, etc.
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specific Embodiment approach 1
[0087] combine figure 1 and Figure 5 Describe the multi-robot system distributed adaptive neural network continuous tracking control method in this embodiment. First, introduce the related technologies applied in this embodiment:
[0088] 1. Euler-Lagrange model:
[0089] According to the Euler-Lagrange model: M i ( q i ) q · · i + C i ( q i , q · i ) q · i + G i ( q i ) = ...
specific Embodiment approach 2
[0147] Different from the specific embodiment 1, the Euler-Lagrange model described in step 1 satisfies antisymmetry and boundedness in the multi-robot system distributed adaptive neural network continuous tracking control method of this embodiment: is an antisymmetric matrix, given any vector x i ∈R p Have The boundedness refers to the existence of normal constants and make where I p is a p×p order identity matrix.
[0148] Prove that the observer is bounded
[0149] Since the navigator is dynamic, according to the navigator's time-varying velocity formula and time-varying trajectory formula, v is a time variable, so v(t-τ)≠v(t). Therefore, substituting the time-varying velocity formula (6) and the time-varying trajectory formula (7) into the control formula (8) of the distributed observer can get: η ‾ · = A η ‾ · ...
Embodiment 1
[0170] Embodiment carried out according to specific embodiment 2: a directed communication network composed of five two-degree-of-freedom manipulator robot systems, wherein numbers 1 to 4 are trackers, and number 5 is a navigator, such as figure 2 shown.
[0171] The Euler-Lagrange dynamic equation of each follower is as follows:
[0172] M i ( q i ) q · · i + C i ( q i , q · i ) q · i + G i ( q i ...
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