Neural Control of Bimanual Robots With Guaranteed Global Stability and Motion Precision
Citations
498 citations
Cites background from "Neural Control of Bimanual Robots W..."
...However, while the robotic end-effector comes in contact with the environment, it is inevitable that an interaction force will develop between the robot and its environment [40]–[42]....
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Cites background from "Neural Control of Bimanual Robots W..."
..., robot manipulators [23]–[25] and marine vehicles [26], where the prescribed performance controllers were designed in a centralized manner....
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269 citations
Cites methods from "Neural Control of Bimanual Robots W..."
...[38], [39] used NN approximation techniques to compensate the unknown dynamics of both the robot arms and manipulated objects by the Baxter robot....
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References
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Additional excerpts
...The BLFs were originally developed in the nonlinear control community to deal with the state and output constraints [37]–[40]....
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...Inspired by the work [40], an asymmetric time-varying barrier function is constructed for the ith robotic arm as...
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...In [40], an asymmetric time-varying BLF was presented for nonlinear systems in a strict-feedback form....
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685 citations
Additional excerpts
...In order to well approximate the robot dynamics and considering both the accuracy and the computational efficiency, we divide the inputs of RBFNN into two groups, with one group containing [qTi , α̇ T i ] T ∈ R6 and another [qTi , q̇Ti , αTi ]T ∈ R9 , and employ three centers for each input dimension of the NNs, and ended up with totally l1 = 20 412 NN nodes for each NN....
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...1) Radial Basis Function Neural Network (RBFNN) [42]: In this paper, the following RBFNNs are used to approximate a continuous vector function F (Z) = [f1(Z), f2(Z), . . . , fn (Z)]T ∈ Rn , F̂ (Z) = Ŵ T S(Z) (23) where F̂ (Z) ∈ Rn is the estimate of F (Z), Z ∈ ΩZ ⊂ Rq is NN inputs vector, and q denotes the demonstration of the input; Ŵ = [Ŵ1 , Ŵ2 , . . . , Ŵn ] ∈ Rn × l is the estimation of NN optimal weight matrix W ∗, and l is the number of NN nodes....
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...Applying RBFNN described in Section III-C, we see that over a compact set Ωi1 F̂i(Zi) = Ŵ Ti Si(Zi) + εi (29) where Ŵi = [Ŵi,1 , Ŵi,2 , . . . , Ŵi,Ni ] T ∈ Rli ×Ni is the estimation of optimal neural weight matrix W ∗i , and Ŵi,j = [ω̂i,j1 , ω̂i,j1 , . . . , ω̂i,j li ] ∈ Rli , Si(Zi) ∈ Rli is the basis vector function, with li being the NN node number, and εi is the NN construction error satisfying |εi | ε̄i ....
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...1) Radial Basis Function Neural Network (RBFNN) [42]: In this paper, the following RBFNNs are used to approximate a continuous vector function F (Z) = [f1(Z), f2(Z), ....
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...The transient responses such as overshoot, settling time, and final tracking RBFNNs are employed to approximate the unknown dynamics of both the robot arms and the manipulated object....
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670 citations