A Dual Neural Network as an Identifier for a Robot Arm

This article focuses on the adaptive synchronization for a class of fractional-order coupled neural networks (FCNNs) with output coupling. The model is new for output coupling item in the FCNNs that treat FCNNs with state coupling as its particular case. Novel adaptive output controllers with logarithm quantization are designed to cope with the stability of the fractional-order error systems for the first attempt, which is also an effective way to synchronize fractional-order complex networks. Based on fractional-order Lyapunov functionals and linear matrix inequalities (LMIs) method, sufficient conditions rather than algebraic conditions are built to realize the synchronization of FCNNs with output coupling. A numerical simulation is put forward to substantiate the applicability of our results.

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“…as the synchronization manifold for system (1). Also, the synchronization state is governed by the following equation:…”

Section: Problem Statementmentioning

confidence: 99%

Adaptive Synchronization of Fractional-Order Output-Coupling Neural Networks via Quantized Output Control

Bao

Park

Cao

2021

IEEE Trans. Neural Netw. Learning Syst.

104

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“…A 3-DoF robot arm has j = 1, 2, 3, as such, substituting DH parameters in (17), three homogeneous transformation matrices are obtained:…”

Section: Dynamicsmentioning

confidence: 99%

“…where M ∈ ℝ 3×3 is the inertia matrix, U ∈ ℝ 3 is the potential energy vector, and F τ f ∈ ℝ 3 is the friction force vector. Using the methodology detailed in [15][16][17] to obtain the torque for the j-th link, ( 24) is transformed to the following form…”

Section: : ð23þmentioning

confidence: 99%

n‐th Order Sensor Output to Control k‐DoF Serial Robot Arms

Alvarez-Rodríguez

Lecona

2021

Journal of Sensors

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Currently, zero-order sensors are commonly used as positioning feedback for the closed-loop control in robotics; thus, in order to expand robots’ control alternatives, other paths in sensing should be investigated more deeply. Conditions under which the n -th order sensor output can be used to control k -DoF serial robot arms are formally studied in this work. In obtaining the mentioned control conditions, the Pickard-Lindeloff theorem has been used to prove the existence and uniqueness of the robot’s mathematical model solution with n order sensory systems included. To verify that the given conditions and claims guarantee controllability for both continuous-based and variable structure-based systems, two types of control strategies are used in obtaining simulation results: the conventional PID control and a second-order Sliding Mode control.

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Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure

Fei

2018

IEEE Trans. Neural Netw. Learning Syst.

195

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In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.

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A Dual Neural Network as an Identifier for a Robot Arm

Cited by 3 publications

References 18 publications

Adaptive Synchronization of Fractional-Order Output-Coupling Neural Networks via Quantized Output Control

Adaptive Synchronization of Fractional-Order Output-Coupling Neural Networks via Quantized Output Control

n‐th Order Sensor Output to Control k‐DoF Serial Robot Arms

Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure

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