A Spintronic Memristor-Based Neural Network With Radial Basis Function for Robotic Manipulator Control Implementation

A radial basis function (RBF) neural network control algorithm can effectively improve the robotic manipulators' performance against a large amount of uncertainty. The adaptive law can be derived by using the Lyapunov method so that the stability of robotic manipulator control system and the weight self-adaptive convergence of RBF neural networks will be guaranteed. Meanwhile, system fluctuations and even overshot phenomenon under every start-up process, which are caused by the system's convergence from the given nonoptimal initial weight value to the optimal weight value, can be avoided by using memristors to remember the optimal weight after the system's first operation. According to the above analysis, this correspondence paper designs a kind of RBF neural network control algorithm based on spintronic memristors, and then analyzes its theoretical derivation process and core design idea. Finally, the system simulation model, which uses a two-link robotic manipulator as control object, is built to prove the algorithm's validity and feasibility. Simulation results show that the proposed algorithm can satisfy the effect of presupposition.

[1]  X. Liu,et al.  Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Mingzhe Liu,et al.  Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm , 2015, Neurocomputing.

[3]  Jun Huang,et al.  Design of Robust Adaptive Neural Switching Controller for Robotic Manipulators with Uncertainty and Disturbances , 2014, Journal of Intelligent & Robotic Systems.

[4]  Ai Hui Tan,et al.  Optimization of Neural Networks Using Variable Structure Systems , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Sangyoon Lee,et al.  Design of an shape memory alloy-actuated biomimetic mobile robot with the jumping gait , 2013 .

[6]  Jinde Cao,et al.  New synchronization criteria for memristor-based networks: Adaptive control and feedback control schemes , 2015, Neural Networks.

[7]  Shital S. Chiddarwar,et al.  Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach , 2010, Eng. Appl. Artif. Intell..

[8]  Shukai Duan,et al.  Memristor-Based Cellular Nonlinear/Neural Network: Design, Analysis, and Applications , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Tingwen Huang,et al.  Complex dynamics of a delayed discrete neural network of two nonidentical neurons. , 2014, Chaos.

[10]  Sangho Shin,et al.  Compact Circuit Model and Hardware Emulation for Floating Memristor Devices , 2013, IEEE Circuits and Systems Magazine.

[11]  Shukai Duan,et al.  Multilayer RTD-memristor-based cellular neural networks for color image processing , 2015, Neurocomputing.

[12]  George Chryssolouris,et al.  On generating the motion of industrial robot manipulators , 2015 .

[13]  Marco Mirolli,et al.  Phasic dopamine as a prediction error of intrinsic and extrinsic reinforcements driving both action acquisition and reward maximization: A simulated robotic study , 2013, Neural Networks.

[14]  Chang Tai Kiang,et al.  Review of Control and Sensor System of Flexible Manipulator , 2015, J. Intell. Robotic Syst..

[15]  L. Chua Memristor-The missing circuit element , 1971 .

[16]  Ana-Maria Cretu,et al.  Soft Object Deformation Monitoring and Learning for Model-Based Robotic Hand Manipulation , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  George A. Rovithakis,et al.  Prescribed Performance Output Feedback/Observer-Free Robust Adaptive Control of Uncertain Systems Using Neural Networks , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Hai Helen Li,et al.  Spintronic Memristor Through Spin-Torque-Induced Magnetization Motion , 2009, IEEE Electron Device Letters.

[19]  Chuandong Li,et al.  Hybrid memristor/RTD structure-based cellular neural networks with applications in image processing , 2013, Neural Computing and Applications.

[20]  Shukai Duan,et al.  Small-world Hopfield neural networks with weight salience priority and memristor synapses for digit recognition , 2015, Neural Computing and Applications.

[21]  Min Wu,et al.  Singularity‐avoiding swing‐up control for underactuated three‐link gymnast robot using virtual coupling between control torques , 2015 .

[22]  Chuandong Li,et al.  Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Ioan Doroftei,et al.  Application of Ni-Ti shape memory alloy actuators in a walking micro-robot , 2014 .

[24]  Shukai Duan,et al.  Exponential Stability of Discrete-Time Delayed Hopfield Neural Networks with Stochastic Perturbations and Impulses , 2012 .

[25]  Jing Ma,et al.  Design of an Adaptive Neural Network Controller for Robot Manipulators , 2013 .