Hardware neural networks controlled rotational actuators and application to an insect type micro robot are reported in this paper. Millimeter size rotational actuators are fabricated by combining MEMS （Micro Electro ...Hardware neural networks controlled rotational actuators and application to an insect type micro robot are reported in this paper. Millimeter size rotational actuators are fabricated by combining MEMS （Micro Electro Mechanical System） technology and shape memory alloy based artificial muscle wires. The actuator is composed of a pair of disk rotators and each rotor is suspended by four artificial muscle wires that are connected to the silicon frame. The rotational motion is generated by flowing the electrical current to each wire successively. Two actuators of different sizes are fabricated. The large actuator shows the displacement of 0.5 mm at the cycle time of 4 s. The small actuator shows 0.3 mm at 2 s. For controlling the actuator, the hardware neural networks are used. The hardware neural networks are composed of electrical circuits imitating cell bodies, excitatory synapses and inhibitory synapses. Four signal ports are extracted from four pairs of excitatory and inhibitory neurons and they are connected to the actuator. The small actuator is applied to the robot and built in the mid body of the robot. The shaft of the actuator is connected to the link mechanisms that transform the rotational motion to the locomotion. The appearance dimensions of the robot are 4.0, 2.7, 2.5 mm width, length and height. The robot performs forward and backward foot step like insects. The speed is 26.4 mm·min^-1 and the stepping width is 0.88 mm. Also, the robot changes the direction by external trigger pulses.展开更多
Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the curr...Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.展开更多
A novel network control method based on trophaUaxis mechanism is applied to the formation flight problem for multiple un- manned aerial vehicles （UAVs）. Firstly, the multiple UAVs formation flight system based on tr...A novel network control method based on trophaUaxis mechanism is applied to the formation flight problem for multiple un- manned aerial vehicles （UAVs）. Firstly, the multiple UAVs formation flight system based on trophallaxis network control is given. Then, the model of leader-follower formation flight with a virtual leader based on trophallaxis network control is pre- sented, and the influence of time delays on the network performance is analyzed. A particle swarm optimization （PSO）-based formation controller is proposed for solving the leader-follower formation flight system. The proposed method is applied to five UAVs for achieving a ＇V＇ formation, and a series of experimental results show its feasibility and validity. The proposed control algorithm is also a promising control strategy for formation flight of multiple unmanned underwater vehicles （UUVs）, unmanned ground vehicles （UGVs）, missiles and satellites.展开更多
文摘Hardware neural networks controlled rotational actuators and application to an insect type micro robot are reported in this paper. Millimeter size rotational actuators are fabricated by combining MEMS （Micro Electro Mechanical System） technology and shape memory alloy based artificial muscle wires. The actuator is composed of a pair of disk rotators and each rotor is suspended by four artificial muscle wires that are connected to the silicon frame. The rotational motion is generated by flowing the electrical current to each wire successively. Two actuators of different sizes are fabricated. The large actuator shows the displacement of 0.5 mm at the cycle time of 4 s. The small actuator shows 0.3 mm at 2 s. For controlling the actuator, the hardware neural networks are used. The hardware neural networks are composed of electrical circuits imitating cell bodies, excitatory synapses and inhibitory synapses. Four signal ports are extracted from four pairs of excitatory and inhibitory neurons and they are connected to the actuator. The small actuator is applied to the robot and built in the mid body of the robot. The shaft of the actuator is connected to the link mechanisms that transform the rotational motion to the locomotion. The appearance dimensions of the robot are 4.0, 2.7, 2.5 mm width, length and height. The robot performs forward and backward foot step like insects. The speed is 26.4 mm·min^-1 and the stepping width is 0.88 mm. Also, the robot changes the direction by external trigger pulses.
基金Supported by the Postdoctoral Science Foundation of China（ No. 20100480964 ） , the Basic Research Foundation of Central University （ No. HEUCF100104） and the National Natural Science Foundation of China （No. 50909025/E091002）.
文摘Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.
基金supported by the National Natural Science Foundation of China(Grant Nos.61273054,60975072 and 60604009)the National Basic Research Program of China("973"Project)(Grant No.2013CB035503)+1 种基金the Program for New Century Excellent Talents in University of China(Grant No.NCET-10-0021)the Aeronautical Foundation of China(Grant No.20115151019)
文摘A novel network control method based on trophaUaxis mechanism is applied to the formation flight problem for multiple un- manned aerial vehicles （UAVs）. Firstly, the multiple UAVs formation flight system based on trophallaxis network control is given. Then, the model of leader-follower formation flight with a virtual leader based on trophallaxis network control is pre- sented, and the influence of time delays on the network performance is analyzed. A particle swarm optimization （PSO）-based formation controller is proposed for solving the leader-follower formation flight system. The proposed method is applied to five UAVs for achieving a ＇V＇ formation, and a series of experimental results show its feasibility and validity. The proposed control algorithm is also a promising control strategy for formation flight of multiple unmanned underwater vehicles （UUVs）, unmanned ground vehicles （UGVs）, missiles and satellites.
