Trajectory control of electro-hydraulic position servo system using improved PSO-PID controller

Design, Modeling, and Control of Biomimetic Fish Robot: A Review

Research status of bionic amphibious robots: A review

Control and Optimization of a Bionic Robotic Fish Through a Combination of CPG model and PSO

This letter presents an experimental study on how different body flapping patterns affect the performances of fish cruising. First, a biomimetic robot fish is designed and built as the experimental platform, which mimics the skeleton structure and the muscle arrangement of real fish. Moreover, an improved Central Pattern Generator (CPG) is developed to generate different patterns, which are characterized by four control parameters: (1) the amplitude, (2) the frequency, (3) the time ratio between the beating phase and half cycle, (4) the shape parameter. Then, a number of experiments are conducted to investigate the thrust, the recoil, the cruising speed and the swimming efficiency. Based on the experimental results, following conclusions can be drawn: (1) Fish cruising follows the traveling wave model proposed in Lighthill's Elongated Body Theory. This model offers a balance among the thrust, the recoil and the swimming speed, which results in a high efficiency. (2) The time asymmetry of the body flapping patterns reduces the thrust. (3) The triangular pattern offers the smallest recoil and the cambering sinusoidal pattern gives the largest thrust. These findings provide better understandings on how fish swims and can be used as guidelines for designing the body flapping patterns for robot fish.

An Experimental Study on the Fish Body Flapping Patterns by Using a Biomimetic Robot Fish

Motion Control and Motion Coordination of Bionic Robotic Fish: A Review

A bionic robotic fish has great potential application prospect. High maneuverability swimming control of a bionic robotic fish has been one of the research hotspots in the robotic fish field. In this paper, an iterative learning method has been proposed to solve the trajectory tracking control problem of robotic fish swimming. First, a dynamic model of the multi-joint bionic robotic fish is established. By considering a three-joint robotic fish as an example, the unified expression of the dynamic equation of the three-joint bionic robotic fish is obtained by Lagrange method. Second, the iterative learning controller for controlling the bionic robotic fish is designed. Then the convergence of the iterative learning controller is proved. Finally, the trajectory tracking control simulation experiment based on iterative learning is conducted. The simulation results show that the trajectory tracking control method based on iterative learning for a bionic robotic fish is effective.

Trajectory tracking control of a bionic robotic fish based on iterative learning

The invention discloses a building energy consumption prediction method and system based on depth reinforcement learning, which comprises: collecting building energy consumption historical data, simultaneously collecting building area, building permanent population quantity, building permanent population consumption level and weather condition data of building location The collected data samplesare grouped and input into the deep reinforcement learning network model according to the obtained training samples to train and save the network model to optimize the state action value function Finally, the prediction samples are inputted into the deep reinforcement learning network model to predict building energy consumption The invention adopts the method of combining the convolution neuralnetwork in the depth learning and the Q learning in the reinforcement learning to realize the energy consumption prediction of the building, Compared with the traditional prediction method, the deepreinforcement learning network based on convolution neural network and Q learning algorithm can reduce the amount of data, reduce the storage requirements of data, improve the efficiency of data use and speed up the efficiency of data processing

Method and system for predicting building energy consumption based on depth reinforcement learning

Controlling a central pattern generator (CPG) network to generate multi-mode signals is a key issue for a self-propelled bionic robotic fish. In this paper, a robotic fish oriented hierarchical control framework combining CPGs with spiking neural networks (SNN) is proposed. Specifically, as a movement mode generator, the CPG network comprises a weak coupling chain of phase oscillators. The output signals of the CPG network serve to regulate locomotion modes of joints of robotic fish. Meanwhile, the CPG network receives signals from the SNN. In particular, the SNN serves as a higher controller, receiving sensor signals and output mode commands to the lower generator. The entire control procedure is similar to one of human movements. Simulation results on diversified swimming modes verify the feasibility of the hierarchical control framework integrating the CPGs with SNN.

Yanlu Zhang

Papers

Motion Control and Motion Coordination of Bionic Robotic Fish: A Review

Control and Optimization of a Bionic Robotic Fish Through a Combination of CPG model and PSO

Trajectory tracking control of a bionic robotic fish based on iterative learning

Method and system for predicting building energy consumption based on depth reinforcement learning

Locomotion Control of Robotic Fish with a Hierarchical Framework Combining Spiking Neural Networks and CPGs