Model Identification and Control Design for a Humanoid Robot

He, Wei; Ge, Wenjun; Li, Yunchuan; Liu, Yan Jun; Yang, Chenguang; Sun, Changyin

doi:10.1109/tsmc.2016.2557227

Cited by 138 publications

(59 citation statements)

References 86 publications

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“…EKF algorithm is a kind of prediction correction algorithm in pattern recognition field. 31 According to an estimated object, the state and measurement equation are setting up and the nonlinear model was linearized to realize the state vector's recursive estimation including the forecasting process and calibration process as follows:…”

Section: -25mentioning

confidence: 99%

Fuzzy control for vehicle status estimation considering roll stability and its application in target recognition of automobile cruise system

Chen

Zhang

Yue-Zhou

et al. 2017

Advances in Mechanical Engineering

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In order to resolve the goal confusion or missing on curve road, a target recognition method on automobile cruise system is put forward based on filtering and fuzzy control theory. A vehicle model with 3 degrees of freedom on the yaw rate, sideslip angle, and body roll angle is discretized and an extended Kalman filter is proposed for estimating the road curvature. For the key target on the straight road, a multi-object changing-lane fuzzy controller is presented to prejudge the dangerous changing-lane objects. For the recognition of key target on the curve road, a recognition model is set up to calculate the lateral offset between the estimated road curvature radius and the target trajectory radius. In order to validate the feasibility of the key target recognition method, the simulation cases are given for multi-objects on the curve road and straight road adopting Carsim and MATLAB/Simulink co-simulation. The simulation results show that designed fuzzy controller has good robustness for prejudging the dangerous changing-lane objects on straight road. The extended Kalman filter has realized the efficient estimation on road curvature and the recognition model has identified the key target accurately to prevent the goal confusion or missing on curve road. The research results can offer the theory reference for improving or perfecting the control performances of automobile intelligent cruise system.

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Section: -25mentioning

confidence: 99%

Fuzzy control for vehicle status estimation considering roll stability and its application in target recognition of automobile cruise system

Chen

Zhang

Yue-Zhou

et al. 2017

Advances in Mechanical Engineering

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“…The adaptive control method for robots has been studied in a considerable number of works [14]- [16], which could be used in the situations of unknown parameters and time-varying parameters in the robot model [17], [18]. Time-varying delays and uncertainties of the robot model have been studied together with adaptive control in [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Teleoperation Control Based on Combination of Wave Variable and Neural Networks

Yang

Wang

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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312

169

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Abstract-In this paper, a novel control scheme is developed for a teleoperation system, combining the radial basis function (RBF) neural networks (NNs) and wave variable technique to simultaneously compensate for the effects caused by communication delays and dynamics uncertainties. The teleoperation system is set up with a TouchX joystick as the master device and a simulated Baxter robot arm as the slave robot. The haptic feedback is provided to the human operator to sense the interaction force between the slave robot and the environment when manipulating the stylus of the joystick. To utilize the workspace of the telerobot as much as possible, a matching process is carried out between the master and the slave based on their kinematics models. The closed loop inverse kinematics (CLIK) method and RBF NN approximation technique are seamlessly integrated in the control design. To overcome the potential instability problem in the presence of delayed communication channels, wave variables and their corrections are effectively embedded into the control system, and Lyapunov based analysis is performed to theoretically establish the closed-loop stability. Comparative experiments have been conducted for a trajectory tracking task, under the different conditions of various communication delays. Experimental results show that in terms of tracking performance and force reflection, the proposed control approach shows superior performance over the conventional methods.

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“…Since Deepmind's AlphaGo trounced one of the world's best human Go players in 2016, there has been explosive interest in the field of advanced intelligent systems (AIS), which have the ability to deal with intellectual tasks of real life problems . For example, a variety of concepts including autonomous vehicles, digital assistants, humanoid robots, and brain‐like computers, as listed in Figure a, are actively being introduced for state‐of‐the‐art AIS demonstration. To realize a human‐like AIS that is interactive with our environments, it is important to implement capabilities of cognizing and learning unstructured data (e.g., visual information, natural language, human emotion, and physiological signals) utilizing artificial intelligence (AI) technology …”

Section: Introductionmentioning

confidence: 99%

Unconventional Inorganic‐Based Memristive Devices for Advanced Intelligent Systems

Sung

Kim

et al. 2019

Adv Materials Technologies

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The latest progresses in software engineering such as cloud computing, big data analysis, and machine learning have accelerated the emergence of advanced intelligent systems (AIS). However, the current computing system has significant challenges in dealing with unstructured data (e.g., image, voice, physiological signals) because the von‐Neumann bottleneck induces latency and power consumption issues. Neuromorphic computing, which imitates the behaviors of neuron and synapse within the biological neural network, is considered a promising solution beyond von‐Neumann architecture, since its collocated structure of processor and memory enables parallel processing of unstructured data with remarkable efficiency. Memristors are considered as next‐generation nonvolatile memory devices due to fast speed, low power, and excellent scalability. However, a low reliability and leakage current issues remain as obstacle to the commercialization of memristors. Memristive devices have been widely investigated as a strong candidate for artificial synapses since their resistance modulation characteristics under electrical stimulus are analogous to the plasticity of the brain synapse. Although emulation of synaptic behavior by single memristor cells is demonstrated by many researchers, the development of fully functional memristive neural network requires further investigations. This paper introduces the recent advances and developments in the field of inorganic‐based unconventional memristive devices for future AIS applications.

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Model Identification and Control Design for a Humanoid Robot

Cited by 138 publications

References 86 publications

Fuzzy control for vehicle status estimation considering roll stability and its application in target recognition of automobile cruise system

Fuzzy control for vehicle status estimation considering roll stability and its application in target recognition of automobile cruise system

Teleoperation Control Based on Combination of Wave Variable and Neural Networks

Unconventional Inorganic‐Based Memristive Devices for Advanced Intelligent Systems

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