共查询到20条相似文献,搜索用时 15 毫秒
1.
Chih-Hui Chiu 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2010,14(6):627-638
In this study, an adaptive output recurrent cerebellar model articulation controller (AORCMAC) is investigated for a nonlinear
system. The proposed AORCMAC has superior capability to the conventional cerebellar model articulation controller in efficient
learning mechanism and dynamic response. The dynamic gradient descent method is adopted to online adjust the AORCMAC parameters.
Moreover, the analytical method based on a Lyapunov function is proposed to determine the learning-rates of AORCMAC so that
the stability of the system can be guaranteed. Furthermore, the variable optimal learning-rates are derived to achieve the
best convergence of tracking error. Finally, the effectiveness of the proposed control system is verified by the several simulation
and experimental results. Those results show that the favorable performance can be obtained by using the proposed AORCMAC. 相似文献
2.
Chih-Hui Chiu 《Neural computing & applications》2010,19(8):1153-1164
In this study, a model-free self-tuning output recurrent cerebellar model articulation controller (SORCMAC) is investigated
to control a wheeled inverted pendulum (WIP). Since the proposed SORCMAC captures the system dynamics, it has superior capability
compared to the conventional cerebellar model articulation controller in terms of an efficient learning mechanism and dynamic
response. The dynamic gradient descent method is also adopted to adjust the SORCMAC parameters online. Moreover, an analytical
method based on a Lyapunov function is proposed to determine the learning rates of the SORCMAC so that the convergence of
the system can be guaranteed. Finally, the effectiveness of the proposed control system is verified by simulations of the
WIP control. Simulation results show that the WIP can move forward and backward stably with uncertainty disturbance by using
the proposed SORCMAC. 相似文献
3.
Sun Wei Wang Cong Bu Dexu Liu Shengnan Wu Baoqiang Ouyang Minghua 《International Journal of Control, Automation and Systems》2012,10(2):430-436
This paper presents a novel online learning visual servo controller integrating the FCMAC with proportion controller for the
control of position of manipulator end-effector. Since the FCMAC has good learning capability and fast learning speed, and
can save much computer memory space by fuzzy processing of input space division and memory unit activation, it is used to
develop an adaptive control law by learning the relationship between the image feature errors and manipulator input, and the
aim of online learning of the FCMAC is to minimize the output of proportion controller. Furthermore, the FCMAC has no need
for models of robot manipulator and image feature extraction, so that the capability of proposed controller for tasks under
uncertain environment can be improved. Finally, the proposed controller is proved to be effective by the experiment, and compared
with BP neural network. 相似文献
4.
Chih-Min Lin Ya-Fu Peng 《Neural Networks, IEEE Transactions on》2005,16(3):636-644
An adaptive cerebellar model articulation controller (CMAC) is proposed for command to line-of-sight (CLOS) missile guidance law design. In this design, the three-dimensional (3-D) CLOS guidance problem is formulated as a tracking problem of a time-varying nonlinear system. The adaptive CMAC control system is comprised of a CMAC and a compensation controller. The CMAC control is used to imitate a feedback linearization control law and the compensation controller is utilized to compensate the difference between the feedback linearization control law and the CMAC control. The online adaptive law is derived based on the Lyapunov stability theorem to learn the weights of receptive-field basis functions in CMAC control. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. Then the adaptive CMAC control system is designed to achieve satisfactory tracking performance. Simulation results for different engagement scenarios illustrate the validity of the proposed adaptive CMAC-based guidance law. 相似文献
5.
Adaptive recurrent cerebellar model articulation controller for linear ultrasonic motor with optimal learning rates 总被引:1,自引:0,他引:1
In this study, an adaptive recurrent cerebellar model articulation controller (ARCMAC) is investigated for the motion control of linear ultrasonic motor (LUSM). The proposed ARCMAC has superior capability to the conventional cerebellar model articulation controller in efficient learning mechanism and dynamic response. The dynamic gradient descent method is adopted to online adjust the ARCMAC parameters. Moreover, the analytical method based on a Lyapunov function is proposed to determine the learning-rates of ARCMAC so that the stability of the system can be guaranteed. Furthermore, the variable optimal learning-rates are derived to achieve the fastest convergence of tracking error. Finally, the effectiveness of the proposed control system is verified by the experiments of LUSM motion control. Experimental results show that high-precision tracking response can be achieved by using the proposed ARCMAC. 相似文献
6.
Robust sliding-mode control applied to a 5-link biped robot 总被引:2,自引:0,他引:2
Spyros Tzafestas Mark Raibert Costas Tzafestas 《Journal of Intelligent and Robotic Systems》1996,15(1):67-133
In this paper the application of robust control to a 5-link biped robotic model is investigated through the sliding mode approach, and compared to pure computed torque control. The biped consists of five links, namely the torso and two links in each leg. These links are connected via four (two hip and two knee) rotating joints which are considered to be friction-free and driven by independent d.c. motors. The locomotion of the biped is assumed to be constrained on the sagittal plane. The paper provides a full derivation of the biped dynamic model (single-leg support phase, biped-in-the-air phase) and an outline of the computed torque and sliding mode control algorithms. The simulation results were derived with two sets of parameters (one of which corresponds to a human-sized biped) and several degrees of parametric uncertainty (from 10% to 200%). In all cases the results obtained through the sliding mode control were much better than those obtained with the computed torque control. This superiority was shown to become stronger as the degree of uncertainty and the size of the biped increases. 相似文献
7.
Shun-Feng Su Zne-Jung Lee Yan-Ping Wang 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2006,36(1):203-208
In this paper, the online learning capability and the robust property for the learning algorithms of cerebellar model articulation controllers (CMAC) are discussed. Both the traditional CMAC and fuzzy CMAC are considered. In the study, we find a way of embeding the idea of M-estimators into the CMAC learning algorithms to provide the robust property against outliers existing in training data. An annealing schedule is also adopted for the learning constant to fulfill robust learning. In the study, we also extend our previous work of adopting the credit assignment idea into CMAC learning to provide fast learning for fuzzy CMAC. From demonstrated examples, it is clearly evident that the proposed algorithm indeed has faster and more robust learning. In our study, we then employ the proposed CMAC for an online learning control scheme used in the literature. In the implementation, we also propose to use a tuning parameter instead of a fixed constant to achieve both online learning and fine-tuning effects. The simulation results indeed show the effectiveness of the proposed approaches. 相似文献
8.
模糊CMAC及其在机器人轨迹跟踪控制中的应用 总被引:7,自引:1,他引:7
小脑模型关节控制器(CMAC)具有结构简单,学习快速的优点,但是它的空间划分方式不能在线进行调整,影响了其自适应能力的提高.本文将模糊理论引入CMAC,提出了一种能够反映人类小脑认知的模糊性和连续性的模糊小脑模型关节控制器(FCMAC).该控制器对CMAC的空间划分方式进行了模糊化处理,可通过BP学习算法对CMAC的空间划分方式进行在线调整,大大提高了CMAC的自适应能力.所提出的FCMAC被应用于机器人的轨迹跟踪控制系统以克服机器人系统中非线性和不确定性因素的影响.仿真实验结果表明,所提FCMAC与传统的CMAC相比性能上有了很大的改善. 相似文献
9.
The application of the hybrid self-organizing fuzzy (SOF) PID controller to a multiinput multioutput nonlinear biped robot
is studied in this article. The SOF-PID controller was initially studied by H.B. Kazemian in 1998. Actually, his SOF-PID controller
has limits. The supervisory capacity of the SOF-PID controller can adjust only certain kinds of parameters. Here the hybrid
SOF-PID controller is introduced to tune some kinds of parameters, and it was tested on a MIMO biped robot. In the experiment,
the hybrid SOF-PID controller shows a better performance than the SOF-PID.
This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February
4–6, 2005 相似文献
10.
11.
《Robotics and Autonomous Systems》2014,62(1):68-80
This paper deals with the use of 0-flat normal form to control a 7 d.o.f-biped robot to follow a specified trajectory. Sufficient geometrical conditions are given to transform the studied nonlinear systems into a 0-flat normal form and determine the flat outputs. On the other hand, a controller design strategy is proposed to control the walking robot. Simulations are carried out using Matlab. The results obtained are very convincing and show the usefulness of such a method in studying highly non-linear systems and designing control laws to drive them. 相似文献
12.
研究单站点传送带给料生产加工站(conveyor-serviced production station,CSPS)系统的前视(look-ahead)距离最优控制问题,以提高系统的工作效率.论文运用半Markov决策过程对CSPS优化控制问题进行建模.考虑传统Q学习难以直接处理CSPS系统前视距离为连续变量的优化控制问题,将小脑模型关节控制器网络的Q值函数逼近与在线学习技术相结合,给出了在线Q学习及模型无关的在线策略迭代算法.仿真结果表明,文中算法提高了学习速度和优化精度. 相似文献
13.
14.
This study aims to propose a more efficient control algorithm for the chaotic system synchronization. In this study, a novel wavelet cerebellar model articulation controller (WCMAC) is proposed, which incorporates the wavelet decomposition property with a cerebellar model articulation controller (CMAC). This WCMAC is a generalization network; in some special cases, it can be reduced to a wavelet neural network, a neural network and a conventional CMAC. Then, an adaptive wavelet cerebellar model articulation control system (AWCCS) is proposed to synchronize a unified chaotic system. In this AWCCS, WCMAC is the main controller utilized to mimic a perfect controller and the parameters of WCMAC are online adjusted by the derived adaptive laws; and a compensation controller is designed to dispel the residual of the approximation error for achieving $ H^{\infty } $ robust performance. The derived AWCCS is then applied to the chaotic system synchronization control. Finally, the effectiveness of the proposed control system is demonstrated through simulation results. 相似文献
15.
Behnam Dadashzadeh M.J. Mahjoob M. Nikkhah Bahrami Chris Macnab 《Advanced Robotics》2014,28(4):231-244
This work formulates the active limit cycles of bipedal running gaits for a compliant leg structure as the fixed point of an active Poincare map. Two types of proposed controllers stabilize the Poincare map around its active fixed point. The first one is a discrete linear state feedback controller designed with appropriate pole placement. The discrete-time control first uses purely constant torques during stance and flight phase, then discretizes each phase into smaller constant-torque intervals. The other controller is an invariant manifold based chaos controller: a generalized Ott, Grebogi and Yorke controller having a linear form and a nonlinear form. Both controllers can stabilize active running gaits on either even or sloped terrains. The efficiency of these controllers for bipedal running applications are compared and discussed. 相似文献
16.
Sangbum Park Youngjoon Han Hernsoo Hahn 《International Journal of Control, Automation and Systems》2009,7(1):75-84
This paper presents a new balance control scheme for a biped robot. Instead of using dynamic sensors to measure the pose of
a biped robot, this paper uses only the visual information of a specific reference object in the workspace. The zero moment
point (ZMP) of the biped robot can be calculated from the robot’s pose, which is measured from the reference object image
acquired by a CCD camera on the robot’s head. For balance control of the biped robot a servo controller uses an error between
the reference ZMP and the current ZMP, estimated by Kalman filter. The efficiency of the proposed algorithm has been proven
by the experiments performed on both flat and uneven floors with unknown thin obstacles.
Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Jae-Bok Song. This work was supported by
the Korea Research Foundation Grant funded by the Korean Government (MOEHRD). This research was supported by the MKE(The Ministry
of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA(Institute
for Information Technology Advancement) (IITA-2008-C1090-0803-0006).
Sangbum Park received the B.S. and M.S. degrees from Electronic Engineering of Soongsil University, Seoul, Korea, in 2004 and 2006 respectively.
He has been with School of Electronic Engineering, Soongsil University since 2006, where he is currently pursuing a Ph.D.
His current research interests include biped walking robot, robotics vision.
Youngjoon Han received the B.S., M.S. and Ph.D. degrees in Electronic Engineering from Soongsil University, Seoul, Korea, in 1996, 1998,
and 2003, respectively. He is currently an Assistant Professor in the School of Electornic Engineering at Soongsil University.
His research interests include robot vision system, and visual servo control.
Hernsoo Hahn received the B.S. and M.S. degrees in Electronic Engineering at Soongsil University and Younsei University, Korea in 1982
and 1983 respectively. He received the Ph.D. degree in Computer Engineering from University of Southern California in 1991,
and became an Assistant Professor at the School Electroncis Engneering in Soongsil University in 1992. Currently, he is a
Professor. His research interests include application of vision sensors to mobile robots and measurement systems. 相似文献
17.
A genetic algorithm based robust learning credit assignment cerebellar model articulation controller
In this paper, a novel approach of genetic algorithm based robust learning credit assignment cerebellar model articulation controller (GCA-CMAC) is proposed. The cerebellar model articulation controller (CMAC) is a neurological model, which has an attractive property of learning speed. However, the distributions of errors into the addressed hypercubes of CMAC are not proportional to their credibility and may cause unacceptable learning performance. The credit assignment CMAC (CA-CMAC) can solve this problem by using the creditability of hypercubes that the calculated errors are assigned proportional to the inverse of learning times. Afterward, the obtained learning times can be optimized by genetic algorithm (GA) to increase its accuracy. In this paper, the proposed algorithm is to combine credit assignment ideas and GA to provide accurate learning for CMAC. Moreover, we embed the robust learning approach into the GCA-CMAC and dynamically adjust the learning constant for training data with noise or outliers. From simulation results, it shows that the proposed algorithm outperforms other CMACs. 相似文献
18.
《Advanced Robotics》2013,27(4):415-435
This paper describes position-based impedance control for biped humanoid robot locomotion. The impedance parameters of the biped leg are adjusted in real-time according to the gait phase. In order to reduce the impact/contact forces generated between the contacting foot and the ground, the damping coefficient of the impedance of the landing foot is increased largely during the first half double support phase. In the last half double support phase, the walking pattern of the leg changed by the impedance control is returned to the desired walking pattern by using a polynomial. Also, the large stiffness of the landing leg is given to increase the momentum reduced by the viscosity of the landing leg in the first half single support phase. For the stability of the biped humanoid robot, a balance control that compensates for moments generated by the biped locomotion is employed during a whole walking cycle. For the confirmation of the impedance and balance control, we have developed a life-sized humanoid robot, WABIAN-RIII, which has 43 mechanical d.o.f. Through dynamic walking experiments, the validity of the proposed controls is verified. 相似文献
19.
Chan-Soo Park Taesin Ha Joohyung Kim Chong-Ho Choi 《International Journal of Control, Automation and Systems》2010,8(2):339-351
This paper presents an effective and systematic trajectory generation method, together with a control method for enabling a biped robot to walk upstairs. The COG (center of gravity) trajectory is generated by the VHIPM (virtual height inverted pendulum mode) for the horizontal motion and by a 6th order polynomial for the vertical motion; an ankle compliance control (ACC) is also added into the robot control. The proposed methods are evaluated by simulations as well as being implemented in a robot for the performance verification. The results show that the proposed methods can generate stable motions when walking upstairs, and these can significantly reduce the zero moment point (ZMP) errors compared with other methods, enabling the robot to walk up steeper stairs. 相似文献
20.
This paper presents a new partial discharge (PD) pattern recognition method based on the cerebellar model articulation controller (CMAC). CMAC is an adaptive system by which defect types for partial discharge can be identified by referring to a table rather than by mathematical solution of simultaneous equations. CMAC maps input features of partial discharge into an input vector which is used to address a memory where the appropriate defect types are stored. Five types of defect models are well-designed on the base of investigation of many power apparatus failures. A PD detector is used to measure the raw three-dimension (3D) PD patterns, from which the fractal dimension, the lacunarity, and the mean discharges of phase windows are extracted as PD features. These critical features form the cluster domains of defect types. Using the characteristics of self-learning, association, and generalization, like the cerebellum of human being, the proposed CMAC-based pattern recognition scheme enables a powerful, straightforward, and efficient pattern recognition method. Moreover, the CMAC has the advantages of higher accuracy, shorter learning times, and noise tolerance, which are useful in recognizing the PD patterns of electrical apparatus. To demonstrate the effectiveness of the proposed method, comparative studies using a multilayer neural network (MNN) and K-means method are conducted on 200 sets of field-test PD patterns with high accuracy and high tolerance in noise interference. 相似文献