SLIDING MODE CONTROL USING RADIAL BASIC FUNCTION NETWORK (SMCRBFN)

and action space. RL-based solutions to the continuous-time optimal control problem have been given in Doya (Doya (2000). The main advantage of using RL for solving optimal Robust Control, Theory and Applications 198 control problems comes from the fact that a number of[r]

feedback to set up directly the desired closed loop response in time domain. The most notable attribute in using sliding mode control is the low sensitivity to disturbances and parameter variations (Utkin, Guldner, & Shi, 2009), since uncertainty conditions are co[r]

2. Dynamical collapse: Unlike traditional control techniques that seek asymptotic convergence, HOSM achieves finite time convergence in systems with arbitrary relative degree, just as classical SMC achieves the same result for the system with relative degree one. This is much more than an aca[r]

operation of an MLP network is synchronous, i.e., given an input vector, it is propagatedto the output by multiplying by the weights of each layer, applying the activation function(the model of each neuron of the network includes a non-linear activation function, being th[r]

the Lyapunov stability condition which has been developed for RM (Lu, 1994). Then, robust stability problem (Wang & Liu, 2003) and optimal guaranteed cost control of the uncertain 2-D systems (Guan et al., 2001; Du & Xie, 2001; Du et al., 2000 ) came to be the area of interest[r]

Many benefits can be expected from the proposed structures such as supplying and absorbing the power peaks by using supercapactors which also allows recovering energy. 4. References Kishinevsky, Y. & Zelingher, S. (2003). Coming clean with fuel cells, IEEE Power & Energy Magaz[r]

The remarkable feature of sliding mode control (SMC) is the stability robustness against disturbances and variations of the system. However to design SMC, the exact model of the plant has to be known. Moreover the large gain of an SMC may intensify the chattering on the sliding surface. To cope with[r]

provided by an additional low-pass filter. The time-constant of the latter tunes the controller functionality between the perturbation compensation and a pure integral sliding mode control, as well as between chattering reduction and system robustness. Studies on the control<[r]

proposed by combining the fuzzy concept and the configuration of neural network, e.g., [19]-[23]. There, the fuzzy logic system is constructed from a collection of fuzzy If-Then rules while the training algorithm adjusts adaptable parameters. Nevertheless, few results using FNN are pro[r]

Practice. Vol., 4, 1023–1028 Takao Sato, Nozomu Araki, Yasuo Konishi, Hiroyuki IshigakiUniversity o f HyogoJapan1. IntroductionThis chapter discusses design methods for improving sliding mode control system (Chern &amp;Wu, 1992b; Sato, 2010; Utkin, 1977). Variable structure[r]

Fliess, Marquez, Delaleau &amp; Sira-Ramírez (2002)); it has been shown, in, Sira-Ramírez &amp;Silva-Ortigoza (2006), to be intimately related to classical compensator networks design.The main limitation of this approach lies in the assumption that the available output signalcoincides with t[r]

8.1 Introduction Switch-mode power supplies (SMPS) are nonlinear and time-varying systems, and thus the design of ahigh-performance control is usually a challenging issue. In fact, control should ensure system stabilityin any operating condition and good static and dynamic perfo[r]

7 Quantitative Feedback Theory and Sliding Mode Control Gemunu Happawana Department of Mechanical Engineering, California State University, Fresno, California USA 1. Introduction A robust control method that combines Sliding Mode Control (SMC) and Qua[r]

Herman, P. (2009b). Strict Lyapunov function for sliding mode control of manipulators usingquasi-velocities. Mechanics Research Communications, Vol.36, No. 2: 169-174.Herman, P. (2009c). A quasi-velocity-based nonlinear controller for rigid manipulators.Mechanics Research[r]

function or by using a second-order sliding mode controller. Model uncertainties in themuscle force characteristic as well as nonlinear friction are directly taken into account bya compensation scheme consisting of a feedforward friction compensation and a nonlinearreduce[r]

Some approaches use vision-based control (Van Der Zwaan &amp; Santos-Victor, 2001)(Quigxiao et al., 2005)(Cufi et al., 2002)(Lots et al., 2001). This strategy uses landmarks or sea bed images to determine the ROV’s actual position and to maintain it there or to follow a specific visual tr[r]

21 A Biomedical Application by Using Optimal Fuzzy Sliding-Mode Control Bor-Jiunn Wen Center for Measurement Standards, Industrial Technology Research Institute Hsinchu, Taiwan, R.O.C. 1. Introduction The development of biochips is a major thrust of the rapidly growing bi[r]

of Shandong (Y2007G06) and the Doctoral Foundation of Qingdao University of Science and Technology. 7. References Lewis,F.L.; Yesildirek A. &amp; Liu K.(1996). Multilayer neural-net robot controller with guaranteed tracking performance. IEEE Trans.on Neural Networks, Vol. 7, No.2, Mar. 1996, p[r]

1 Faculty of Electronics and Telecommunications, HUTBangkok, Jun. 14 – 23, 20061Radial-basis function (RBF) networksRBF = radial-basis function: là hàm phụ thuộc vào khỏang cách gốc từ một vectorXOR problemKhả năng phân tách theo bậc 2 (quadratically separable) 2 Facu[r]