Browsing by Author "Liu, HL"

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Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems
(2002-10-01)
Wang, CH
;
Lin, TC
;
Lee, TT
;
Liu, HL
;
電控工程研究所
A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between,plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.
Dynamic optimal learning rates of a certain class of fuzzy neural networks and its applications with genetic algorithm
(2001-06-01)
Wang, CH
;
Liu, HL
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
The stability analysis of the learning rate for a two-layer neural network (NN) Is discussed first by minimizing the total squared error between the actual and desired outputs for a set of training vectors. The stable and optimal learning rate, in the sense of maximum error reduction, for each iteration in the training (back propagation) process can therefore be found fur this two-layer NN, It has also been proven in this paper that the dynamic stable learning rate for this two-layer NN must be greater than zero. Thus it is guaranteed that the maximum error reduction can be achieved by choosing the optimal learning rate for the next training iteration. A dynamic fuzzy neural network (FNN) that consists of the fuzzy linguistic process as the premise part and the two-layer NN as the consequence part is then illustrated as an immediate application of our approach, Each part of this dynamic FNN has its own learning rate for training purpose. A genetic algorithm is designed to allow a more efficient tuning process of the two learning rates of the FNN. The objective of the genetic algorithm is to reduce the searching time by searching for only one learning rate, which is the learning rate of the premise part, in the FNN. The dynamic optimal learning rates of the two-layer NN can he found directly using our innovative approach, Several examples are fully illustrated and excellent results are obtained for the model car backing up problem and the identification of nonlinear first order and second order systems.
Electronic structure and transport properties of La0.7Ce0.3MnO3
(2005-10-01)
Chang, WJ
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Tsai, JY
;
Jeng, HT
;
Lin, JY
;
Zhang, KYJ
X-ray absorption spectroscopy (XAS), optical reflectance spectroscopy, and the Hall effect measurements were used to investigate the electronic structure in La0.7Ce0.3MnO3 thin films (LCeMO). The XAS results are consistent with those obtained from LDA+U calculations. In that the doping of Ce has shifted up the Fermi level and resulted in marked shrinkage of hole pockets originally existing in La0.7Ca0.3MnO3 (LCaMO). The Hall measurements indicate that in LCeMO the carriers are still displaying the characteristics of holes as LDA+U calculations predict. Analyses of the optical reflectance spectra evidently disapprove the scenario that the present LCeMO might have been dominated by the La-deficient phases.
Observer-based indirect adaptive fuzzy-neural tracking control for nonlinear SISO systems using VSS and H-infinity approaches
(2004-04-16)
Lin, TC
;
Wang, CH
;
Liu, HL
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Fuzzy control is a model free approach, i.e., it does not require a mathematical model of the system under control. An observer-based indirect adaptive fuzzy neural tracking control equipped with VSS and H control algorithms is developed for nonlinear SISO systems involving plant uncertainties and external disturbances. Three important control methods, i.e., adaptive fuzzy neural control scheme, VSS control design and H tracking theory, are combined to solve the robust nonlinear output tracking problem. A modified algebraic Riccati-like equation must be solved to compensate the effect of the approximation error via adaptive fuzzy neural system on the H control. The overall adaptive scheme guarantees the stability of the resulting closed-loop system in the sense that all the states and signals are uniformly bounded and arbitrary small attenuation level of the external disturbance on the tracking error can be achieved. The simulation results confirm the validity and performance of the advocated design methodology. (C) 2003 Elsevier B.V. All rights reserved.
On-line intelligent adaptive control for uncertain Nonlinear systems using TS-type fuzzy models with maximum allowable computational time for controller
(2003)
Wang, CH
;
Ker, SH
;
Liu, HL
;
Lee, TT
;
電控工程研究所
A new Takagi-Sugeno(TS)-type FNN learning architecture is proposed for the on-line identification of the TS-type fuzzy model of the uncertain system. The dynamical optimal learning rule is adopted to update the linearized TS-type fuzzy model to guarantee the convergence of on-line training process. To improve the convergence speed of the on-line training process, the lease-squared identification is applied to identify the initial parameters of the TS-type fuzzy model. Once the linearized TS-type fuzzy model of the uncertain nonlinear system is obtained in real-time environment, the on-line adaptive controller can be easily designed to accomplish the design specifications. A simplified tracking controller is also proposed to perforin the tracking of a reference signal for unknown system. Critical constraint criteria are applied to find the computational time for generating controller signal. Based on this sampling time, suitable equipments are used in actual hardware implementation. Inverted pendulum system is illustrated to track sinusoidal signal.
Raman scattering studies of single-crystal La1-xSrxMnO3
(2005-06-01)
Her, JL
;
Liu, HL
;
Mukovskii, YM
;
Lin, JY
;
Hsu, YY
We present the results of Raman scattering studies of single-crystal La1-xSrxMnO3 (x = 0.05, 0.10, 0.15, 0.20, 0.25, and 0.35) as a function of doping and in the temperature range 10-300 K. The room-temperature Raman spectrum of the x = 0.05 compound displays four phonon bands near similar to 260, 488, 612, and 674 cm(-1). With increasing Sr doping, and a corresponding structural phase transition from an orthorombic to a rhombohedral form, there is a broadening of the linewidths and a decrease in intensities of these phonon modes. 14 When the temperature is lowered, for the x = 0.05 material, two low-frequency phonons associated with the out-of-phase rotation and internal bending of MnO6 octahedra show a noticeable hardening below the canted anti-ferromagnetic transition, which is an evidence for strong spin-phonon coupling. Interestingly, this anomalous behavior disappears in the higher Sr doping level.
X-ray absorption and optical spectroscopy studies of (Mg1-xAlx)B-2
(2003-09-01)
Yang, HD
;
Liu, HL
;
Lin, JY
;
Kuo, MX
;
Ho, PL
X-ray absorption spectroscopy and optical reflectance measurements have been carried out to elucidate the evolution of the electronic structure in (Mg1-xAlx) B-2 for x=0.0, 0.1, 0.2, 0.3, and 0.4. The important role of B 2psigma hole states to superconductivity has been identified, and the decrease in the hole carrier number is quantitatively determined. The rate of the decrease in the hole concentration agrees well with the theoretical calculations. On the other hand, while the evolution of the electronic structure is gradual through the doping range, T-c suppression is most significant at x=0.4. These results suggest that the superstructure in (Mg1-xAlx)B-2, in addition to the sigma holes, can affect the lattice dynamics and contributes to the T-c suppression effect. Other possible explanations like the topological change of the sigma band Fermi surface are also discussed.