Browsing by Author "Ma, Li-Shan"

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Absolute Stability Analysis in Uncertain Static Fuzzy Control Systems with the Parametric Robust Popov Criterion
(2008)
Wu, Bing-Fei
;
Ma, Li-Shan
;
Perng, Jau-Woei
;
Chin, Hung-I
;
電控工程研究所
This study analyzes the absolute stability in static fuzzy logic control systems with certain and uncertain parameters. For certain static fuzzy control systems, the absolute stability can be analyzed with Popov criterion. The uncertain parameters for absolute stability analysis include the reference input, actuator gain and interval linear plant. The parametric robust Popov criterion based on Lur'e systems is applied to stability analysis respect to uncertain parameters. In our work, the parametric robust Popov criterion is applied to analyze absolute stability in static fuzzy logic control systems first time. This study can provide a valuable reference in designing fuzzy control systems. Finally, numerical simulations are provided to verify the analytical results.
Adaptive synchronization for unknown chaotic systems with fuzzy-neural network observer
(2006)
Wu, Bing-Fei
;
Ma, Li-Shan
;
Perng, Jau-Woei
;
Chin, Hung-I
;
Lee, Tsu-Tian
This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In proposed approach, the receiver states can be reconstructed from one transmitted state using AFNO design. The adaptive fuzzy-niural network (INN) in AFNO is adopted to model the nonlinear term in the transmitter. Additionally, an observer is designed to estimate the other states of the master. Synchronization is achieved when all states are observed. The proposed scheme can adaptively estimated the transmitter states using AFNO, even if the transmitter changes into another chaotic system. Simulation results confirm that the proposed AFNO design is valid.
Limit cycle prediction of a neurocontrol vehicle system based on gain-phase margin analysis
(2010-06-01)
Perng, Jau-Woei
;
Ma, Li-Shan
;
Wu, Bing-Fei
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Based on some useful frequency domain methods, this paper proposes a systematic procedure to address the limit cycle prediction of a neural vehicle control system with adjustable parameters. A simple neurocontroller can be linearized by using describing function method firstly. According to the classical method of parameter plane, the stability of linearized system with adjustable parameters is then considered. In addition, gain margin and phase margin for limit cycle generation are also analyzed by adding a gain-phase margin tester into open loop system. Computer simulations show the efficiency of this approach.
Observer-based synchronization for a class of unknown chaos systems with adaptive fuzzy-neural network
(2008-07-01)
Wu, Bing-Fei
;
Ma, Li-Shan
;
Perng, Jau-Woei
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In this approach, the adaptive fuzzy-neural network (FNN) in AFNO is adopted on line to model the nonlinear term in the transmitter. Additionally, the master's unknown states can be reconstructed from one transmitted state using observer design in the slave end. Synchronization is achieved when all states are observed. The utilized scheme can adaptively estimate the transmitter states on line, even if the transmitter is changed into another chaos system. On the other hand, the robustness of AFNO can be guaranteed with respect to the modeling error, and external bounded disturbance. Simulation results confirm that the AFNO design is valid for the application of chaos synchronization.
Optimal signgle-input PID type fuzzy logic controller design with genetic algorithm
(2007)
Wu, Bing-Fei
;
Ma, Li-Shan
;
Perng, Jau-Woei
;
Lee, Tsu-Tian
;
電控工程研究所
In this paper, the PID type single-input fuzzy logic controller (SFLC) is proposed by modified from PD type SFLC. By proposed structure, the steady error from PD type SFLC can be improved. Furthermore, the optimal control performance can be achieved by tuning membership function and gain parameter with genetic algorithm (GA). The simulation results show the proposed structure has better performance by comparing with GA PID and Ziegler-Nichols PID approaches.
Robust longitudinal controller and observer design for vehicles with a riccati equation approach
(2006)
Wu, Bing-Fei
;
Ma, Li-Shan
;
Perng, Jau-Woei
;
Chin, Hung-I
;
Lee, Tsu-Tian
In this paper, the robust stabilizer with Riccati equations is applied to vehicle longitudinal system design. In our design, even if the uncertainties exist in vehicle systems, the designed observer and state feedback controller can stabilize the systems for any given initial conditions. In this method, Riccati equations are solved for finding positive-definite symmetry matrices to construct the observer and state feedback gain matrices. After the inter loop is stable, the PI controller is designed in outer loop for improving the ability of tracking performance. Finally, the simulation shows the feasibility of our design.
Stability analysis of equilibrium points in static fuzzy control systems with reference inputs and adjustable parameters
(2006)
Wu, Bing-Fei
;
Ma, Li-Shan
;
Perng, Jau-Woei
;
Chin, Hung-I
;
Lee, Tsu-Tian
In this paper, the stability in static fuzzy control systems for linear systems with different fixed reference inputs and adjustable parameters are analyzed. Under certain conditions, the unique equilibriums of error in fuzzy control systems can be solved with fixed reference. Furthermore, when the unique equilibrium is stable, the steady state error can be obtained from this stable equilibrium. Under the adjustable parameters and reference inputs, the equilibriums may have stable and unstable transformations each other respect to absolutely stability. Additionally, the insight mechanism for oscillation of equilibrium is also given with a numerical example.
The Absolute Stability Analysis in Fuzzy Control Systems with Parametric Uncertainties and Reference Inputs
(2009-08-01)
Wu, Bing-Fei
;
Ma, Li-Shan
;
Perng, Jau-Woei
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur'e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.