Yonggon Lee

Designing a genetic neural fuzzy antilock-brake-system controller

A typical antilock brake system (ABS) senses when the wheel lockup is to occur, releases the brakes momentarily, and then reapplies the brakes when the wheel spins up again. In this paper, a genetic neural fuzzy ABS controller is proposed that consists of a nonderivative neural optimizer and fuzzy-logic components (FLCs). The nonderivative optimizer finds the optimal wheel slips that maximize the road adhesion coefficient. The optimal wheel slips are for the front and rear wheels. The inputs to the FLC are the optimal wheel slips obtained by the nonderivative optimizer. The fuzzy components then compute brake torques that force the actual wheel slips to track the optimal wheel slips; these torques minimize the vehicle stopping distance. The FLCs are tuned using a genetic algorithm. The performance of the proposed controller is compared with the case when maximal brake torques are applied causing a wheel lockup, and with the case when wheel slips are kept constant while the road surface changes.

Self-Organizing Radial Basis Function Network for Real-Time Approximation of Continuous-Time Dynamical Systems

Real-time approximators for continuous-time dynamical systems with many inputs are presented. These approximators employ a novel self-organizing radial basis function (RBF) network, which varies its structure dynamically to keep the prescribed approximation accuracy. The RBFs can be added or removed online in order to achieve the appropriate network complexity for the real-time approximation of the dynamical systems and to maintain the overall computational efficiency. The performance of this variable structure RBF network approximator with both Gaussian RBF (GRBF) and raised-cosine RBF (RCRBF) is analyzed. The compact support of RCRBF enables faster training and easier output evaluation of the network than that of the network with GRBF. The proposed real-time self-organizing RBF network approximator is then employed to approximate both linear and nonlinear dynamical systems to illustrate the effectiveness of our proposed approximation scheme, especially for higher order dynamical systems. The uniform ultimate boundedness of the approximation error is proved using the second method of Lyapunov.

Uniformly ultimately bounded fuzzy adaptive tracking controllers for uncertain systems

Fuzzy adaptive tracking controllers for a class of uncertain nonlinear dynamical systems are proposed and analyzed. The controllers consist of adaptive and robustifying components whose role is to nullify the effects of uncertainties and to achieve a desired tracking performance. The interactions between the two components have been investigated. The closed-loop system driven by the proposed controllers is shown to be stable with all the adaptation parameters being bounded. In particular, the proposed controllers guarantee uniform ultimate boundedness of the tracking error and the time bound of the uniform ultimate boundedness is obtained. An upper bound on the steady-state tracking error is obtained as a function of the gain of the robustifying term and the parameters of the adaptive component. The controllers are tested on an inverted pendulum and simulation results are included. A comparison of the proposed controllers with the ones in the literature is conducted.

Evolutionary Optimization of PowerElectronics Based Power Systems

This paper sets forth and demonstrates an approach to the design of power electronics based power systems using evolutionary computing techniques. Key features of the paper are the use of evolutionary computing in the context of classical control design, construction of appropriate multievent based performance metrics, and the use of multiobjective evolutionary computing in the selection of control parameters based on system performance versus control effort. The proposed approach is demonstrated in a power electronics based power distribution system similar to those being designed for next generation warships.

Genetic neural fuzzy control of anti-lock brake systems

A typical anti-lock brake system (ABS) senses when the wheel lockup is to occur, releases the brakes momentarily, and then reapplies the brakes when the wheel spins up again. In this paper, a genetic neural fuzzy ABS controller is proposed that consists of a nonderivative neural optimizer and fuzzy logic components. The nonderivative optimizer finds the optimal wheel slips that maximize the road adhesion coefficient. The optimal wheel slips are for the front and rear wheels. The inputs to the fuzzy logic component are the optimal wheel slips obtained by the nonderivative optimizer. The fuzzy components then compute brake torques that force the actual wheel slips to track the optimal wheel slips. The brake torques that force the actual wheel slips to track the optimal wheel slips minimize the vehicle stopping distance. The fuzzy logic components are tuned using a genetic algorithm. The performance of the proposed controller is compared with the case when maximal brake torques are applied causing a wheel lockup and with the case when wheel slips are kept constant while the road surface changes.

Genetic fuzzy tracking controllers for autonomous ground vehicles

Genetic algorithm (CA) based fuzzy logic controller (FLC) design methods are presented for the step-lane-change maneuver of an autonomous ground vehicle. Fuzzy logic allows us to incorporate expert knowledge in the controller design. However, the fine-tuning process of a fuzzy logic controller may be very tedious and time consuming. If the information, regarding the controller operation, derived from the expert knowledge is incomplete, then an appropriately designed genetic algorithm is employed to complete the rule base specifying the desired functioning of the controller.

Fuzzy adaptive robust control of a class of nonlinear systems

A fuzzy adaptive robust tracking controller for a class of uncertain nonlinear dynamical systems is proposed and analyzed. The controllers construction and its analysis involve sliding modes. The proposed controller consists of two components. A robust feedback component is employed to eliminate the effects of disturbances, while a fuzzy logic component equipped with an adaptation mechanism reduces modeling uncertainties by approximating the models nonlinearities on-line. A projection method is used to prevent the adaptation parameters from going unbounded in the presence of disturbances. It is shown that the closed-loop system driven by the proposed controller is stable and the adaptation parameters are bounded. A guaranteed transient performance and a guaranteed final tracking accuracy in the presence of parametric uncertainties and disturbances are achieved. Furthermore, if there are no disturbances and the unknown models nonlinearities are within the approximation range of the fuzzy logic system, asymptotic output tracking is also achieved.

Variable structure neural network based direct adaptive robust control of uncertain systems

Direct adaptive robust state feedback and output feedback controllers are proposed for the output tracking control of a class of uncertain systems with disturbance. The proposed controllers employ self-organizing raised-cosine radial basis function networks, which are capable of determining their structures dynamically, to approximate unknown system dynamics. Radial basis functions of the network can be added or removed on-line in order to ensure the desired tracking accuracy and the computational efficiency simultaneously. The closed-loop systems are characterized by the guaranteed transient response and the final tracking accuracy. The performance of the proposed output feedback controller is illustrated by numerical simulations.