Linear controllers perform well for straight-line path-following autonomous vehicles. For curved paths, significant large errors may result. These can be improved using two linguistic fuzzy-logic controllers (FLCs) even in the presence of inaccurate measurement of path curvature. One of the FLCs is used for accounting the path curvature. Using the linear-to-fuzzy algorithm, the synthesis of the linguistic FLCs is readily achieved. Also using the equivalent transformation algorithm, the stability analysis of the linguistic FLCs can be carried out based on a Lyapunov function which is obtained using the linear-matrix-inequality method. Experimental results are presented to illustrate the effectiveness of the proposed FLCs.

It is the function of the design of a fuzzy-logic controller to determine the universes of discourse of the antecedents and the consequents, number of membership labels, distribution and shape of membership functions, rule formulation, etc. Much of the information is usually extracted from expert knowledge, operator experience, or heuristic thinking. It is hence difficult to mechanize the first-stage design of fuzzy-logic controllers using linguistic labels whose performance is no worse than that of conventional multivariable linear controllers such as state-feedback controllers, PID controllers, etc. In this paper, an original systematic seven-step linear-to-fuzzy (LIN2FUZ) algorithm is proposed for generating the labels, universes of discourse of the antecedents and the consequents, and fuzzy rules of ;basically linear' fuzzy-logic controllers, given the reference design of available conventional multivariable linear controllers. The functionally equivalent fuzzy-logic controllers can thus provide the sound basis for the further development to achieve performance beyond the capability or the conventional controllers. The validity and effectiveness of the proposed LIN2FUZ algorithm are demonstrated by a four-input one-output inverted pendulum system.

The performance of automatic guided vehicles (AGVs) is gauged by their position and orientation errors. Sometimes the orientation errors are not measurable, and output feedback using position errors only is employed. In this paper, a controller using output feedback is proposed for a differential-wheel-drive AGV with a simple linear array of 64/spl times/1 opto-sensors. A linear controller is first developed. The performance of the linear controller is further improved using fuzzy logic. Two fuzzy-logic controllers are accordingly formed. Stability based on the Hurwitz criterion, the Popov criterion, and the phase-plane method is demonstrated. Experimental results using a laboratory AGV are provided to illustrate the effectiveness of the proposed controllers.

Autonomous vehicles often move along rather arbitrary types of path each demanding a different strategy for effective control. While it has been shown in previous papers that basic fuzzy (nonlinear) controllers can perform better than linear optimal controllers, it is desirable to take more fuzzy variables into account in order to cope intelligently with a wider variety of situations. By means of actual examples and using a physical mobile testbed, the application of intelligent fuzzy switching is investigated, which reduces a three-variable problem to a two-level lower-order problem, with reasonably smooth blending of the control strategies according to different path conditions, and with significant reduction of design effort used to generate the fuzzy control law. Experimental results are provided to demonstrate the usefulness of the proposed approach for dealing with vehicle paths characterised by nonlinear shapes and abrupt curvature changes.