In this paper, the concepts of car maneuvers, fuzzy logic control (FLC), and sensor-based behaviors are merged to implement the human-like driving skills by an autonomous car-like mobile robot (CLMR). Four kinds of FLCs, fuzzy wall-following control, fuzzy corner control, fuzzy garage-parking control, and fuzzy parallel-parking control, are synthesized to accomplish the autonomous fuzzy behavior control (AFBC). Computer simulation results illustrate the effectiveness of the proposed control schemes. The setup of the CLMR is provided, where the implementation of the AFBC on a field-programmable gate array chip is also addressed. Finally, the real-time implementation experiments of the CLMR in the test ground demonstrate the feasibility in practical car maneuvers. Index Terms-Autonomous fuzzy behavior control (AFBC), car-like mobile robot (CLMR), field-programmable gate array (FPGA), fuzzy logic control (FLC), garage parking, parallel parking, real-time implementation.

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The main theme of this paper is to present robust fuzzy controllers for a class of discrete fuzzy bilinear systems. First, the parallel distributed compensation method is utilized to design a fuzzy controller, which ensures the robust asymptotic stability of the closed-loop system and guarantees an H(infinity) norm-bound constraint on disturbance attenuation for all admissible uncertainties. Second, based on the Schur complement and some variable transformations, the stability conditions of the overall fuzzy control system are formulated by linear matrix inequalities. Finally, the validity and applicability of the proposed schemes are demonstrated by a numerical simulation and the Van de Vusse example.

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This paper presents some novel results for stabilizing singularly perturbed (SP) nonlinear systems with guaranteed control performance. By using Takagi-Sugeno fuzzy model, we construct the SP fuzzy (SPF) systems. The corresponding fuzzy slow and fast subsystems of the original SPF system are also obtained. Two fuzzy control designs are explored. In the first design method, we propose the composite fuzzy control to stabilize the SPF subsystem with control performance. Based on the Lyapunov stability theorem, the stability conditions are reduced to the linear matrix inequality (LMI) problem. The composite fuzzy control will stabilize the original SP nonlinear systems for all (0 ) and the upper bound can be determined. For the second design method, we present a direct fuzzy control scheme to stabilize the SP nonlinear system with control performance. By utilizing the Lyapunov stability theorem, the direct fuzzy control can guarantee the stability of the original SP nonlinear systems for a given interval [ ]. The stability conditions are also expressed in the LMIs. Two SP nonlinear systems are adopted to demonstrate the feasibility and effectiveness of the proposed control schemes. Index Terms-control performance, linear matrix inequality (LMI), singularly perturbed fuzzy (SPF) systems, singularly perturbed (SP) nonlinear systems, Takagi-Sugeno (T-S) fuzzy model.

This paper presents the composite fuzzy control to stabilize the nonlinear singularly perturbed (NSP) systems with guaranteed control performance. We use the Takagi-Sugeno (T-S) fuzzy model to construct the singularly perturbed fuzzy (SPF) systems. The corresponding fuzzy slow and fast subsystems of the original SPF system are also obtained. At first, a set of common positive-define matrices and the controller gains are determined by the Lyapunov stability theorem and linear matrix inequality (LMI) approach. Then, a sufficient condition is derived for the robust stabilization of NSP systems. The composite fuzzy control will stabilize the original NSP systems for all (0 ) and the allowable perturbation bound can be determined via some algebra inequalities. A practice example is adopted to demonstrate the feasibility and effectiveness of the proposed control scheme. Index Terms-control performance, linear matrix inequality (LMI), nonlinear singularly perturbed (NSP) systems, singularly perturbed fuzzy (SPF) systems, Takagi-Sugeno (T-S) fuzzy model.

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