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This article tackles the path-tracking problem of wheeled mobile robots (WMRs) used in the Micro Robot Soccer Tournament (MicroSot). An heuristic fuzzy logic controller (FLC) has been designed based on a model-free approach. Hardware experimental results have been presented to verify that the FLC can control a WMR. The performance of a fine-tuned P-controller has been compared with that of the proposed fuzzy logic controller. The response time of the fuzzy-logic-controlled WMR is two times faster. Good tracking control performance was also obtained from the proposed controller.

Loop-shaping H∞ controllers are designed for both stable and integrating processes. H∞ controllers for typical industrial processes have a PID structure interpretation. Further, if the precompensator is carefully chosen, the design indicator will be independent of plant constants. This fact leads to the derivation of a direct relation between the controller parameters and the plant constants, and the results can be viewed as new PID tuning rules for stable and integrating processes. The main contribution of the tuning rules is that the PID parameters can be obtained using one design parameter which reflects the trade-off between stability robustness and time domain performance of the closed-loop system, thus making it convenient to choose PID parameters dependent on the uncertainties of the processes.

This paper proposes an environment model acquisition technique for robot navigation in an unknown terrain, which requires no prior information about the environment, and which can handle concave Curved/polygonal obstacles as well as convex ones . In this method, features extracted from the coefficients of X/sup 2/ terms which approximate sets of 4 consecutive range sensor data with second-order polynomials by the Least-squares method. Then a "local environment map" representing the robot's line of sight environment can be built. This map is helpful for a local planner to devise safe paths in an unknown environment.

A model reference adaptive speed control structure for an induction motor drive with the fuzzy neural network is proposed. The fuzzy neural network is composed of an antecedent network and a consequent network. A concise and fast-speed algorithm called the fuzzy hierarchy error approach (FHEA) is employed. Simulation results demonstrate that this kind of neural fuzzy controller is effective in tracking as well as the regulation speed control for an induction motor drive.

A direct composite H/sub /spl infin// controller design procedure is applied to a quarter-car active suspension model by utilizing its two-time-scale property. Unlike the usual approach, the procedure does not need to decompose the full plant into slow and fast subsystems, thus avoiding some numerical difficulties. It is shown that our reduced-order H/sub /spl infin// controller achieves better performance than controllers designed by other approaches.

A new PID tuning rule is derived for integrating processes. We base our method on the loop-shaping H/sub /spl infin// design with the observation that the H/sub /spl infin// controllers can be interpreted as PID controllers and that for some choice of pre-compensator the 'design indicator' is independent of plant constants. Simulation results show that our PID settings work well compared with other settings.

This paper describes the application of genetic algorithms (GAs) to a GA-optimized fuzzy-PID controller. GAs are utilized to automatically optimize the parameters of multimode controllers for cargo ships and industrial process controlled plants. Simulation results demonstrate that this GA-based approach is beneficially complementary to some classic methods.

Regarding switching converters as highly nonlinear plants with inherent parameter uncertainties, the deleterious effects due to large disturbances in line voltage and load resistance are tackled by a grid-point control approach which owns merits in both performance and cost-effectiveness. Design and experimental results based on a boost converter are given.