A new adaptive multiple neural network controller (AMNNC) with a supervisory controller for a class of uncertain nonlinear dynamic systems was developed in this paper. The AMNNC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple neural networks. The weighted sum of the multiple neural networks was used to approximate system nonlinearity for the given task. Each neural network represents the system dynamics for each task. For a job where some tasks are repeated but information on the load is not defined and unknown or varying, the proposed controller is effective because of its capability to memorize control skill for each task with each neural network. For a new task, most similar existing control skills may be used as a starting point of adaptation. With the help of a supervisory controller, the resulting closed-loop system is globally stable in the sense that all signals involved are uniformly bounded. Simulation results on a cartpole system for the changing mass of the pole were illustrated to show the effectiveness of the proposed control scheme for the comparison with the conventional adaptive neural network controller (ANNC).

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So far, mobile robot equipped with multiple ultrasonic sensors with fixed beam-width has been used in robot navigation. In this paper, we used multiple ultrasonic sensors with different beam-widths in mobile robot navigation. The use of ultrasonic sensor with small beam-width gives good resolution in recognizing environmental condition and we need more sensors to detect obstacles in wide angular region. However, if we use wide beam-width ultrasonic sensors, we can detect surrounding obstacles with a few sensors. By fusing the aspects of ultrasonic sensors with different beam-widths, we can obtain more efficient collision avoidance behavior in robot control. We stacked three kinds of ultrasonic sensors and get distance information from each sensor. Small beam-width sensor can detect environment with high resolution and large beam-width sensor gives information on possible obstacles in robot motion. Using the approach, the robot can navigate through a complex environment.

An intelligent walking-assistance robot system has been developed to help the elderly or the disabled in rehabilitation programs. From the design viewpoint, several different mechanisms to satisfy the strict requirements for use in a rehabilitation program were considered and studied. A two-wheel mobile mechanism was developed to provide motions to follow the patient's walking trajectory, and also to make the patient follow a specified trajectory. Equations of motion were derived for the unloading control, and a force control algorithm was developed. For motion control of the mobile base, virtual trajectory planning by the B-spline method and PID control were used. Sensing the motion of the patient is performed by a linear potentiometer and a rotating encoder attached to the robot manipulator. The system was tested on patients in hospital and the experimental results are reported.