In the future, the population's aging problem will become increasingly serious. For medical care, action-assisted vehicles are one of the solutions. A wheelchair is a common mobility assistance equipment, and the safety of the user is an important issue. For the ordinary wheelchairs, most of them use a fixed hand brake or disk brake to allow the users to stop the wheelchair. However, improper use of brakes tends to cause slippage, and there is no concept of the anti-lock braking system (ABS) for the wheelchair. Therefore, this paper applies this idea to intelligent wheelchairs with the aim of enhancing the safety of users in wheelchairs. The ABS architecture includes the core algorithms of the adaptive fuzzy-neural inference system and the friction coefficient estimation system. The friction coefficient estimation system uses a particle filter to quickly adapt to a non-linear state and unknown environment. The system provides more accurate braking control for the ABS according to the range of the friction coefficient based on the pavement type. In ABS, it uses the gyroscope to detect the acceleration and wheelchair angle information and then calculates the parameters of the wheelchair. The user clicks the stop command on the wheelchair to activate the brake system to achieve a simple and efficient mode of operation. It can efficiently reduce the braking time and braking distance and enhance the riding safety of wheelchairs.
- adaptive control
- fuzzy neural networks