Mohammad J. Mahjoob

Ali Shafiekhani, Mohammad J. Mahjoob, Mehdi Akraminia

Fuzzy critic-based learning forms a reinforcement learning method based on dynamic programming. In this paper, an adaptive critic-based neuro-fuzzy system is presented for an unmanned bicycle. The only information available for the critic agent is the system feedback which is interpreted as the last action performed by the controller in the previous state. The signal produced by the critic agent is used along with the error back propagation to tune (online) conclusion parts of the fuzzy inference rules of the adaptive controller. Simulations and experiments are conducted to evaluate the performance of the proposed controller. The results demonstrate superior performance of the developed controller in terms of improved transient response, robustness to model uncertainty and fast online learning.

Afshin Alipour, Mohammad J. Mahjoob, Zahra Fakhari et al.

Stationary robotic trainers are lower limb rehab robots which often incorporate an exoskeleton attached to a stationary base. The issue observed in the stationery trainers for simultaneous knee and ankle-foot complex joints is that they restrict the natural motion of ankle-foot in the rehab trainings due to the insufficient Degrees of Freedom (DOFs) of these trainers. A new stationary knee-ankle-foot rehab robot with all necessary DOFs is developed here. A typical rehab training is first implemented in simulation, and then tested on a healthy subject. Results show that the proposed system functions naturally and meets the requirements of the desired rehab training.