A power assistant algorithm based on human–robot interaction analysis for improving system efficiency and riding experience of e‐bikes

As robots are becoming more accessible in our daily lives, the interest in physical human– robot interaction (HRI) is rapidly increasing. An electric bicycle (E‐bike) is one of the best examples of HRI, because a rider simultaneously actuates the rear wheel of the E‐bike in close proximity. Most commercially available E‐bikes employ a control methodology known as a power assistant system (PAS). However, this type of system cannot offer fully efficient power assistance for E‐bikes since it does not account for the biomechanics of riders. In order to address this issue, we propose a control algorithm to increase the efficiency and enhance the riding experience of E‐bikes by implementing the control parameters acquired from analyses of human leg kinematics and muscular dynamics. To validate the proposed algorithm, we have evaluated and compared the performance of E‐bikes in three different conditions: (1) without power assistance, (2) assistance with a PAS algorithm, and (3) assistance with the proposed algorithm. Our algorithm required 5.09% less human energy consumption than the PAS algorithm and 11.01% less energy consumption than a bicycle operated without power assistance. Our algorithm also increased velocity stability by 11.89% and acceleration stability by 27.28%, and decreased jerk by 12.36% in comparison to the PAS algorithm.