This article proposes a new stable biped walking pattern generator with preset step-length value, optimized by multi-objective JAYA algorithm. The biped robot is modeled as a kinetic chain of 11 links connected by 10 joints. The inverse kinematics of the biped is applied to derive the specified biped hip and feet positions. The two objectives related to the biped walking stability and the biped to follow the preset step-length magnitude have been fully investigated and Pareto optimal front of solutions has been acquired. To demonstrate the effectiveness and superiority of proposed multi-objective JAYA, the results are compared to those of MO-PSO and MO-NSGA-2 optimization approaches. The simulation and experiment results investigated over the real small-scaled biped HUBOT-4 assert that the multi-objective JAYA technique ensures an outperforming effective and stable gait planning and walking for biped with accurate preset step-length value.

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This paper presents the design, development and implementation of a novel adaptive neural PID (AN-PID) controller suitable for real-time robust walking biped robot control application. The unique feature of the proposed AN-PID controller is that it has highly simple and dynamic selforganizing structure, fast online-tuning speed and flexibility in online-updating. The proposed adaptive algorithm focuses on efficiently optimizing Gain Scheduling and PID weighting parameters of neural MLPNN model integrated in the proposed AN-PID controller. This implemented AN-PID controller aims to successfully control the robust walking of the highly nonlinear full-sized biped robot HUBOT-3. The performance of this novel neural-based PID controller was found to be outperforming in comparison with conventional PID controller.