The optimal neuroprosthetic control paradigm seeks to minimize the differences between artificial and natural physiology regarding subjective embodiment and dynamic performance. This study introduces a volitional control system integrating an electromyography (EMG)-driven musculoskeletal model with a finite state machine (FSM) impedance controller. A Hill-type muscle model is used to simulate the Gastrocnemius (GAS) and Tibialis Anterior (TA) muscles around the ankle joint. The system activates these muscles using input from ankle sensors and EMG data from antagonist muscles. To improve functionality and responsiveness, muscle parameters are optimized using a surrogate-based optimization approach. The impact of EMG control on a hybrid controller was analyzed, focusing on muscle activation during the stance phase. Two controllers were tested on a transtibial amputee for level-ground walking and stair ascent. Insights were gained into optimizing muscle activation for better gait dynamics. The model-based design technique was employed to automate validation, verification, and coding, reducing manual steps and errors. For level-ground walking, both hybrid controllers behaved more like an impedance controller than an EMG control. Disabling EMG control during plantarflexion with controller 1 prevented excessive plantarflexion, leading to a more natural gait. Controller 1, which used the TA muscle exclusively during controlled dorsiflexion, demonstrated greater repeatability. During stair ascent, both controllers allowed the user to place their toe first at each step, closely mimicking a natural gait pattern. Controller 2 exhibited better repeatability and slightly higher torque at the start of the controlled dorsiflexion phase, simplifying the control strategy and reducing computational effort.