Siber fiziksel insan sistemlerinin uyarlamali kontrolü

This dissertation focuses on the control of cyberphysical human systems in the presence of actuators' redundancy and constraints. A novel adaptive control technique is proposed to allocate control signals among redundant actuators in the presence of uncertainty and actuator saturation. The proposed method does not require any uncertainty identification or persistency of excitation assumption. The stability of the proposed method is guaranteed using Lyapunov stability analysis. In addition, a modified projection operator that can be implemented to the adaptive control allocation is proposed. This operator enables the allocator to handle both magnitude and rate limits of actuators. A novel sliding mode controller with time-varying sliding surface is designed to complement the adaptive allocator and guarantee stability and reference tracking in the presence of uncertainty and actuator saturation. This controller is robust to both adaptive control allocation error and external disturbance. Furthermore, an adaptive human model is proposed to mimic the human control response in the presence of uncertainty. The proposed structure is based on the model reference adaptive control, and the adaptive laws are obtained using the Lyapunov-Krasovskii stability criteria. To validate this model, an experimental setup is employed to collect data and a statistical analysis is conducted to measure the predictive power of the pilot model. Finally, the stability limits of a human-in-the-loop closed loop control system, where the plant to be controlled has redundant actuators with uncertain dynamics, are demonstrated. Various human models with and without time delays are investigated. Simulation results are provided to demonstrate the effectiveness of the proposed methods in each chapter.