The proposed framework can be adapted to handle various types of model uncertainties beyond additive and multiplicative disturbances, including:
Parametric Uncertainties: Uncertainties in system parameters such as mass, inertia, or damping coefficients can be addressed by extending the CLF-HJ framework to account for parametric variations. Robust control techniques like μ-synthesis or structured singular value analysis can be integrated to handle parametric uncertainties effectively.
Time-Varying Uncertainties: Systems with time-varying uncertainties, such as changing environmental conditions or external disturbances, can be accommodated by incorporating time-varying models and adaptive control strategies. Adaptive MPC or online learning algorithms can adapt the controller to varying uncertainties in real-time.
Modeling Errors: Inaccuracies in the system model or unmodeled dynamics can be treated as model uncertainties. Robust identification techniques and adaptive control mechanisms can be employed to account for modeling errors and improve the system's robustness.
Actuator Failures: Uncertainties related to actuator failures or faults can be addressed by integrating fault-tolerant control strategies within the CLF-HJ framework. Fault detection and isolation algorithms can trigger reconfiguration of the control system to maintain stability and performance in the presence of actuator faults.