adaptieverobuste

Adaptieverobuste, in control theory, refers to approaches that combine adaptive control with robust control to maintain performance when a system model is uncertain or when disturbances are present. The aim is to adjust controller parameters online while ensuring stability and bounded tracking error, even in the face of unmodeled dynamics.

Core ideas include handling parametric uncertainty through adaptive laws that update parameter estimates and employing robust terms to mitigate the effect of disturbances and unmodeled dynamics. Stability is typically demonstrated using Lyapunov-based analysis, which provides guarantees on convergence and boundedness. Practical implementations often use parameter projection or normalization to keep estimates within physically meaningful ranges and to avoid excessive control action.

Architectures commonly seen in adaptieverobuste designs include model reference adaptive control with a robustifying term, adaptive backstepping with robustness layers, and variations of sliding-mode or H-infinity approaches that incorporate adaptation. The robust component helps protect against modeling errors that adaptation alone cannot eliminate.

Applications span several engineering domains, such as robotics (manipulators and mobile robots), aerospace (attitude and flight control), automotive systems (active suspension and engine control), process control, and power electronics. These methods are valued for their ability to preserve performance under uncertainty and to provide formal stability or performance bounds.

Advantages include improved resilience to uncertainty and disturbances, with theoretical guarantees on stability. Limitations involve design complexity, tuning of adaptive gains, potential conservatism from the robust part, and the need for adequate excitation for reliable parameter estimation in some cases.