A5041799267- Advanced Control Systems Optimization
- Control Systems and Identification
- Fault Detection and Control Systems
- Iterative Learning Control Systems
- Adaptive Control of Nonlinear Systems
ETH Zurich
2024
Southeast University
2005-2014
This work introduces the Data-Enabled Predictive iteRative Control (DeePRC) algorithm, a direct data-driven approach for iterative LTI systems. The DeePRC learns from previous iterations to improve its performance and achieves optimal cost. By utilizing tube-based variation of scheme, we propose two-stage that enables safe active exploration using left-kernel-based input disturbance design. method generates informative trajectories enrich historical data, which extends maximum achievable...
A class of dual-rate system with a fast control updating rate and slow output sampling is considered. Lifting technology applied to derive lifted state space model subspace equation for systems. From the equation, predictive law calculated from set input/output (I/O) open-loop experimental data and, thus, this approach data-driven since it does not involve an explicit model. The outputs have good tracking performance .The simulation result shows that proposed method effective.
A robust output feedback tracking strategy for uncertain nonlinear systems with measurement noise is presented by using a passivity approach. The control algorithm guarantees boundedness of all the signals in whole closed-loop system. Tracking accuracy can be reduced to desirable bound. simulation example included demonstrate effectiveness proposed algorithm.