New Algorithms for Discrete-Time Parameter Estimation
This addresses parameter estimation problems in control systems, offering incremental improvements over existing methods like RLS.
The paper tackles parameter estimation in discrete-time systems by proposing two algorithms: one for time-varying parameters under persistent excitation conditions, and another for constant parameters without such conditions. The results show uniform convergence to a compact set for time-varying cases and boundedness guarantees for constant cases, with simulations indicating better convergence than recursive least squares and comparable performance to RLS with forgetting factor.
We propose two algorithms for discrete-time parameter estimation, one for time-varying parameters under persistent excitation (PE) condition, another for constant parameters under no PE condition. For the first algorithm, we show that in the presence of time-varying unknown parameters, the parameter estimation error converges uniformly to a compact set under conditions of persistent excitation, with the size of the compact set proportional to the time-variation of unknown parameters. Leveraging a projection operator, the second algorithm is shown to result in boundedness guarantees when the plant has constant unknown parameters. Simulations show better convergence results compared to recursive least squares (RLS) and comparable results to RLS with forgetting factor.