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Thiagarajan Kittappa, A. Merlin, S. Lakshmi Priya, V. Malini · 6 authors
No abstract is available for this record.
Nhan T. Nguyen
No abstract is available for this record.
Sayan Basu Roy, Shubhendu Bhasin, Indra Narayan Kar
Convergence of controller parameters in standard model reference adaptive control (MRAC) requires the system states to be persistently exciting (PE), a restrictive condition to be verified online. A recent data-driven approach, concurrent learning, uses information-rich past data concurrently with the standard parameter update laws to guarantee parameter convergence without the need of the PE condition. This method guarantees exponential convergence of both the tracking and the controller parameter estimation errors to zero, whereas, the classical MRAC merely ensures asymptotic convergence of tracking error to zero. However, the method requires knowledge of the state derivative, at least at the time instances when the state values are stored in memory. The method further assumes knowledge of the control allocation matrix. This paper addresses these limitations by using a memory-based finite-time system identifier in conjunction with a data-driven approach, leading to convergence of both the tracking and the controller parameter estimation errors without the PE condition and knowledge of the system matrices and the state derivative. A Lyapunov based stability proof is included to justify the validity of the proposed data-driven approach. Simulation results demonstrate the efficacy of the suggested method.
J.J. Duffy
New results are given for the design of a step-varying digital control system's feedback gains that shape the zero-input response of single-input plants. These gains are calculated forward in time, the only requirement being that the plant be completely controllable. An algebraic matrix proof is given that these gains provide an ongoing solution to the problem of unmodeled disturbances. The classical method used for determining deadbeat feedback gains for both discrete and continuous time-invariant plants is shown to be a subset of this procedure. This method does not require a knowledge of the system's characteristic equation or eigenvalues. A stable and efficient computational method for the calculation of these gains is introduced. A tracking system is presented, deadbeat feedback and input gains calculated, and the system response analyzed. Numerical results are presented which demonstrate the utility of this method.
Roméo Ortega, Gustavo Sanchez Galindo
A complete proof is given of global convergence to zero of the prediction error and asymptotic optimality for a direct adaptive controller is established based on multistep quadratic cost minimization with a receding horizon philosophy. The only substantial assumptions are that the controller designed when the parameters are known, stabilizes the plant and the exact knowledge of the first N y (N y -optimization horizon) impulse response coefficients of the plant. The technical hurdles for the development of a comprehensive convergence theory of this type of controller are also highlighted.