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This paper proposes a model free adaptive cascade control scheme for superheated steam temperature regulation, in which a model free adaptive (MFA) controller is designed as the primary controller in the outer loop. The implementation procedures of the proposed cascade control approach are summarized. Simulation results demonstrate that the proposed control methodology achieves better performances...
In this paper, a data-driven model free adaptive control (MFAC) scheme, based on a novel transformation and linearization of the evaporator model, is developed for superheating regulation in an organic Rankine cycle (ORC) process. The designing procedures of the proposed approach are presented. The main feature of the method is that the controller design depends only on the measured input pump rotating...
In this paper, an enhanced data-driven optimal terminal iterative learning control (E-DDOTILC) is proposed for a class of nonlinear and nonaffine discrete-time systems. A dynamical linearization approach is first developed with iterative operation points to formulate the relationship of system output and input into a linear affine form. Then, an ILC law is constructed with a nonlinear learning gain,...
This paper presents an adaptive iterative learning control approach for the PH neutralization in Batch Processes. The proposed approach includes a feedback control law and a parameter iterative updating law together. The parameter updating law is designed by a projection algorithm to estimate the time-varying parametric uncertainties of the PH neutralization. Both the rigorous analysis and the simulation...
Terminal iterative learning control (TILC) has been developed to track a single desired point at the terminal end of operation interval over iterations. In this paper, the feedback control knowledge of previous time instants is utilized via an equivalent dynamical predictive model to update the input signals for the TILC problem. The proposed scheme consists of a control input updating law with feedback...
This paper presents a new data-driven optimal terminal iterative learning control (TILC) using time-varying control input signals to enhance control performance. The iterative learning control input is updated using the terminal output in previous runs, together with the control input information in previous runs and previous time instants of the current run, without the need of any reference trajectory...
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