Abstract
Event-triggered control is applied to adaptive dynamic programming (ADP) as an effective method to reduce the computational cost, in which sampling only happens when a specific event occurs. In several cases of industrial applications, however, the sampling rate should be reduced in the given ranges where the prior knowledge of the process has been obtained. Correspondingly a greater sampled data are needed to observe the change of the control system more carefully if the controlled variables lie outside the boundary of the desired region. In response to this industrial demand, a novel adaptive sampling strategy according to a priori knowledge for different system states is proposed to reduce the sensitivity of the ADP-based methods. To implement the strategy into nonlinear continuous-time systems, an adaptive sampling condition is given. Furthermore, the stability analysis for the close-loop system is explicitly provided by using Lyapunov approach. The experimental results on the rare earth extraction process (REEP) verify the effectiveness of the proposed method.
Highlights
Dynamic programming (DP) has revealed a basic property of the optimal strategy for multistage decision-making process: the rest of the decision-making stages must be an optimal strategy when any one of them is regarded as the initial stage and initial state, no matter what the initial state and initial decision-making are
The online learning rules in adaptive dynamic programming (ADP)-based control schemes were developed with a corresponding error term minimized overtime
A novel adaptive sampling strategy is proposed to on the basis of the different zones of the controlled variables to reduce the sensitivity of the ADP-based methods
Summary
Dynamic programming (DP) has revealed a basic property of the optimal strategy for multistage decision-making process: the rest of the decision-making stages must be an optimal strategy when any one of them is regarded as the initial stage and initial state, no matter what the initial state and initial decision-making are. Sahoo et al [7] presented an approximate optimal control of nonlinear continuous-time systems in affine form by using ADP with event-sampled. In [8], a novel event-triggered ADP control method was proposed for nonlinear continuous-time system with unknown internal states. A novel adaptive sampling strategy is proposed to on the basis of the different zones of the controlled variables to reduce the sensitivity of the ADP-based methods. To the best of our knowledge, it has not been reported in the published literatures that zone-based adaptive sampling rate is integrated into DHP for the nonlinear continuous-time system. The DHP controller based on adaptive sampling technique (ASDHP) is designed on basis of priori knowledge for different system states. A zone-based adaptive sampling condition is given for nonlinear continuous-time systems.
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