Key result
Constrained adaptive dynamic programming successfully tunes PID controllers offline while preserving stability in nonlinear processes.
The proposed ACADP algorithm successfully tunes PID controller parameters in a model-free offline fashion while preserving stability.
In this paper, a model-free off-policy approximate constrained adaptive dynamic programming (ACADP) algorithm is developed for discrete-time general nonlinear systems. By using an adaptive learning rate, the controller update is constrained in a well-defined neighborhood to avoid performance degradation in case of high convergence uncertainty of the ACADP mechanism. The learning mechanism is used to tune a Proportional-Integrative-Derivative (PID) controller parameters in a model-free offline fashion. The Markovian assumption is proved for the closed-loop system with a general PID controller to show its applicability in Adaptive Dynamic Programming (ADP) like algorithms. The validation performed on a nonlinear process highlighting the stabilitypreserving attributes of the proposed method using the constrain mechanism.
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Timotei Lala (2025) studied this question. approximate constrained adaptive dynamic programming (ACADP) algorithm was evaluated. An approximate constrained adaptive dynamic programming algorithm successfully tuned PID controller parameters in a model-free offline fashion while preserving stability in a nonlinear process.
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