An Idea of Using Reinforcement Learning in Adaptive Control Systems

Koszaka, L.; Rudek, Radosław; Pozniak-Koszalka, Iwona

doi:10.1109/icniconsmcl.2006.52

Cited by 13 publications

(18 citation statements)

References 4 publications

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“…In Theorem 1, we showed that the passive robust controller (9) leads to bounded tracking errors attracted to the invariant set S for a given choice of the feedback gains K i j , j = 1, ..., ri, i = 1, ..., m. Next, to iteratively tune the feedback gains of (9), we define a desired cost function, and use a multi-variable extremum seeking to iteratively auto-tune the gains and minimize the defined cost function. We first denote the cost function to be minimized as Q(z(β)) where β represents the optimization variables vector, defined as…”

Section: B Iterative Tuning Of the Feedback Gainsmentioning

confidence: 99%

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Multi-parametric extremum seeking-based auto-tuning for robust Input-Output linearization control

Benosman

2014

53rd IEEE Conference on Decision and Control

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Abstract-We study in this paper the problem of iterative feedback gains tuning for a class of nonlinear systems. We consider InputOutput linearizable nonlinear systems with additive uncertainties. We first design a nominal Input-Output linearization-based controller that ensures global uniform boundedness of the output tracking error dynamics. Then, we complement the robust controller with a model-free multi-parametric extremum seeking (MES) control to iteratively auto-tune the feedback gains. We analyze the stability of the whole controller, i.e. robust nonlinear controller plus model-free learning algorithm. We use numerical tests to demonstrate the performance of this method on a mechatronics example.

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Section: B Iterative Tuning Of the Feedback Gainsmentioning

confidence: 99%

“…controller (9), with the varying gains (12) and (15), we first need to introduce some additional Assumptions.…”

Section: B Iterative Tuning Of the Feedback Gainsmentioning

confidence: 99%

Multi-parametric extremum seeking-based auto-tuning for robust Input-Output linearization control

Benosman

2014

53rd IEEE Conference on Decision and Control

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“…This method has been successfully used in Ref. [11] to find the gains of a PID controller of a system with parameter uncertainties and no available prior information. The same approach has been used by the authors [12] for a load frequency control design using an RL on-line multi-agent framework.…”

Section: Introductionmentioning

confidence: 98%

Reinforcement learning based backstepping control of power system oscillations

Karimi¹,

Eftekharnejad²,

Feliachi³

2009

Electric Power Systems Research

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“…Åström and Hägglund, in [12], place the development of design methods for the automatic tuning of the PID control algorithm as an active research in adaptive control. The tuning problem of searching for optimal PID parameters for a control task is NP-hard (nondeterministic polynomial-time) [15]. Hence, PID tuning for good control performance can be burdensome [13,16,17] even for very common servomechanism applications.…”

Section: Introductionmentioning

confidence: 99%

Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

Somefun¹,

Akingbade²,

Dahunsi³

2020

ujca

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One of the lowest level control tasks, especially in robotics and other manufacturing industries, upon which other high-level controls are dependent, is the speed control of a dc motor. Usually, tuning the parameters of the proportional integral derivative (PID) controller for this task employs established conventional methods that expose the knowledge of nominal process model parameters to the control algorithm. These methods have found widespread use. Notwithstanding, a promising line of inquiry is: to search alternate possibilities of a PID being designed to automatically achieve comparable good control performance without using a formal mathematical process model approximation of the actual physical system, such as dc motor plant, in the frequency or time domain. In this paper, we propose an intelligent PID design method, "optimal closed PID-loop model predictive control", that answers this question using the characteristic settling-time (including delay-time) property of dynamic processes. The performance of this proposed method is benchmarked with popular process-model based methods. Simulation results illustrate the promise and effectiveness of the proposed tuning method, in ensuring good closed-loop performance quality for the dc motor, without using formal process models.

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An Idea of Using Reinforcement Learning in Adaptive Control Systems

Cited by 13 publications

References 4 publications

Multi-parametric extremum seeking-based auto-tuning for robust Input-Output linearization control

Multi-parametric extremum seeking-based auto-tuning for robust Input-Output linearization control

Reinforcement learning based backstepping control of power system oscillations

Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

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