An Indirect Self-Tuning Speed Controller Design for DC Motor Using A RLS Principle

Fawzy, Asmaa

doi:10.1109/mepcon47431.2019.9007978

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The operation of DC motors is amidst a revolutionary change with the adoption of sophisticated microcontrollers and control strategies. Control of DC motors is a well-studied topic in the literature, including using neural networks [1,2] including neural network-based auto-tuning of classical proportional, integral, derivative controllers [3], as well as recursive least squares [4]. Estimators and estimation techniques are deployed side-by-side with control strategies to determine the parameters and even the state of the system using a model.…”

Section: Introductionmentioning

confidence: 99%

Discerning Discretization for UUV DC Motor Control

Menezes¹,

Sands²

2022

Preprint

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Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, adaptive and learning methods are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is a method in which the controller is designed to adapt the system with parameters which vary or are uncertain. Parameter estimation is the process of computing the parameters of a system using a model &amp; measured data. Adaptive methods have been used in conjunction with different parameter estimation techniques. Deterministic artificial intelligence, a learning-based approach that uses the process dynamics defined by physics, is also applied to control the output of the DC motor to track a specified trajectory. This work goes further to evaluate the performance of the adaptive &amp; learning techniques based on different discretization methods. The results are evaluated based on the absolute error mean between the output &amp; the reference trajectory and the standard deviation of the error. The first order-hold method of discretization and surprisingly large sample time of seven tenths of a second yields over sixty percent improvement over the results presented in the prequel literature.

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Section: Introductionmentioning

confidence: 99%

Discerning Discretization for UUV DC Motor Control

Menezes¹,

Sands²

2022

Preprint

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“…Example motors are depicted in Figure 2, where current is supplied to wires at one end of the motor and rotation is provided by the motor at the other end (as depicted). Control of the motor is a well-studied topic in the literature [1][2][3][4], culminating in a very recent publication of deterministic artificial intelligence used for motor control [5,6]. Motor control using neural networks was presented in [1][2][3], while indirect self-tuners were presented in [4], which were the basis for comparison of the recently presented method of deterministic artificial intelligence [5], where the prequel comparison to self-tuners is presented in [6] applied to DC motors for unmanned underwater vehicles.…”

Section: Introductionmentioning

confidence: 99%

Comparing Methods of DC Motor Control for UUVs

Shah

Sands

2021

Applied Sciences

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Adaptive and learning methods are proposed and compared to control DC motors actuating control surfaces of unmanned underwater vehicles. One type of adaption method referred to as model-following is based on algebraic design, and it is analyzed in conjunction with parameter estimation methods such as recursive least squares, extended least squares, and batch least squares. Another approach referred to as deterministic artificial intelligence uses the process dynamics defined by physics to control output to track a necessarily specified autonomous trajectory (sinusoidal versions implemented here). In addition, one instantiation of deterministic artificial intelligence uses 2-norm optimal feedback learning of parameters to modify the control signal, while another instantiation is presented with proportional plus derivative adaption. Model-following and deterministic artificial intelligence are simulated, and respective performance metrics for transient response and input tracking are evaluated and compared. Deterministic artificial intelligence outperformed the model-following approach in minimal peak transient value by a percent range of approximately 2–70%, but model-following achieved at least 29% less error in input tracking than deterministic artificial intelligence. This result is surprising and not in accordance with the recently published literature, and the explanation of the difference is theorized to be efficacy with discretized implementations.

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