Survey of model reference adaptive control

Pathak, Kalpesh B.; Adhyaru, Dipak M.

doi:10.1109/nuicone.2012.6493284

Cited by 18 publications

(4 citation statements)

References 21 publications

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“…In addition, some of the recent literature focuses on model reference adaptive control (MRAC). It is used for the systems of which the parameters are unknown and/or change with time, and designs controllers with controller parameter adjustment mechanism by comparing the output of the systems and the reference models (Pathak & Adhyaru, 2012). However, this method has the shortcomings of effectiveness for nonlinear systems, and the theories on sensitivity, controllability, observability, stability and robustness need further investigation (Shekhar & Sharma, 2018).…”

Section: About Model Reference Tracking ( Mrt )mentioning

confidence: 99%

High-order fully-actuated system approaches: Part IX. Generalised PID control and model reference tracking

Duan

2021

International Journal of Systems Science

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Tracking control of general dynamical systems in high-order fully actuated (HOFA) system representation is solved. For the case of tracking a constant or slow time-varying signal in the presence of constant or slow time-varying disturbances, a generalised PID control scheme is proposed, which realises asymptotical tracking to a prescribed signal and also guarantees that the state derivatives of certain orders converge to the origin. For the case that the signal to be tracked is generated by a reference model, a model reference tracking (MRT) controller is presented, which relies on the solution to a type of generalised Sylvester matrix equations and guarantees the desired asymptotical tracking requirement. Due to the full-actuation property of the HOFA models, closed-loop systems under both control schemes are constant and linear. Furthermore, based on a general parametric solution to the type of Sylvester matrix equations and a general parametric eigenstructure assignment result, simple and complete parameterisation of the two types of control designs are provided, and feasibility conditions in terms of the system initial values for sub-fully actuated systems are also derived. An illustrative example is presented to demonstrate the application of the proposed approaches and their effects.

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Section: About Model Reference Tracking ( Mrt )mentioning

confidence: 99%

High-order fully-actuated system approaches: Part IX. Generalised PID control and model reference tracking

Duan

2021

International Journal of Systems Science

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“…In search of new advanced control schemes, theories have evolved in several directions, giving a very rich bibliography over 80 years. For the CSTR control example, various control strategies, such as the exact feedback linearization control [11,12], the nonlinear backstepping control [2], the model predictive control [4,[13][14][15][16][17][18][19], different optimal control strategies [20][21][22][23], the adaptive control approaches [24][25][26][27], and the sliding mode control theory [1,[28][29][30][31][32] have been proposed among others. We can also ind several articles based on successful combinations between advanced nonlinear control theories and soft computing tools such as artiicial neural networks (ANN) [33,34], fuzzy inference systems (FIS) [3,35], and many bio-inspired optimization algorithms such as the genetic algorithm (GA) [7,36], etc.…”

Section: Introductionmentioning

confidence: 99%

Extended State Observer Based Robust Feedback Linearization Control Applied to an Industrial CSTR

Medjebouri

2024

JAMRIS

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In the chemical and petrochemical industries, the Continuous Stirred Tank Reactor (CSTR) is, without doubt, one of the most popular processes. From a control point of view, the mathematical model describing the temporal evolution of the CSTR has a strongly nonlinear cross-coupled character. Moreover, modeling errors such as external disturbances, neglected dynamics, and parameter variations or uncertainties make its control task a very difficult challenge. This problem has been the subject of a wide number of control strategies. This article attempts to propose a viable, robust nonlinear decoupling control scheme. The idea behind the proposed approach lies in the design of two nested control loops. The inner loop is responsible for the compensation of the nominal model's nonlinear cross-coupled terms via a static nonlinear feedback; while the outer loop, designed around an Extended State Observer (ESO), which the additional state gathers the global effect of modeling errors, is charged with instantaneously estimating and then compensating the ESO extended state. This way, the CSTR complex dynamics are reduced to a series of decoupled linear subsystems easily controllable using a simple Proportional-Integral (PI) linear control to ensure the robust pursuit of reference signals respecting the desired performance. The presented control validation was performed numerically by an objective comparison to a classical PID controller.

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“…Recently MRAC has received considerable attention and many new approaches have been applied to the practical process [2]. In the MRAC scheme, the controller is designed to realize the plant output converges to reference model output based on assumption that plant can be linearized [3], [4], and [5]. Therefore, direct MRAC is best controller for controlling linear plants with unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The adaptive controller is designed to realize a plant output tracks to reference model output based on assumption that the plant can be linearized. [8], [9], [3] However, as most industrial processes are highly nonlinear, non-minimum, and with various type of uncertainties and load disturbances the performance of the linear MRAC may deteriorate, and suitable nonlinear control may have to be used.…”

Section: Introductionmentioning

confidence: 99%

Neural Network based Direct MRAC Tech-nique for Improving Tracking Performance for Nonlinear Pendulum System

Assefa¹

2020

J. Infor. Electr. Electron. Eng.

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This paper investigates the application of a neural network-based model reference adaptive intelligent controller for controlling the nonlinear systems. The idea is to control the plant by minimizing the tracking error between the desired reference model and the nonlinear system using conventional model reference adaptive controller by estimating the adaptation law using a multilayer backpropagation neural network. In the conventional model reference adaptive controller block, the controller is designed to realize the plant output converges to reference model output based on the plant, which is linear. This controller is effective for controlling the linear plant with unknown parameters. However, controlling of a nonlinear system using MRAC in real-time is difficult. The Neural Network is used to compensate the nonlinearity and disturbance of the nonlinear pendulum that is not taken into consideration in the conventional MRAC therefore, the proposed paper can significantly improve the system behaviour and force the system to behave the reference model and reduce the error between the model and the plant output. Adaptive law using Lyapunov stability criteria for updating the controller parameters online has been formulated. The behaviour of the proposed control scheme is verified by developing the simula-tion results for a simple pendulum. It is shown that the proposed neural network-based Direct MRAC has small rising time, steady-state error and settling time for a different disturbance than Conventional Direct MRAC adaptive control.

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Survey of model reference adaptive control

Cited by 18 publications

References 21 publications

High-order fully-actuated system approaches: Part IX. Generalised PID control and model reference tracking

High-order fully-actuated system approaches: Part IX. Generalised PID control and model reference tracking

Extended State Observer Based Robust Feedback Linearization Control Applied to an Industrial CSTR

Neural Network based Direct MRAC Tech-nique for Improving Tracking Performance for Nonlinear Pendulum System

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