A modified model reference adaptive control with application to MEMS gyroscope

Zareh, Mehran; Soheili, Sahel

doi:10.1007/s12206-011-0607-5

Cited by 15 publications

(4 citation statements)

References 24 publications

(42 reference statements)

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“…It applies the MRAC scheme [20][21][22] to real-world systems. For the stability analysis of the system by the MIT rule, we needed a loss function J, often known as the cost function, which may be illustrated using [23][24][25][26][27],…”

Section: Mit Rulementioning

confidence: 99%

Investigations on MIT and Lyapunov rule-based modified MRAC for noninteracting and interacting two-tank coupled systems

Gupta,

Kumar,

Kumar

2024

FME Transactions

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The adaptive control method is a technique that measures the dynamic characteristics of the plant automatically and continuously to make a comparison with its required output. It utilizes the difference between plant output and reference output to compute adaptable system parameters to maintain optimal performance regardless of the system variations. The behavior of the adaptation rule is significantly affected by the adaptation gain value. This paper describes the design of MIT (Massachusetts Institute of Technology) and Lyapunov rule-based modified Model Reference Adaptative Controller (MRAC) to stabilize a non-interacting and interacting two-tank process system. Investigation of variation in adaptation gain has also been done. Initially, a traditional MIT and Lyapunov rule-based MRAC is designed to stabilize the non-interacting and interacting tow-tank coupled system and it is found that both the systems are stable only for a few values of adaptation gain. To overcome this problem the modified MRAC is planned to stabilize and improve the response of the system. The modified MRAC scheme is just the PD (Proportional Derivative) controller superimposed on the MRAC control method. Now with the modified MRAC, the systems have been stabilized and their response has been improved for the wide range of adaptation gains. The comparative analysis of traditional and modified MRAC has also been presented. The performance analysis in terms of rise time, settling time, and peak overshoot has been carried out by comparing the results obtained for all the mentioned rules with the variations in the adaptation gain, on the MATLAB/Simulink platform. The obtained results present encouraging outcomes.

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Section: Mit Rulementioning

confidence: 99%

Investigations on MIT and Lyapunov rule-based modified MRAC for noninteracting and interacting two-tank coupled systems

Gupta,

Kumar,

Kumar

2024

FME Transactions

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show abstract

“…It applies the MRAC scheme (Mukherjee et al, 2018b; Sethi et al, 2017) to real-world systems. For the stability analysis of the system by MIT rule, we needed a loss function J , often known as the cost function, which may be illustrated using (Fan and Kobayashi, 1998; Karthikeyan et al, 2012; Mfoumboulou 2021; Rothe et al, 2020; Zareh and Soheili 2011)…”

Section: Mit Rulementioning

confidence: 99%

Effect of adaptation gain and reference model in MIT and Lyapunov rule–based model reference adaptive control for first- and second-order systems

Gupta,

Kumar,

Giri

2023

Transactions of the Institute of Measurement and Control

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An adaptive control law encompasses a regulating control rule compensating for system dynamics variations by adjusting the controller characteristics to maintain the overall system performance. Recently, some techniques have been developed based on fundamental aspects of the adaptability of living organisms. The adaptive control method is a technique that measures the dynamic characteristics of the plant automatically and continuously to make a comparison with its required output. It utilizes the difference between these two to commute adaptable system parameters to maintain optimal performance regardless of the system variations. The behavior of the adaptation rule is significantly affected by the adaptation gain value. Here, it has also been investigated that the adaptation gain range is wide for the systems with the lower order. The appropriate range of adaptation gain decreases as the order of the system increases. In the present work, the model reference adaptive control (MRAC) for first- and second-order systems has been designed and investigated using a wide range of values for the adaptation gain and variations in the reference model parameters. The MIT (Massachusetts Institute of Technology) and Lyapunov rules are applied for the analyses of systems. On the MATLAB/Simulink platform, all the adaptation process comparisons, variations, and investigations have been carried out by altering the adaptation gain and the reference model parameters. The obtained results present encouraging outcomes.

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“…For robustness,

{H}_{\infty }

augmentation following inverse optimality theory is proposed. Similarly, different robustness modification techniques have been proposed in Ajel et al (2021), Fravolini et al (2020), Humaidi and Hameed (2018), Rothe et al (2020), Stepanyan and Krishnakumar (2012), Zareh and Soheili (2011), such as

\sigma

‐modification, e‐modification, Dead Zone, Optimal, data‐driven modifications including Projection operator to ensure bounded parameter estimation. Contrary to the control formulations proposed above, this paper's suggested controller combines feedback linearization with MRAC, an estimate of the disturbance, and an augmented tracking error‐based term.…”

Section: Introductionmentioning

confidence: 99%

Projection operator‐based robust adaptive control of an aerial robot with a manipulator

Sumathy

Abdul

Ghose

2023

Journal of Field Robotics

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This paper describes a quadcopter manipulator system, an aerial robot with an extended workspace, its controller design, and experimental validation. The aerial robot is based on a quadcopter with a three degree of freedom robotic arm connected to the base of the vehicle. The work aims to create a stable airborne robot with a robotic arm that can work above and below the airframe, regardless of where the arm is attached. Integrating a robotic arm into an underactuated, unstable system like a quadcopter can enhance the vehicle's functionality while increasing instability. To execute a mission with accuracy and reliability during a real-time task, the system must overcome the inter-coupling effects and external disturbances. This work presents a novel design for a robust adaptive feedback linearization controller with a model reference adaptive controller and hardware implementation of the quadcopter manipulator system with plant uncertainties. The closed-loop stability of the aerial robot and the tracking error convergence with the robust controller is analyzed using Lyapunov stability analysis. The quadcopter manipulator system is custom developed in the lab with an off-the-shelf quadcopter and a 3D-printed robotic arm. The robotic system architecture is implemented using a Jetson Nano companion computer for autonomous onboard flight. Experiments were conducted on quadcopter manipulator system to evaluate the autonomous aerial robot's stability and trajectory tracking with the proposed controller.

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A modified model reference adaptive control with application to MEMS gyroscope

Cited by 15 publications

References 24 publications

Investigations on MIT and Lyapunov rule-based modified MRAC for noninteracting and interacting two-tank coupled systems

Investigations on MIT and Lyapunov rule-based modified MRAC for noninteracting and interacting two-tank coupled systems

Effect of adaptation gain and reference model in MIT and Lyapunov rule–based model reference adaptive control for first- and second-order systems

Projection operator‐based robust adaptive control of an aerial robot with a manipulator

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