Robust output tracking of a class of non-affine systems

Binazadeh, Tahereh; Rahgoshay, Mohammad Ali

doi:10.1080/21642583.2017.1376296

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Furthermore the function h ê(k + 1) in Figure 3, shows that h ê(k + 1) is a bounded function regardless of the value of ê(k + 1) with the limits −1.2 ≤ h ê(k + 1) ≤ 1.2. Given that ε h (k)h ê(k + 1) is a bounded function with an unknown sign, the Lyapunov function differentiation (30) can be rewritten as…”

Section: Model Estimatormentioning

confidence: 99%

“…This property of the non-affine systems makes it very difficult to find an exact solution for them. Non-affine systems have countless applications such as active magnetic bearings, aircraft dynamics, biochemical processes, dynamic models in pendulum control, underwater vehicles, and so on [30,31]. The common approach to controlling non-affine systems is adaptive control, which mostly involves neural networks where the control direction is either known or estimated with the Nussbaum gain [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Adaptive Controller Based on Hyperbolic Cost Function for Non-Affine Discrete-Time Systems with Variant Control Direction

Flores-Padilla,

Treesatayapun

2024

ASI

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As technology evolves, more complex non-affine systems are created. These complex systems are hard to model, whereas most controllers require information on systems to be designed. This information is hard to obtain for systems with varying control directions. Therefore, this study introduces a novel data-driven estimator and controller tailored for single-input single-output non-affine discrete-time systems. This approach focuses on cases when the control direction varies over time and the mathematical model of the system is completely unknown. The estimator and controller are constructed using a Multiple-input Fuzzy Rules Emulated Network framework. The weight vectors are updated through the gradient descent optimization method, which employs a unique cost function that multiplies the error by a hyperbolic tangent. The stability analyses demonstrate that both the estimator and controller converge to uniformly ultimately bounded (UUB) functions of Lyapunov. To validate the results, we show experimental tests of force control that were executed on the z-axis of a drive-controlled 3D scanning robot. This system has a varying control direction, and we also provide comparison results with a state-of-the-art controller. The results show a mean absolute percentage tracking error smaller than one percent on the steady state and the expected variation in the system’s control direction.

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Section: Model Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Adaptive Controller Based on Hyperbolic Cost Function for Non-Affine Discrete-Time Systems with Variant Control Direction

Flores-Padilla,

Treesatayapun

2024

ASI

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“…To validate the effectiveness of the proposed method, we apply the proposed controller to a two dimensional nonlinear system model having the same structure of magnetic levitation systems affected by a mismatched disturbance [14] in this section.…”

Section: Numerical Examplementioning

confidence: 99%

Robust Control Barrier Function for Systems Affected by a Class of Mismatched Disturbances

Takano

Yamakita

2020

SICE Journal of Control, Measurement, and System Integration

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This paper proposes a robust exponential control barrier function (RECBF) for systems affected by a class of mismatched disturbances, which forces system states to remain in a given safety set expressed by constraint functions. We consider the case that given constraint functions have different relative degrees for control input and disturbances due to the property of mismatched disturbances, and we extend a concept of the nominal exponential control barrier function (ECBF) to such cases. As a main result, we show RECBF conditions to guarantee invariance of the given safety set and formulate a convex optimization based controller with the RECBF conditions. In particular, we combine a disturbance estimation using Gaussian process regression, which is one of the machine learning methods, with the controller to make use of good properties of RECBF conditions. This formulation enables us to realize robust disturbance compensation based on experimental data, and it can be easily applied to practical systems. We show the effectiveness of the proposed controller through a numerical simulation of a magnetic ball levitation system having model uncertainty.

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Q-Learning-based model predictive variable impedance control for physical human-robot collaboration

Roveda

Testa

Shahid

et al. 2022

Artificial Intelligence

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Robust output tracking of a class of non-affine systems

Cited by 4 publications

References 17 publications

Data-Driven Adaptive Controller Based on Hyperbolic Cost Function for Non-Affine Discrete-Time Systems with Variant Control Direction

Data-Driven Adaptive Controller Based on Hyperbolic Cost Function for Non-Affine Discrete-Time Systems with Variant Control Direction

Robust Control Barrier Function for Systems Affected by a Class of Mismatched Disturbances

Q-Learning-based model predictive variable impedance control for physical human-robot collaboration

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