Blending Based Multiple-Model Adaptive Control for Multivariable Systems and Application to Lateral Vehicle Dynamics

Zengin, Halit; Zengin, Nursefa; Fi̇dan, Barış; Khajepour, Amir

doi:10.23919/ecc.2019.8795818

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…A supervisor system takes in input the control signal and the measured output signal in order to decide which operating condition occurs, with the goal to select the opportune controller. Some examples of modern application of this control concept can be founded in Zengin et al [13], where the authors propose an application to the control of the vehicle lateral dynamic model, and in Outeiro et al [14], where the authors present the application of the control strategy on a quad-rotor flying trajectory tracking.…”

Section: State Of the Art On Robust And Adaptive Controlmentioning

confidence: 99%

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Design of Adaptive Controller Exploiting Learning Concepts Applied to a BLDC-Based Drive System

Dini

Saponara

2020

Energies

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This work presents an innovative control architecture, which takes its ideas from the theory of adaptive control techniques and the theory of statistical learning at the same time. Taking inspiration from the architecture of a classical neural network with several hidden levels, the principle is to divide the architecture of the adaptive controller into three different levels. Each level implements an algorithm based on learning from data and therefore we can talk about learning concepts. Each level has a different task: the first to learn the required reference to the control loop; the second to learn the coefficients of the state representation of a model of the system to be controlled; and finally, the third to learn the coefficients of the state representation of the actual controller. The design of the control system is reported from both a rigorous and an operational point of view. As an application example, the proposed control technique is applied on a second-order non-linear system. We consider a servo-drive based on a brushless DC (BLDC) motor, whose dynamic model considers all the non-linear effects related to the electromechanical nature of the electric machine itself, and also an accurate model of the switching power converter. The reported example shows the capability of the control algorithm to ensure trajectory tracking while allowing for disturbance rejection with different disturbance signal amplitude. The implementation complexity analysis of the new controller is also proposed, showing its low overhead vs. basic control solutions.

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Section: State Of the Art On Robust And Adaptive Controlmentioning

confidence: 99%

“…The evolution of robust control in this sense is the adaptive control [7][8][9][10][11][12][13][14], which basically involves linearized process modeling at many operational points of application interest. On each sub-model of the process, a controller is designed with simple control techniques that therefore apply locally.…”

Section: Introductionmentioning

confidence: 99%

Design of Adaptive Controller Exploiting Learning Concepts Applied to a BLDC-Based Drive System

Dini

Saponara

2020

Energies

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“…Here, since the disturbance in (4) is relatively small and has been handled by the MPC controller, it only has very limited influence on the property of the system, so the disturbance term is ignored in the derivation process. If all the uncertainties can be contained in a convex polyhedron constructed by a set of known matrix pairs A i , B i , i = 1,…, N, the uncertain system in (14) with any can be expressed as follows [27]:…”

Section: Multiple Model Adaptive Lawmentioning

confidence: 99%

“…If all the uncertainties can be contained in a convex polyhedron constructed by a set of known matrix pairs A i , B i , i = 1,…, N , the uncertain system in (14) with any λ can be expressed as follows [27]:

(][, A_{p} false(λ false) B_{p} false(λ false)) \in Co (}{, (][, A_{i} B_{i}) : i = 1, \dots, N)

where Co{.} denotes the convex hull of a set of matrices.…”

Section: Multiple Model Adaptive Predictive Controlmentioning

confidence: 99%

Multi‐model adaptive predictive control for path following of autonomous vehicles

Liang

Khajepour

et al. 2020

IET intell. transp. syst

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The uncertainties in tire cornering stiffness can degrade the path following the performance of autonomous vehicles, especially in low adhesive conditions, to deal with this problem, a novel multi-model adaptive predictive control is proposed in this study. Firstly, a model predictive path following controller is designed based on a combined model of vehicle dynamics and road-related kinematics relationship. Then, to deal with the model uncertainties, the multiple model adaptive theory is introduced, and the recursive least adaptive law is proposed with its convergence proved by Lyapunov theory. Finally, the multiple-model adaptive law is combined with the proposed model predictive control by a convex polytope of tire cornering stiffness. In this way, the proposed algorithm can be adaptive to the uncertainties of tire cornering stiffness. Simulation results show the effectiveness and robustness of the proposed method to the uncertainties of the tire cornering stiffness resulting in an excellent performance in any road condition without introducing conservativeness.

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“…Motivated by the above literature, here, a nonlinear disturbance observer‐based multiple model blending‐based adaptive explicit model predictive controller (NDO‐MMAEMPC) is developed for nonlinear systems. Unlike the technique used in Reference 38, we have used multiple adaptive estimation models with the same initial value of the parameter. This methodology has led to faster converge of some earlier multiple‐model‐based control strategies 34,35 .…”

Section: Introductionmentioning

confidence: 99%

Nonlinear disturbance observer based multiple‐model adaptive explicit model predictive control for nonlinear MIMO system

Dutta

Das

2023

Intl J Robust & Nonlinear

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In this article, a nonlinear disturbance observer‐based multiple‐model adaptive explicit model predictive control (NDO‐MMAEMPC) scheme is developed for a nonlinear MIMO system with parametric uncertainty as well as an external disturbance. The proposed method manages external disturbances without affecting the degree of smoothness of the control signals. Further, to cope with the unknown system uncertainty, blending‐based adaptive identification schemes are used for the same class of systems. For each identification model, an explicit nonlinear model predictive controller is computed off‐line for the corresponding model in advance, which saves computation power during operation. The generated control inputs from the set of explicit controllers are being blended online using adaptive weight. An extended Kalman filter algorithm is employed for the estimation of the unavailable states of the nonlinear system. Using an aerodynamic laboratory setup, the effectiveness of the proposed control algorithm is verified.

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Blending Based Multiple-Model Adaptive Control for Multivariable Systems and Application to Lateral Vehicle Dynamics

Cited by 5 publications

References 14 publications

Design of Adaptive Controller Exploiting Learning Concepts Applied to a BLDC-Based Drive System

Design of Adaptive Controller Exploiting Learning Concepts Applied to a BLDC-Based Drive System

Multi‐model adaptive predictive control for path following of autonomous vehicles

Nonlinear disturbance observer based multiple‐model adaptive explicit model predictive control for nonlinear MIMO system

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