Design of a completely model free adaptive control in the presence of parametric, non-parametric uncertainties and random control signal delay

Tutsoy, Önder; Barkana, Duygun Erol; Tugal, Harun

doi:10.1016/j.isatra.2018.03.002

Cited by 55 publications

(18 citation statements)

References 19 publications

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“…Even when u and y are measured in CL, the identification of S i is OL-like by employing the filtered signals ζ 0 and z i . Identification algorithms as [37] [38] [39] are suitable for S i identification.…”

Section: B Dual Yk Parameterization Supervisormentioning

confidence: 99%

Multi-Model Adaptive Control for CACC Applications

Navas

Milanés

Flores

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a multi-model adaptive control (MMAC) algorithm based on Youla-Kucera (YK) theory to deal with heterogeneity in cooperative adaptive cruise control (CACC) systems. The main idea of MMAC is to choose the plant in a predefined set that best approximates the system dynamics, applying the corresponding predesigned controller. A set of linear plants describing different vehicle dynamics is defined. Different CACC controllers are designed depending on these linear plants. Simulation and experimental results prove how MMAC determines the closest plant in the set, choosing the CACC system able to ensure string stability.

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Section: B Dual Yk Parameterization Supervisormentioning

confidence: 99%

Multi-Model Adaptive Control for CACC Applications

Navas

Milanés

Flores

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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“…This control solution can compensate external disturbances modifying its value function parameters. In [22], a model-free adaptive controller was proposed to control a pneumatic actuator. The controller makes use of a Q-function to estimate the long-term performance of the adaptive control.…”

Section: State Of the Artmentioning

confidence: 99%

A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

Escorcia-Hernández

Aguilar-Sierra

Aguilar-Mejía

et al. 2020

J Intell Robot Syst

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In this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neural Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic Manipulators (PKMs) are highly nonlinear systems, so the design of a suitable control scheme represents a significant challenge given that these kinds of systems are continually dealing with parametric and non-parametric uncertainties and external disturbances. The main contribution of this work is the design of an adaptive feedforward compensation term using B-Spline Neural Networks (BSNNs). They make an on-line approximation of the DPR dynamics and integrates it into the control loop. The BSNNs' functions are bounded according to the extreme values of the desired joint space trajectories that are the BSNNs' inputs, and their weights are on-line adjusted by gradient descend rules. In order to evaluate the effectiveness of the proposed control scheme with respect to the standard RISE controller, numerical simulations for different case studies under different scenarios were performed.

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“…Vehicle suspension system plays a vital role in both the driving safety and comfort, where the ride comfort and the roadhandling capacity are important in modern vehicles. In order to meet these vehicle performance requirements, three types of vehicle suspension systems, including passive [1], [2], semi-active [3], [4], and active suspensions [5], [6], are currently being studied and applied in practice in the past decades. For active suspension control design, more energy should be added and dissipated by actuators between the wheel-axle and sprung mass than passive and semi-active suspension control.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Robust Control for Vehicle Active Suspension Systems via Parameterized Controller

et al. 2020

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A parameterized controller design approach is proposed to solve the problem of multiobjective control for vehicle active suspension systems by using symbolic computation. The considered model is a quarter-vehicle model of the active suspension system. The multi-objective robust control performances include the sprung mass acceleration, suspension deflection, and tire deflection. Based on dissipative Hamiltonian systems and Lyapunov function, a multi-objective H ∞ controller design approach is developed, which can avoid solving Hamilton-Jacobi-Issacs equations. Then, an algorithm of solving semi-positive definite polynomial with tuning parameters is proposed by using symbolic computation. Furthermore, a method of parameter optimization is proposed. Simulations and comparation show that the control performance is significantly improved comparing with passive controlled systems and existing other control systems for active suspension systems. INDEX TERMS Multi-objective control, active suspension system, parameterized controller, symbolic computation, dissipative Hamiltonian systems.

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Design of a completely model free adaptive control in the presence of parametric, non-parametric uncertainties and random control signal delay

Cited by 55 publications

References 19 publications

Multi-Model Adaptive Control for CACC Applications

Multi-Model Adaptive Control for CACC Applications

A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

Multi-Objective Robust Control for Vehicle Active Suspension Systems via Parameterized Controller

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