An Adaptive Neural Network Controller for Active Suspension Systems With Hydraulic Actuator

Liu, Lei; Zeng, Qiang; Tong, Shaocheng

doi:10.1109/tsmc.2018.2875187

Cited by 91 publications

(39 citation statements)

References 57 publications

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“…Adaptive techniques have been already developed as controller methods [19][20][21][22][23][24] as well as observer algorithms [25][26][27][28] in diverse applications. [29][30][31][32] Here, an adaptive technique is incorporated in a solution for problem of relative velocity estimation for aerial AMRs. The estimation algorithm needs the measurement on the relative distance and the vector of relative acceleration.…”

Section: Discussionmentioning

confidence: 99%

Adaptive relative velocity estimation algorithm for autonomous mobile robots using the measurements on acceleration and relative distance

Safaei

2020

Adaptive Control & Signal

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Summary In this article, an adaptive algorithm is proposed for online velocity estimation of the autonomous mobile robots (AMRs) without positioning data received from a Global Positioning System (GPS) module or other means for odometry. Unlike the popular Kalman and particle filters that use the measurements on vectors of global (or local) position and acceleration of a mobile robot, the proposed adaptive relative velocity estimation (ARVE) algorithm requires the scalar value of measured distance to a beacon agent and also the measurement on acceleration vector, in order to generate an online estimation of the global velocity vector of a mobile robot. Combining the ARVE algorithm with the recently proposed adaptive relative position estimation (ARPE) algorithm provides a solution for online estimation of the translational states of a mobile robot without accessing the GPS data, which makes the package applicable in both indoor and outdoor environments. The stability of the ARVE algorithm is analyzed with LaSalle‐Yoshizawa theorem. In addition, two simulation studies are provided to show the application of the proposed estimation package (ARVE+ARPE) for aerial AMRs in two cases corresponding to the stationary and moving beacon agents. In the simulation results, it is shown that the estimation package can be used in conjunction with the recently proposed adaptive model‐free control (AMFC) algorithm to achieve desired tracking objective in autonomous movement of a quadrotor, without requiring the information on the internal dynamics of the robot.

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Section: Discussionmentioning

confidence: 99%

Adaptive relative velocity estimation algorithm for autonomous mobile robots using the measurements on acceleration and relative distance

Safaei

2020

Adaptive Control & Signal

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“…To cope up with these drawbacks, adaptive control methods were proposed. It has been most widely used in the recent control fields [93]. In the active control area, the adaptive controller is superior to the classical controller in terms of reducing the noise and suppressing the vibration practically if the ideal model reference is available.…”

Section: Adaptive Controllermentioning

confidence: 99%

A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems

Al-Ashmori

Wang

2020

Applied Sciences

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Drivers of heavy trucks are exposed to large amounts of vibration which can lead to serious health risks. Many suspension systems/methods can be used to isolate these transmitted vibrations, such as vehicle suspension systems, cabin suspension systems and seating suspension systems. The central idea of the work is to identify the research gaps and raise our future research questions in this specific area. The novelty of this paper is proposing a model predictive controller for active vibration control of seating suspension systems. A systematic literature review of the existing work of the vibration control of seating suspension systems has been conducted. Various control techniques that are used in the seating suspension systems have been summarized and evaluated. This paper focusses on the biodynamic model of the driver and seat for the first step needed in the design of the seating suspension system. Then, it illustrates the different types of the system vibration controls and their performance evaluation methods. At the end, the paper details several active seating suspension systems including their actuation system structures and control algorithms which are used in the heavy vehicle trucks.

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“…Via the combination of backstepping technique, adaptive fuzzy/neural control approaches are applied to control nonlinear systems without satisfying matching conditions 13‐16 . Thanks to the approximation ability of neural networks, many adaptive backstepping neural control methods are explored for strict‐feedback systems in References 17‐26. Correspondingly, some fuzzy adaptive backstepping control schemes are designed for different types of nonlinear systems with the utilization of fuzzy approximation in References 27‐31.…”

Section: Introductionmentioning

confidence: 99%

Filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties

2020

Intl J Robust & Nonlinear

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This paper synthesizes a filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. The nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF)-based neural network incorporated with a piecewise constant adaptive law, where the adaptive law will generate adaptive parameters by solving the error dynamics between the real system and the state predictor with the neglection of unknowns. The combination of GRBF-based neural network and piecewise constant adaptive law relaxes hardware limitations (CPU). A filtering control law is designed to handle the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. The matched uncertainties are cancelled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. Since the virtual reference system defines the best performance that can be achieved by the closed-loop system, the uniform performance bounds are derived for the states and control signals via comparison. To validate the theoretical findings, comparisons between the model reference adaptive control method and the proposed filtering adaptive neural network control architecture with the implementation of different sampling time are carried out.

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An Adaptive Neural Network Controller for Active Suspension Systems With Hydraulic Actuator

Cited by 91 publications

References 57 publications

Adaptive relative velocity estimation algorithm for autonomous mobile robots using the measurements on acceleration and relative distance

Adaptive relative velocity estimation algorithm for autonomous mobile robots using the measurements on acceleration and relative distance

A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems

Filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties

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