Active vehicle suspension control using road preview model predictive control and radial basis function networks

Papadimitrakis, Myron; Alexandridis, Alex

doi:10.1016/j.asoc.2022.108646

Cited by 46 publications

(26 citation statements)

References 30 publications

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“…With the matrices Q, N and R in ( 9), the new matrices for the augmented system are defined as (23). With those matrices and the augmented system (22), the LQ objective function ( 9) is also augmented with the previewed disturbances as (24).…”

Section: Design Of Lq Optimal Preview Controller With a Quarter-car M...mentioning

confidence: 99%

“…The discrete-time LQ optimal preview controller is obtained as (25) from LQR for the augmented system (22) with LQ objective function (24). As shown in (25), LQR for the augmented system has the form of the full-state feedback control, which consists of the feedback and feedforward parts, KFB and KFF, corresponding to the vectors of state and disturbance, respectively.…”

Section: Design Of Lq Optimal Preview Controller With a Quarter-car M...mentioning

confidence: 99%

“…With the previewed road profile, a preview control has been applied to date [7,8]. In fact, most of the papers on feedforward control for an active suspension system have adopted a preview control [7,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Among them, LQ optimal preview control has been widely adopted for designing a preview controller [10][11][12][13][14][15][16]19,21,23].…”

Section: Introductionmentioning

confidence: 99%

“…Among them, LQ optimal preview control has been widely adopted for designing a preview controller [10][11][12][13][14][15][16]19,21,23]. H optimal preview control, sliding mode control and model predictive control (MPC) were also adopted for preview controller design [13,17,18,20,22,24].…”

Section: Introductionmentioning

confidence: 99%

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Design of Virtual Reference Feedforward Controller for an Active Suspension System

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Section: Design Of Lq Optimal Preview Controller With a Quarter-car M...mentioning

confidence: 99%

Section: Design Of Lq Optimal Preview Controller With a Quarter-car M...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of Virtual Reference Feedforward Controller for an Active Suspension System

et al. 2022

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“…When an input is provided to the NN, it will generate the best possible results via adopting to changing input without redesigning the output criteria. For example, Radial Basis Function (RBF) NNs have been used as a tool for modeling nonlinear functions in control engineering due to their simple structure and good accuracy [76]. RBF networks can approximate an unknown function with a linear combination of a group of nonlinear functions, called base functions.…”

Section: ) Neural Networkmentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

et al. 2022

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Network control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world network control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.

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Backstepping Sliding Mode Control Design for Active Suspension Systems in Half-Car Model

Pham

Nguyen

et al. 2022

Advances in Engineering Research and Application

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Active vehicle suspension control using road preview model predictive control and radial basis function networks

Cited by 46 publications

References 30 publications

Design of Virtual Reference Feedforward Controller for an Active Suspension System

Design of Virtual Reference Feedforward Controller for an Active Suspension System

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Backstepping Sliding Mode Control Design for Active Suspension Systems in Half-Car Model

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