Stability-assured robust adaptive control of semi-active suspension systems

Nilkhamhang, Itthisek; Sano, Akira

doi:10.1109/acc.2008.4586844

Cited by 2 publications

(16 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It practice, model parameters λ , β , μ , η are obtained by the curve‐fitting just as done in , meanwhile, there exist perturbations on the other parameters of the suspension. Therefore, here λ , β , μ , η are unknown and will be estimated by the adaptive control, and other parameters are regarded as the nominal values with the perturbations.…”

Section: Suspension System With Mr Damper and Control Problemmentioning

confidence: 99%

“…γ , γ 1 ∈ (0, 1) represent the attenuation level for external and interior disturbance. d is defined as

d = ({, \begin{array}{c} center & x_{2} - x_{4} \\ center & δ \end{array}) \begin{matrix} center \\ center, \\ center, \end{matrix} \begin{matrix} center \\ center |x_{2} - x_{4}| > δ \\ center else \end{matrix}

δ ∈ (0, 1) is a very small positive constant .…”

Section: Observer‐based Adaptive L2 Disturbance Attenuation Controlmentioning

confidence: 99%

“…For the validation of the proposed adaptive controller, the simulation comparison results among the following four strategies including the passive case, LQR control and adaptive control in will be given. To this end, the corresponding controllers are first given as follows: LQR controller

({, \begin{array}{l} left & z = ([], - K_{1} X - c_{0} ((), x_{2} - x_{4}) - k_{3} ((), x_{1} - x_{3}) - k_{4} ((), {\overset{true˙}{x}}_{2} - {\overset{true˙}{x}}_{4}) - f_{0}) true/ α \\ left & i = ({}, - η + ([], \overset{z}{true} + λ (||, x_{2} - x_{4}) z (||, z) + β ((), x_{2} - x_{4}) z^{2}) true/ d) true/ μ \end{array})

where, K 1 is an optimal output feedback gain matrix.…”

Section: Simulation Validationmentioning

confidence: 99%

“…To this end, the corresponding controllers are first given as follows: LQR controller

({, \begin{array}{l} left & z = ([], - K_{1} X - c_{0} ((), x_{2} - x_{4}) - k_{3} ((), x_{1} - x_{3}) - k_{4} ((), {\overset{true˙}{x}}_{2} - {\overset{true˙}{x}}_{4}) - f_{0}) true/ α \\ left & i = ({}, - η + ([], \overset{z}{true} + λ (||, x_{2} - x_{4}) z (||, z) + β ((), x_{2} - x_{4}) z^{2}) true/ d) true/ μ \end{array})

where, K 1 is an optimal output feedback gain matrix. Adaptive inverse controller in

({, \begin{array}{l} left & \overset{\overset{true¯}{F}}{true} = {\overset{true^}{\overset{θ}{true}}}^{T} φ, \overset{\overset{true^}{\overset{θ}{true}}}{true} = - \overset{Γ}{true} ϕ_{N} \overset{ε}{true}, \overset{\overset{true¯}{Γ}}{true} = λ_{1} \overset{Γ}{true} - λ_{2} \overset{Γ}{true} ϕ_{N} ϕ_{N}^{T} \overset{Γ}{true}, ϕ_{N} = φ true/ ([], {((), ρ + φ^{T} φ)}^{0.5}) \\ left & z = ([], F - {\overset{true^}{c}}_{0} ((), x_{2} - x_{4}) - {\overset{true^}{k}}_{3} ((), x_{1} - x_{3}) - {\overset{true^}{k}}_{4} ((), {\overset{true˙}{x}}_{2...})) \end{array})

…”

Section: Simulation Validationmentioning

confidence: 99%

“…To further illustrate the effectiveness of the proposed control strategy, the comparisons are also given by utilizing the RMS

x_{R M S} = \sqrt{\frac{1}{T} true {true\int}_{0}^{T} x^{2} ((), t) d t}

conception in for statistic characteristic of the performance evaluation indexes, where T is the period of simulation, among the passive case, LQR control, controller, controller, controller and the proposed adaptive controller, shown in Table Table and Table show the RMS of the performance indexes when the system parameters are varying. Table is the result that the body mass has two times variation, which mainly aims at the parameter perturbation discussed in .…”

Section: Simulation Validationmentioning

confidence: 99%

See 4 more Smart Citations

Observer‐Based Adaptive L₂ Disturbance Attenuation Control of Semi‐Active Suspension with MR Damper

Cheng

Jiao

2016

Asian Journal of Control

View full text Add to dashboard Cite

A novel nonlinear observer‐based adaptive disturbance attenuation control strategy was proposed for a quarter semi‐active suspension system with a magneto‐rheological (MR) damper in light of the intrinsic nonlinearity, parameter uncertainty, state immeasurability and road randomness. Adaptive adjusting parameters were adopted to avoid the curve fitting and identification of the system parameters by a great deal of experimental data for shortening the development cycle of the control system. Based on the reduced‐order observer, the system states including the immeasurable virtual state of MR damper and inconveniently measured states of suspension system were estimated for the realistic frame of the proposed controller in practice. The dissipative system theory was utilized to reduce the influence of the road disturbance on the system control performance. Simulation results in the bump road and B‐class road indicate that, whether there are perturbations of the system parameters or not, the proposed control scheme always ensures a better performance on the suspension travel, ride comfort and handling stability in comparison with other existing methods.

show abstract

Section: Suspension System With Mr Damper and Control Problemmentioning

confidence: 99%

“…γ , γ 1 ∈ (0, 1) represent the attenuation level for external and interior disturbance. d is defined as

d = ({, \begin{array}{c} center & x_{2} - x_{4} \\ center & δ \end{array}) \begin{matrix} center \\ center, \\ center, \end{matrix} \begin{matrix} center \\ center |x_{2} - x_{4}| > δ \\ center else \end{matrix}

δ ∈ (0, 1) is a very small positive constant .…”

Section: Observer‐based Adaptive L2 Disturbance Attenuation Controlmentioning

confidence: 99%

({, \begin{array}{l} left & z = ([], - K_{1} X - c_{0} ((), x_{2} - x_{4}) - k_{3} ((), x_{1} - x_{3}) - k_{4} ((), {\overset{true˙}{x}}_{2} - {\overset{true˙}{x}}_{4}) - f_{0}) true/ α \\ left & i = ({}, - η + ([], \overset{z}{true} + λ (||, x_{2} - x_{4}) z (||, z) + β ((), x_{2} - x_{4}) z^{2}) true/ d) true/ μ \end{array})

where, K 1 is an optimal output feedback gain matrix.…”

Section: Simulation Validationmentioning

confidence: 99%

“…To this end, the corresponding controllers are first given as follows: LQR controller

({, \begin{array}{l} left & z = ([], - K_{1} X - c_{0} ((), x_{2} - x_{4}) - k_{3} ((), x_{1} - x_{3}) - k_{4} ((), {\overset{true˙}{x}}_{2} - {\overset{true˙}{x}}_{4}) - f_{0}) true/ α \\ left & i = ({}, - η + ([], \overset{z}{true} + λ (||, x_{2} - x_{4}) z (||, z) + β ((), x_{2} - x_{4}) z^{2}) true/ d) true/ μ \end{array})

where, K 1 is an optimal output feedback gain matrix. Adaptive inverse controller in

({, \begin{array}{l} left & \overset{\overset{true¯}{F}}{true} = {\overset{true^}{\overset{θ}{true}}}^{T} φ, \overset{\overset{true^}{\overset{θ}{true}}}{true} = - \overset{Γ}{true} ϕ_{N} \overset{ε}{true}, \overset{\overset{true¯}{Γ}}{true} = λ_{1} \overset{Γ}{true} - λ_{2} \overset{Γ}{true} ϕ_{N} ϕ_{N}^{T} \overset{Γ}{true}, ϕ_{N} = φ true/ ([], {((), ρ + φ^{T} φ)}^{0.5}) \\ left & z = ([], F - {\overset{true^}{c}}_{0} ((), x_{2} - x_{4}) - {\overset{true^}{k}}_{3} ((), x_{1} - x_{3}) - {\overset{true^}{k}}_{4} ((), {\overset{true˙}{x}}_{2...})) \end{array})

…”

Section: Simulation Validationmentioning

confidence: 99%

“…To further illustrate the effectiveness of the proposed control strategy, the comparisons are also given by utilizing the RMS

x_{R M S} = \sqrt{\frac{1}{T} true {true\int}_{0}^{T} x^{2} ((), t) d t}

Section: Simulation Validationmentioning

confidence: 99%

See 3 more Smart Citations

Observer‐Based Adaptive L₂ Disturbance Attenuation Control of Semi‐Active Suspension with MR Damper

Cheng

Jiao

2016

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

Modified active disturbance rejection control for non-linear semi-active vehicle suspension with magneto-rheological damper

Cheng

Jiao

2017

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This paper presents a novel idea processing the complex non-linear dynamics of a magneto-rheological (MR) damper and the external road disturbance based on the linear extended state observer (LESO) technology, and further verifies its reasonability by application of linear active disturbance rejection control (LADRC) in the quarter-car non-linear semi-active suspension system. In order to optimize the body acceleration and dynamic tyre load to improve the ride comfort and road-handling ability, a modified active disturbance rejection control, the double linear active disturbance rejection control (DLADRC), is further proposed based on the idea of the hybrid skyhook–groundhook control strategy. LESO is used to estimate the total disturbance including the external road disturbance and the internal non-linear dynamic of the MR damper. For effectiveness validation of the proposed control scheme, comparison results with the existing linear quadratic regulation (LQR) control, hybrid skyhook–groundhook control and adaptive control strategies are presented for the same quarter-car semi-active suspension. It is shown from the simulation comparisons among these several control strategies that the semi-active suspension system with DLADRC has a better control performance on the ride comfort and road-handling ability corresponding to the body acceleration and dynamic tyre load.

show abstract

Stability-assured robust adaptive control of semi-active suspension systems

Cited by 2 publications

References 18 publications

Observer‐Based Adaptive L₂ Disturbance Attenuation Control of Semi‐Active Suspension with MR Damper

Observer‐Based Adaptive L₂ Disturbance Attenuation Control of Semi‐Active Suspension with MR Damper

Modified active disturbance rejection control for non-linear semi-active vehicle suspension with magneto-rheological damper

Contact Info

Product

Resources

About

Stability-assured robust adaptive control of semi-active suspension systems

Cited by 2 publications

References 18 publications

Observer‐Based Adaptive L2 Disturbance Attenuation Control of Semi‐Active Suspension with MR Damper

Observer‐Based Adaptive L2 Disturbance Attenuation Control of Semi‐Active Suspension with MR Damper

Modified active disturbance rejection control for non-linear semi-active vehicle suspension with magneto-rheological damper

Contact Info

Product

Resources

About

Observer‐Based Adaptive L₂ Disturbance Attenuation Control of Semi‐Active Suspension with MR Damper

Observer‐Based Adaptive L₂ Disturbance Attenuation Control of Semi‐Active Suspension with MR Damper