Smith Predictor‐Taylor Series‐Based LQG Control for Time Delay Compensation of Vehicle Semiactive Suspension

Tao, Laihua; Chen, Shi-An; Fang, Guisheng; Guanghao, Zu

doi:10.1155/2019/3476826

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…As the key to achieve adaptive performance of MR intelligent suspension, the design of control algorithm in MR intelligent suspension research is the core issue. A series of control algorithms are proposed by researchers, including skyhook control algorithm [6], LQR control algorithm [7,8], neural network control algorithm [9,10], fuzzy control algorithm [11,12], robust control [13], human simulated intelligent control algorithm [14] and PID control algorithm [15,16] and so on. These algorithms usually take the absolute velocity of the sprung mass and relative velocity of the damper as the feedback variables of the controller, and the absolute velocity are obtained by acceleration integral.…”

Section: Introductionmentioning

confidence: 99%

Incremental proportion integration differentiation control of all-terrain vehicle magnetorheological suspension system under low-frequency disturbances

Xia

et al. 2023

Smart Mater. Struct.

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In the intelligent control system of magnetorheological (MR) suspension of all-terrain vehicle(ATV), the low-frequency disturbance(LFD) in the measured feedback signal (acceleration) makes the controller unable to calculate the theoretical control force accurately. Especially, when the frequency of the LFD signal is close to that of the actual acceleration signal, the LFD can’t be filtered by designing a traditional filter. Based on the above questions, this paper proposes an incremental proportion integration differentiation (IPID) strategy to address the issue of LFD in the measured feedback acceleration signal of the MR suspension system of ATV. First of all, the model of 1/4 vehicle suspension in consideration of LFD is established, the source of LFD is analyzed which is due to the transformation of Coriolis acceleration under the condition of vehicle body pitch and roll. Next, a semi-active IPID controller is designed by utilizing differential derivation of discrete PID and semi-active principle to eliminate the LFD component mixed in the acceleration signal by making a difference. The particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the controller, which is then verified through numerical simulation. Subsequently, a real vehicle control experiment is carried out based on a 4x4 ATV equipped with the MR suspension system and implemented by DSP controller with the designed IPID algorithm. The effectiveness of the prosed method is evaluated under the speed 10km/h and E road. And the designed method is compared with the traditional PID control algorithm through simulation and experimentation to demonstrate the superiority and rationality of "filtering out" LFD signal and improving control effectiveness.

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Section: Introductionmentioning

confidence: 99%

Incremental proportion integration differentiation control of all-terrain vehicle magnetorheological suspension system under low-frequency disturbances

Xia

et al. 2023

Smart Mater. Struct.

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“…Simulation and experiment showed that the control algorithm could improve the vibration damping performance of the vehicle well. Tao et al [15] proposed a Smith predictor-Taylor series-based LQG (TLQG) control method to compensate the time delay of a semi-active suspension system. The time delay was divided into two parts, one half of which was compensated with a TLQG controller, and the other was further compensated by a TLQG-based Smith predictor.…”

Section: Introductionmentioning

confidence: 99%

Gray skyhook predictive control of magnetorheological semi-active seat suspension with time delay

Dong,

Fei,

Zhang

et al. 2023

Smart Mater. Struct.

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Semi-active seat suspension with a magnetorheological (MR) damper has been a popular study issue in recent years. Since the response time delay of the MR damper can reduce the control effect and even make the vibration more severe, there is an urgent need to compensate for the response time delay. In this paper, gray prediction is introduced into the vibration control study of commercial vehicle seat suspension. A gray skyhook prediction controller is designed to compensate for the response time delay. This proposed controller does not rely heavily on controller parameter optimization and requires fewer state variables than other typical time delay compensation controllers. The mechanical property of the MR damper has been tested, modelled, and analysed. A seat suspension dynamic model considering geometric nonlinearity is established based on the motion relationship between the components of the suspension. Subsequently, the effect of the response time delay on seat suspension vibration control has been verified by simulation. The results show that the control effect deteriorates significantly after considering the response time delay. Finally, prediction accuracy and vibration reduction performance of the designed gray skyhook predictive controller are verified by simulation and bench testing. The results illustrate that the gray skyhook predictive controller provides excellent time delay compensation and can significantly improve the ride comfort and handling stability of the driver. This study provides a reference for vibration control research of the MR seat suspension.

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“…For example, Yue Zhu and Sihong Zhu [13] developed a quartervehicle suspension equipped with an MR damper with a time delay using an adaptive neural network structure. Laihua Tao et al [14] applied a Smith predictor-Taylor seriesbased LQG control to compensate for the time delay of a vehicle's semi-active suspension. Young-Tai Choi et al analyzed MR dampers [15] and developed the controller for the landing gear system of a helicopter equipped with MR dampers.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Control and a Model Predictive Control for a Single Landing Gear Equipped with a Magnetorheological Damper

Le,

Park,

Kim

et al. 2023

Aerospace

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Aircraft landing gear equipped with a magnetorheological (MR) damper is a semi-active system that contains nonlinear behavior, disturbances, uncertainties, and delay times that can have a huge impact on the landing’s performance. To solve this problem, this paper adopts two types of controllers, which are an intelligent controller and a model predictive controller, for a landing gear equipped with an MR damper to improve the landing gear performance considering response time in different landing cases. A model predictive controller is built based on the mathematical model of the landing gear system. An intelligent controller based on a neural network is designed and trained using a greedy bandit algorithm to improve the shock absorber efficiency at different aircraft masses and sink speeds. In this MR damper, the response time is assumed to be constant at 20 ms, which is similar to the response time of the commercial MR damper. To verify the efficiency of the proposed controllers, numerical simulations compared with a passive damper and a skyhook controller in different landing cases are executed. The major finding indicates that the suggested controller performs better in various landing scenarios than other controllers in terms of shock absorber effectiveness and adaptability.

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Smith Predictor‐Taylor Series‐Based LQG Control for Time Delay Compensation of Vehicle Semiactive Suspension

Cited by 7 publications

References 26 publications

Incremental proportion integration differentiation control of all-terrain vehicle magnetorheological suspension system under low-frequency disturbances

Incremental proportion integration differentiation control of all-terrain vehicle magnetorheological suspension system under low-frequency disturbances

Gray skyhook predictive control of magnetorheological semi-active seat suspension with time delay

An Intelligent Control and a Model Predictive Control for a Single Landing Gear Equipped with a Magnetorheological Damper

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