Nonlinear Control Design of a Half-Car Model Using Feedback Linearization and an LQR Controller

Khan, Muhammad Aseer; Abid, Muhammad; Ahmed, Nisar; Wadood, Abdul; Park, Herie

doi:10.3390/app10093075

Cited by 18 publications

(10 citation statements)

References 19 publications

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“…Therefore, theoretically, t feedback linearization theory is one of the most suitable theories to solve the control design problem of the buck-boost converter. However, not all nonlinear systems can linearized into a linear system by feedback, which needs complex operation proof [2 Nevertheless, the feedback linearization theory is still widely used in the power syste [26][27][28][29], power electronics [30,31], chemical industry [32,33], vehicles [34,35], and ae space [36,37].…”

Section: The Model Of the Buck-boost Convertermentioning

confidence: 99%

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A Multi-Index Feedback Linearization Control for a Buck-Boost Converter

Chen

2021

Energies

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Due to the nonlinear and nonminimum phase characteristics of the buck-boost converter, the design of its controller has always been a challenging problem. In this paper, a multi-index feedback linearization control strategy is proposed to design the controller of the buck-boost converter. Firstly, by constructing an appropriate output function, the original nonlinear system is mapped into a combination of a linear subsystem and a nonlinear subsystem. Then, according to the structural characteristics of these two subsystems, the linear optimal control theory is adopted for the control design of the linear subsystem to make it have a good output performance, while for the nonlinear subsystem, the coefficient of the output function is adjusted to ensure its stability. Finally, based on the Hartman–Grobman theorem, the internal mechanism and coefficient adjustment basis of the proposed method are revealed; that is, by adjusting the coefficient of the output function and the feedback coefficient of the linear control law, the poles of the system are configured to achieve the purpose of adjusting the static and dynamic performance of the system. The simulation results show the feasibility and superiority of using the multi-index feedback linearization control strategy to design the nonlinear control law of the buck-boost converter.

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Section: The Model Of the Buck-boost Convertermentioning

confidence: 99%

“…Substituting (34) into (31), and making z 1 = 0, the zero-dynamic equation can be obtained as follows:…”

Section: Analysis Of Zero Dynamic Stability Of the Buck-boost Convertermentioning

confidence: 99%

A Multi-Index Feedback Linearization Control for a Buck-Boost Converter

Chen

2021

Energies

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“…The results showed that, compared with the passive suspension system, both the active CM system and the active ADM system are able to reduce seat acceleration to varying degrees, improving the ride comfort and road holding. Aiming at the non-linear problem of active suspension system, Khan et al [ 3 ] proposed an improved half-car model control method. The input/output feedback linearization method was used to transform the nonlinear half-car model system into an equivalent linear system, and then an LQR controller was designed.…”

Section: Introductionmentioning

confidence: 99%

Active Suspension Control Strategy of Multi-Axle Emergency Rescue Vehicle Based on Inertial Measurement Unit

Guo

Zhao

et al. 2021

Sensors

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Active suspension control strategies are a top priority in active suspension system. The current research on active suspension control strategies is mostly focused on two-axle vehicles, and there is less research investigating multi-axle vehicles. Additionally, their effective implementation is dependent on accurate mathematical models, and most of them adopt force feedback control, which is vulnerable to external interference. To solve these problems, this paper proposes an active suspension control strategy based on Inertial Measurement Unit. The multi-axle emergency rescue vehicle is made to be equivalent to a 3-degrees-of-freedom parallel mechanism by using the method of grouping and interconnecting the suspension units of the whole vehicle. The attitude change of the vehicle body was transformed into the servo actuator’s displacement by solving the inverse solution of the parallel mechanism position and the action of the servo actuator was driven in reverse according to the displacement obtained. In this way, the vehicle body attitude can be compensated, and the ride comfort and the handling stability of the vehicle can be improved. To verify the effectiveness of the control strategy proposed, the three-axle six vehicle was taken as the research object, the position inverse solution of its equivalent 3-degrees-of-freedom parallel mechanism was deduced, and a high-pass filter was designed. The three-axle vehicle experiment platform integrating active suspension and hydro-pneumatic suspension was built, and the gravel road and slope road experiments were carried out and the results compared with those obtained with hydro-pneumatic suspension. The experiment results showed that, compared with hydro-pneumatic suspension, the active suspension control strategy based on Inertial Measurement Unit proposed in this paper can not only stabilize the body attitude, but also effectively suppress body vibration, improving the ride comfort and handling stability of the vehicle significantly.

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“…Many experts have conducted many studies on the algorithms, including adaptive PID control [1], fuzzy control [2], sliding mode control [3], neural networks [4], reinforcement learning [5], etc. Besides, the LQR theory, as one of the earliest and most mature control algorithms in modern control theory, has been studied deeply [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

Jiang

et al. 2021

Applied Sciences

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The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input model are established. The weights of an LQR controller are optimized using a genetic algorithm. Then, a possible weighting space is constructed based on this optimal solution. Random weighting coefficients of each performance index are generated in this space. Next, LQR control for the 1/4 vehicle model is performed, and the simulation data are recorded automatically, with these random weighting values, different road classes, and driving speed. A machine learning dataset is built from these simulations. Finally, a K-means clustering algorithm is used to classify the LQR control active suspension into three performance modes: safety mode, comprehensive mode, and comfort mode. The optimal weighting matrix of each performance mode is determined to satisfy requirements for different types of drivers. The results show that the new GKL algorithm not only improves the suspension control effect but also realizes different performance modes. It can better adapt to the changes in driving conditions and drivers.

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Nonlinear Control Design of a Half-Car Model Using Feedback Linearization and an LQR Controller

Cited by 18 publications

References 19 publications

A Multi-Index Feedback Linearization Control for a Buck-Boost Converter

A Multi-Index Feedback Linearization Control for a Buck-Boost Converter

Active Suspension Control Strategy of Multi-Axle Emergency Rescue Vehicle Based on Inertial Measurement Unit

Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

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