Research on Robust Model Predictive Control for Electro-Hydraulic Servo Active Suspension Systems

Wang, Dazhuang; Zhao, Dan; Gong, Mingde; Yang, Bin

doi:10.1109/access.2017.2787663

Cited by 46 publications

(21 citation statements)

References 35 publications

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“…To solve the problem, researchers have studied different modern control strategies. In addition to the traditional PID control strategy, they also adopt control strategies such as SDC (Skyhook Damper Control) [1], LQG (Linear Quadratic Gaussian) control [2], [3], fuzzy control [4], [5], SMC (Sliding Mode Control) [6], H∞ control [7], [8], adaptive control [9], [10], predictive control [11], [12], neural network control [13], [14], nonlinear intelligent control [15], and compound control [16], to reduce the vibration amplitude of vehicle body and improve the vehicle ride smoothness, stability, and ride comfort.…”

Section: Introductionmentioning

confidence: 99%

“…Milanese et al [11] presented a fast model predictive control (FMPC) implementation method based on nonlinear function approximation technology to solve the problem of fast calculation of predictive control rate, and its effectiveness is verified by simulation. Wang et al [12] designed a robust model predictive controller (RMPC) for active suspension system by defining the performance evaluation function of RMPC. The accuracy and effectiveness of the controller were verified by prototype vehicle simulation and road test.…”

Section: Introductionmentioning

confidence: 99%

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Semi-Active Suspension Control Based on Deep Reinforcement Learning

Liu

Rong

et al. 2020

IEEE Access

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The performance of vehicle body vibration and ride comfort of active or semi-active suspension with proper control is better than that with passive suspension. The key to achieve good control effect is that the suspension control system should have strong real-time learning ability according to changes in the road surface and suspension parameters. In the control strategies adopted by previous researchers, the classical neural network controller has some learning ability, but it is mainly based on offline learning with a large number of samples. In this paper, the deep reinforcement learning strategy is used to solve the above problems .Aiming at the continuity of state space and execution action in vehicle active suspension system, the control of the semi-active suspension is realized by using improved DDPG (Deep Deterministic Policy Gradient) algorithm. To overcome the shortcoming of low efficiency of this algorithm in the initial stage of learning, the DDPG algorithm is improved and using empirical samples in the learning method is proposed. Based on Mujoco, the physical model of semi-active suspension is established, and its dynamic characteristics are analyzed under the condition of various road level and vehicle speed. The simulation results show that compared with the passive suspension, the semi-active suspension based on improved DDPG algorithm with learning method using experienced samples can better adapt to various road level, more effectively reduce the vertical acceleration of the vehicle body and the dynamic deflection of the suspension, and further improve the ride comfort. INDEX TERMS Semi-active suspension, deep reinforcement learning, DDPG, experienced samples.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semi-Active Suspension Control Based on Deep Reinforcement Learning

Liu

Rong

et al. 2020

IEEE Access

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“…The main reasons for this are their extremely high power consumption requirements. Concepts [17][18][19][20][21], as well as research work on the control of active suspension systems [21][22][23][24][25][26][27][28][29], can be found in the literature. There have also been a few approaches to the design of high-level controllers for active suspension that have dealt with the delays and failures of actuators [30,31].…”

Section: Introductionmentioning

confidence: 99%

Active Shock Absorber Control Based on Time-Delay Neural Network

2020

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A controlled suspension usually consists of a high-level and a low-level controller. The purpose the high-level controller is to analyze external data on vehicle conditions and make decisions on the required value of the force on the shock absorber rod, while the purpose of the low-level controller is to ensure the implementation of the desired force value by controlling the actuators. Many works have focused on the design of high-level controllers of active suspensions, in which it is considered that the shock absorber can instantly and absolutely accurately implement a given control input. However, active shock absorbers are complex systems that have hysteresis. In addition, electro-pneumatic and hydraulic elements are often used in the design, which have a long response time and often low accuracy. The application of methods of control theory in such systems is often difficult due to the complexity of constructing their mathematical models. In this article, the authors propose an effective low-level controller for an active shock absorber based on a time-delay neural network. Neural networks in this case show good learning ability. The low-level controller is implemented in a simplified suspension model and the simulation results are presented for a number of typical cases.Energies 2020, 13, 1091 2 of 16 of a semi-active shock absorber lies in I and III quadrants; thus, the force vector F is always in one direction with the velocity vector v.Energies 2020, 13, 1091 2 of 15In an active shock absorber, the set of dissipative characteristics cover all IV quadrants due to an external energy source, and force and velocity vectors can be oppositely directed. This property is the great advantage of active shock absorbers, and it allows, in theory, the achievement of a complete absence of movement of the vehicle body for the given planes.

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“…With the development of digital technology, control theory, pattern recognition technology, and electronic technology, EHSSs have become one of the most popular topics in the fields of both scientific research and industrial engineering [2,3]. EHSSs including a position servo control system and a force servo control system are a typical closed-loop torque control system [4,5].…”

Section: Introductionmentioning

confidence: 99%

Optimized PID Controller Based on Beetle Antennae Search Algorithm for Electro-Hydraulic Position Servo Control System

Fan

Shao

Sun

2019

Sensors

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To improve the controllability of an electro-hydraulic position servo control system while simultaneously enhancing the anti-jamming ability of a PID controller, a compound PID controller that combines the beetle antennae search algorithm with PID strategy was proposed, and used to drive the position servo control system of the electro-hydraulic servo system. A BAS-PID controller was designed, and the beetle antennae search algorithm was used to tune PID parameters so that the disturbance signal of the system was effectively restrained. Initially, the basic mathematical model of the electro-hydraulic position servo control system was established through theoretical analysis. The transfer function model was obtained by identifying system parameters. Then, the PID parameter-tuning problem was converted into a class of three-dimensional parameter optimization problem, and gains of PID controllers were adjusted using the beetle antennae search algorithm. Finally, by comparing the effectiveness of different algorithms, simulation and experimental results revealed that the BAS-PID controller can greatly enhance the performance of the electro-hydraulic position servo control system and inhibit external disturbances when different interference signals are used to test the system’s robustness.

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Research on Robust Model Predictive Control for Electro-Hydraulic Servo Active Suspension Systems

Cited by 46 publications

References 35 publications

Semi-Active Suspension Control Based on Deep Reinforcement Learning

Semi-Active Suspension Control Based on Deep Reinforcement Learning

Active Shock Absorber Control Based on Time-Delay Neural Network

Optimized PID Controller Based on Beetle Antennae Search Algorithm for Electro-Hydraulic Position Servo Control System

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