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Semi Active Suspension Control Design For Vehicles
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What Are The 3 Types Of Suspension Systems? — Tevema Bv
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By Kevin Herubiel Floreán-Aquino Kevin Herubiel Floreán-Aquino Scilit Preprints.org Google Scholar View Publications 1 , Manuel Arias-Montiel Manuel Arias-Montiel Scilit Preprints.org Google Scholar View Publications 2, * , Jesús Linares-Flores Jesús Linares Preprints.org Google Scholar View Publications 2 , José Gabriel Mendoza-Larios José Gabriel Mendoza-Larios Scilit Preprints.org Google Scholar View Publications 3 and Álvaro Cabrera-Amado Álvaro Cabrera-Amado Scilit Preprints.org Google Scholar View Publications 4
This article is an extended version of our paper published in Florean-Aquino, K.H.; Arias-Montiel, M.; Lugo-Gonzalez, E.; Cabrera-Amado, A. Single and multi-position positive control of magnetorheological vehicle suspensions. In Proceedings of the National Congress on Automatic Control, Puebla, Mexico, 23–25 October 2019.
Tenneco Advanced Suspension Technologies
Received: 29 December 2020 / Revised: 16 January 2021 / Accepted: 22 January 2021 / Published: 27 January 2021
This paper describes a modern semi-active control system for a car’s quarter-wheel suspension with a magnetorheological damper (MRD) to dampen vibrations and improve passenger comfort and road holding. The first solution is a multiple positive control system (MPPF) to reduce the vibration amplitude at two modal frequencies. The second solution is based on the basic passivity assumption of false passive output with respect to false errors. Passive feedback is used to improve control objectives. Finally, the third solution is related to the exclusion control (DRC) based on the state controller. The three proposed control schemes consider the inverse polynomial model of the commercial MRD for numerical application and are evaluated using the comfort and road performance indicators proposed in the literature. . In addition, the effect of the variation of the amount of sprung (simulating the number of passengers) on the performance of the controller. Numerical results show in two cases (constant and variable output mass) that passivity-based control (PBC) and DRC improve the performance parameters compared to classical sky-hook control and open-loop system with constant current input for MRD. . The results obtained for compression force and power consumption are within the range of commercial MRD considered viable for the experimental application of the proposed control scheme.
Rheological actuators (RA) are the best semi-active devices for dissipating energy in systems under load. These actuators contain fluids that can change the rheological system (producing stress and apparent viscosity) through the action of an electric field (electrorheological) or a magnetic field (magnetorheological) [1]. According to [2], RA is a passive dissipation device because it does not provide energy to the controlled system. In addition, low power consumption, large power, high bandwidth, low cost, power control and fast response time make magnetorheological dampers (MRD) a viable option for vibration control systems in civil engineering applications, impact maintenance and isolation technology in industrial engineering. and heavy vehicles, as well as advanced prosthetics in the biomedical field [3, 4, 5]. In automotive applications, MRD is used in the design of electronic suspension systems (ES) to reduce the transmission of mechanical vibrations caused by the lack of an unknown path that improves passenger comfort (mainly related to transmitted vibrations to the passenger in the non-compliance, in this work it is measured. through the displacement of the chassis) as well as the road holding of the vehicle (related to the deflection of the wheels , measured by the difference in wheel displacement and movement caused by the road profile) [6, 7]. The automotive ES based on magnetorheological dampers (MRD) has shown excellent performance. In addition, they offer other important characteristics such as response delays in the order of milliseconds and low power consumption [6, 7, 8]. In the classification of controllable suspension systems presented by Savaresi et al. [7], the typical power consumption for a type of electronically controlled suspension is given. According to this information, the semi-active suspension system, mainly based on RA, has a power demand of a few tens of Watts, while the demand of active suspension power is around hundreds of kilowatts. The input to the MRD control is an electric current from the coil that induces a magnetic field in the magnetorheological fluid (MRF). Regarding the characteristics of MDR, a parametric model has been developed to describe the hysteretic behavior of the velocity-curve [4, 9]. In addition, a non-parametric model is available to describe the hysteretic loop as a polynomial speed function. This type of model allows the expression of the electric power as a function of the desired pressure in semi-active control (SAC) applications [10]. In this case, the execution of the MRD suspension depends entirely on the characteristics of the SAC system. The conventional SAC strategy is based on continuous switching according to two different modes of operation: comfort or lane keeping. Some examples of these methods are Ground-hook (road-holding), Acceleration Driven Damper (comfort), sky-hook (comfort) and SH-ADD (comfort and road-holding) [7]. Conventional SAC presents some disadvantages such as high frequency communication that can stimulate unmodeled dynamics of mechanical systems and a reduced bandwidth that limits the possibility of improving passenger comfort and keeping track together [6, 11]. Instead of overcoming the limitations of conventional SAC, modern SAC systems have emerged mainly based on the Linear Parameter Varying (LPV) approach [8], optimal control theory [12], robust control [ 13, 14], adaptive control [15] , modal and multi-modal control [16, 17] and fault tolerant control [18].
In model-based control systems, unknown and unmeasurable external disturbances greatly affect the performance of closed-loop systems. In this regard, an open state controller (ESO) has been proposed with an active disturbance rejection control (ADRC) method [19] to estimate the unmeasured state and the unknown disturbance. and also to satisfy, and be strong with the supervisor. Recently, ESO has found applications in uncertain mechanical systems with various control systems [ 20 , 21 , 22 , 23 ].
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According to the reviewed literature, one of the main shortcomings of the conventional SAC system is its inability to improve passenger comfort and road maintenance at the same time. In addition, some modern SAC projects have been found to be successful in large scale vibration models [13, 14, 15, 16, 18], and only a few works consider MR damper dynamics [8, 15]. Additionally, the effect of system parameter variations on controller performance is rare, even in robust controller systems. This work is an extended and improved version of the results reported in [17] and presents the design of three modern control schemes for the suspension of a quarter car with MRD considering the dynamics actuator from the previously defined inverse polynomial model. In addition, the control system is evaluated by the parameters of comfort and road holding, while the change in the mass produced (simulating the number of passengers) is taken into account in the performance the supervisor. The methodology used in this article is shown in Figure 1. The development of the research is divided into four phases: modeling, covering the suspension dynamics as well as the polynomial model characteristic of MRD; control design, which provides the theoretical basis to combine the proposed SAC projects; simulation, where the proposed SAC scheme is numerically proven considering the variation of the mass value; and evaluation by performance index for comfort and road holding. The main objective of the proposed control is to achieve a high level of passenger comfort as well as to achieve vehicle stability (road holding). The first solution is taken from [17] and uses a modal modulation scheme to expand and control the first dynamic through a virtual filter corresponding to the largest modal frequency. Another solution based on the assumption of the basic passivity of the false passive output of the false error is presented using a Hamiltonian design to improve the two objectives of low power consumption. Finally, a robust adaptive controller based on non-uniform flatness is developed that incorporates ESO to add robustness and adaptability to the original controller system. All these proposed strategies are implemented numerically by considering the inverse polynomial model of commercial MRD and evaluated for performance.
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