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1 International Conference on Electrical, Control and Computer Engineering Pahang, Malaysia, June 21-22, 2011 A Review on Control Strategies for Passenger Car Intelligent Suspension System Noor H. Amer, Rahizar Ramli Wan Nor Liza Mahadi Mohd. Azman Zainul Abidin Department of Mechanical Department of Electrical Proton Professor Office Engineering Engineering PROTON Research Department Universiti Malaya, Malaysia Universiti Malaya PROTON Headquarters [email protected], Kuala Lumpur, Malaysia Shah Alam, Malaysia [email protected] [email protected] [email protected] Abstract The main function of a suspension system is to requirement and simpler solution. Main difference from active reduce/isolate vibration caused by road irregularities to improve suspension is that semi-active suspension may not exert comfort, reliability and road holding of the vehicle. In this paper, additional force. In other words, while active suspension can various control aspects in controllable suspension are reviewed dissipate and generate energy within the system, semi-active focusing on suspension performance criteria, control strategies can only dissipate energy. The actuator in active suspension is and control methodologies. This includes the implementation of software-in-the-loop (SIL) and hardware-in-the-loop (HIL) replaced with a controllable damper that will offer variable simulations. Based on these reviews, a study on a suspension damping which provide better solution than passive systems. system control strategy with implementation of the HIL and SIL Variable damping is achieved by varying resistant to fluid simulations approach is proposed for future works. flow in the damper by controlling valves opening or smart fluids (electro-rheological and magneto-rheological fluids). Keywordsactive suspension, semi-active suspension, control In this paper, Section II reviews common performance strategy, software-in-the loop , hardware-in-the-loop, skyhook criterion usually used in previous researches. Section III will I. INTRODUCTION discuss control strategies divided into two subtopics, linear and nonlinear systems and Section IV will discuss Suspension system improves passengers comfort in the computational simulation employed in suspension studies. vehicle and decreases the damage for vehicle components caused by excessive external vibration. For the past few decades, suspension system for passenger vehicles has been dominated by the conventional, passive suspension. As shown in Figure 1, they can be modeled by a spring and damper in parallel; both with stiffness and damping coefficients k1 and c1, connecting the wheels (unsprung mass with coefficients k2 and c2) and the vehicle body (sprung mass). While its simplicity and cost effective features has been proven beneficial, it can only provide limited damping and stiffness to the vehicle during maneuvers on irregular road surfaces. Thanks to the increasing availability of supercomputers and fast processors, controllable suspensions have been explored extensively in the past few decades to provide variable damping and forces to cater to these needs [1-5]. These systems can be categorized into two main types; semi active Figure 1: Conventional passive Quarter-car suspension model and active suspension systems. Active suspension system has been studied since 1930s [6]. It contains an actuator (usually hydraulic, pneumatic or II. SUSPENSION PERFORMANCE CRITERIA electromechanical) to produce force in the suspension system to control the motion of the sprung mass and relative velocity For any suspension control strategy proposed, a set of between sprung and unsprung mass. Studies on this system criteria has to be imposed to quantify the quality of the can be found in [5, 7-12] and it has been implemented into strategy. Also, this criterion will be the optimizing objective in actual cars by several car manufacturers [13, 14]. However, performance index in cases where optimal control is to be few drawbacks have been highlighted mainly due to its high proposed (see section III). There are few common criteria cost and large power supply requirement. This leads to the usually employed in suspension studies; concept of semi-active suspension proposed by Karnopp et al Body (sprung mass) acceleration which relates to [15] which provides more economical, lower power passengers comfort or ride characteristics, 978-1-61284-230-1/11/$26.00 2011 IEEE 404

2 Suspension displacement which defines the III. CONTROL STRATEGIES IN CONTROLLABLE SUSPENSION suspension travel range and also design requirement One of the main challenges in controllable suspension is to for suspension space choose the most suitable control strategy for the system. This Tire (unsprung mass) deflection or tire loading that section will review the implementation of several control relates to the road holding. strategies in the design of active and semi-active suspension over the years based on the linearity of suspension/vehicle The characteristics can be expressed as transfer function model of the system to be controlled. over road displacement and velocity respectively [16]. Any system is linear when the input and output are However, most works used RMS values to quantify the proportional to each other. For example, spring and dampers characteristics. Kruczek et al. [9] used RMS value of wheel- in passive suspension has a linear characteristic and road displacements to quantify road holding ability, Jstab and proportional to displacement and velocity respectively. body acceleration to quantify the comfort characteristics, Additionally, active suspension studies may use electric linear JcomfISO for active suspension control they proposed. JcomfISO motor [9] as actuator to maintain linearity in the vehicle also includes transfer function Gw from ISO 2631 [17] that model. However, if the output cannot be written as linear define the sensitivity of human body being exposed to combination of its input, the system is said to be non-linear. vibrations with different frequencies illustrated in (1) & (2). For example, studies by Hyun-Chul et al [22] and Basari [23] Here, zw and zr are wheel and road displacement respectively. utilized non-linear models of quarter car with MacPherson strut suspension to ensure the study will be as close as possible to the real case. Also, most variable dampers and actuators in 2 T controllable suspensions inherit the nonlinearity properties. Jstab = zw t -zr t dt (1) 0 For example, the behavior of continuous variable damper is governed by valve openings and variable viscosity smart fluids T 2 and their operating processes inherit nonlinearity aspects into JcomfISO 0 Gw t- * zb t dt (2) dampers properties. Linear interpolation of these processes Different quantification method was used by Canale et al will only valid under certain conditions and under extreme [18]. Here, RMS value for body acceleration was normalized velocity, unpredictable results will be observed [24]. Based on with gravitational acceleration, g to characterize comfort, chaos theory, a little change in the input of a nonlinear system b, RMS may cause unpredictable and complex effects to its output, Zrel which is shown in (3). Road holding performance increasing the complexity of the controller needed to govern FwRMS was characterized by RMS values of forces exchanged the system itself [25]. Due to this, control strategies governing between road and wheel, , normalized with static forces linear and nonlinear systems should be considered separately. acting on the wheel, F stat w,* for both rear and front part of A. Control Strategies for Linear System vehicle. The equation for RMS quantification can be seen in (4) where Fw,* = kw,* zw,* -zr,* + cw,* zw,* -zr,* and 1) Skyhook and Other Switching Algorithm Approach F stat w, * = g m b,* + m w,* . Note that * can be replaced by each Mainly due to its simple algorithm, this is undoubtedly one rear and front subscript respectively. The study also integrates of the most popular semi-active control strategy used. The an approach to quantify pitch, and bounce, Z b,max concept of skyhook control has been originated in 1974 by performance in their work as shown in (5). Karnopp et al. [15], based on a fictitious controllable damper placed between the vehicle body and a stationary sky. In 2 practical, the concept can be modeled by two main control b, RMS 1 T Zb (t) Zrel = dt (3) algorithms; two-state (on/off) control strategy, studied in [26- 0 g 28] and continuous variable damping skyhook control, studied in [22, 29]. In two-state skyhook control, the variable damping 2 coefficient, cvar(t) will only have a maximum and minimum 1 T Fw,* t Fw,RMS *= stat dt (4) damping state, which is cmax and cmin respectively. In linear 0 Fw,* skyhook control, a continuous variable damper is employed. This enables any values to be assigned for cvar(t) ranging RMS 1 T 2 b,max = (t) dt and Z = max (5) between cmax and cmin. The control algorithm determines the 0 suitable values based on the relative motion between the For validation purposes, all the quantified characteristics unsprung mass and the sprung mass. If the relative velocity were compared to a justified result. A study by Canale et al. and body absolute velocity is in the same direction, a positive [18] opted for comparison with validated results from previous value is given for its product, thus maximum damping is published works. In this case, the results from skyhook and generated. Conversely, if the vehicles body absolute velocity optimal (Linear Quadratic) LQ-clipped control were used to and the relative motion are not in the same direction, validate the proposed strategy. However, most of studies used minimum damping force will be generated. Figure 2 illustrates passive suspension performance as benchmark to be improved the skyhook control. The algorithm for both two-state (on/off) [8, 19-21]. and continuous variable damping strategy can be summarized 405

3 in (6a) & (6b) respectively [29]. Here, is a Savaresi et al. [29] investigated the use of body , acceleration instead of body velocity for its switching saturation operator and typical values for is 0.5 as suggested algorithm that resembles skyhook control which proven to by Savaresi et al. [29]. give remarkable performance when compared to passive suspension. Another interesting switching algorithm, MiniMax control were studied by author in [30]. The algorithm will switch damping state between hard or soft based on current shock absorber state (compressed or stretched) and also wheel loading condition (whether there are increase or decrease in wheel loading). This controller was compared with skyhook controller and showed that MiniMax control improved road holding characteristics which skyhook control lacks. 2) Optimal Control Studies and applications of optimal control have been recorded since early 1970s. Hrovat et al. [4] has reviewed optimal control applications on various setups of controllable suspension. Extensive studies on optimal control applied in semi active, active and also slow active suspension can be seen in [34, 35]. The most common optimal control methods Figure 2: Concept of Skyhook damping are the Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and Model Predictive Optimal Control if x1 x1 -x2 0 cvar =cmax (MPC). Different performance index were introduced with (6a) different kind of optimization algorithm to solve the optimal if x1 x1 -x2

4 to a normal MPC strategy and shown significant improvement lookup table/data and rules which designed based on human in ride performance compared to a skyhook approach. Study in experienced dealing with different conditions. The [41] (and the references within) however, used preview functionality of FLC was explained with great detail by Chen information of incoming road instead of using a vehicle et al. [20] using a 2-input-1-output rule base. The input suspension model. The semi-active suspension was applied at considered is error of body acceleration, E and the change in rear wheel of the vehicle. Based on road measurement error, EC to dictate the output control force. In [45], a lookup recorded from front wheels, the road excitation is modeled as table was designed using the suspension deflections and a delayed signal so that the rear suspension can provide sprung mass velocity to provide the control forces needed suitable reaction to isolate vehicle body from this excitation. under current situation. Danesin et al [46] demonstrate the use Improvement in body acceleration was observed but always of FLC with Real Time Damper subsytem in with an increase in tire load. Additionally, from the testing on MATLAB/SIMULINK simulations. rounded pulse road input, the performance cannot be improved with this approach. Consideration for using this approach in an IV. COMPUTATIONAL SIMULATION IN CONTROLLABLE active controller also been detailed in this study. SUSPENSION RESEARCH Advancement in computational technologies has B. Control Strategies for Nonlinear System accelerated the research effort to explore the possibility of Vehicle system suspension behavior always assumed to be semi-active and active suspension [4]. Several approaches linear to ease the process of coming out with a mathematical have been explored for the past few years. To study the model to predict the state and control its processes [4, 5]. The effectiveness of any control algorithm, often than not a studies of suspension control behavior should use the computational simulation was used. In simulating vehicle nonlinear representation to ensure its effectiveness and suspension system, the process flow can be simplified as robustness under various conditions (vibration frequencies, shown in Figure 3. The most common control simulation tool velocities, etc.). Basari [23] include nonlinear MacPherson is undoubtedly MATLAB/SIMULINK from MathWorks Inc. suspension quarter car model and controlled it with a robust The software provides a very robust platform to model and controller using back-stepping technique. In [42], they used simulate any defined system with the aid of customizable sets feedback linearization to linearize force/velocity response of of blocks available from its library. For a full simulation in MR damper which enables them to use a linear approach, MATLAB/SIMULINK, all the stages will be defined as sets of skyhook control. This was done by employing feedback gain blocks containing mathematical equations and commands and feed-forward gain in the feedback controller. detailing the behavior of each process. Studies involving One of the controller model used is adaptive controller. MATLAB can be seen in [8, 9, 30, 32, 43]. However, as Adaptive control uses the vehicle state to adapt with varying discussed in Section III-B, nonlinearity of a system imposes condition the vehicle undergoes. Common approach used is great challenge for modeling and controlling processes. One feed-forward and feedback where the vehicle state is fed into way to incorporate nonlinear aspects of the system without the the controller to calculate the suitable response. In 2008, need of complex algorithm and mathematic model is to use an Jiangtao et al. [5] reviewed on intelligent methods for adaptive actual component in the simulation loop [22]. To do this, the control. They examined fuzzy logic, neural network and concept of software-in-the-loop (SIL) and hardware-in-the- genetic algorithm to be employed in adaptive control. These loop (HIL) were introduced. This section will review the approaches were applied into the adaptive controller to concept of SIL and (HIL) simulation which uses contribute brain and experience to calculate better responses. MATLAB/SIMULINK as the control platform. Applications of Linear Parameter-Varying (LPV), LPV/H controller were demonstrated by [21, 24, 43] with the additional of anti-roll distribution control by feedback- feedforward approach in [43]. To apply this approach, nonlinear vehicle model was transformed and reformulated into a LPV system that involve gain scheduling parameters which will be suitable with H framework controller. The transformation makes this type of controller to be a robust controller suitable for ever-varying suspension system. In [9], an interesting study involving H controller with full vehicle model were carried out. Different controller setup (quarter, half and full H controller) were applied on the full vehicle model to observe the effectiveness of each setup. They concluded that a simple quarter car H controller setup provides the best result in ride and comfort performance compared to half and full car H controller setups. For the past few years, the implementation of fuzzy logic controller (FLC) can be observed in several studies [20, 44- Figure 3: Simulation process flow for controllable suspension study 46]. FLC controller determines suitable response based on a 407

5 A. Software-in-the-loop (SIL) Simulation Most of the studies were testing the damper component For SIL simulation, different CAE software will be used to physically while the vehicle model and control algorithm were model any of the components in the simulation loop. This modeled in MATLAB/SIMULINK Real Time Workshop. The software is aimed to perform specific task in its own specific C-code generated from this model was downloaded into a environment. For example, a Multi-Body Dynamic Software target PC which will perform the real time simulation with the (MBS) can be used for geometric modeling of a nonlinear physical damper in the loop. Communication between the vehicle behavior, or electromagnetic CAE software may be target pc and tested hardware (damper) is via data acquisition used to model an electromagnetic shock absorber used in an card. Digital-to-analog and analog-to-digital converter was active suspension configuration. The software will be used to convert signal being exchanged between the real-time changing input and output parameters with simulation and damper. Implementation of HIL can greatly MATLAB/SIMULINK which acts as control platform for the shorten the research period and once set-up; it can be repeated simulation. The specific result (e.g. loading distribution and with different control algorithms. However, great knowledge heat generated within the model) of considered model (e.g about instrumentation is needed in realization of the approach. vehicle, damper or actuator) can be monitored separately V. CONCLUSION AND PROSPECTS which will make design process easier. For the past 10 years, several studies have demonstrated the application of SIL on Various control methodologies for controllable suspension both active and semi-active suspension configuration which were reviewed with different performance criteria. utilized commercial dynamic MBS, MSC ADAMS [7, 30, 47- Nonlinearity problem in modeling and control applications has 49] and MSC Visual NASTRAN [32]. To implement this been addressed and one of the practical solutions is to replace method, first the vehicle model and its respective input/output the nonlinear component with dedicated modeling software variables were defined in MBS software. Next, the model can (SIL) or physical hardware (HIL). It has been interestingly be exported as a subsystem in MATLAB/SIMULINK where observed by the authors that most of the studies reviewed were the controller can be constructed based on instructions using variable damper or smart fluids (e.g. MR damper). regarding input/output variables to be exchanged with the Lately, several studies has proposed an electromagnetic MBS. The variable exchange can be simplified in Figure 4. suspension (EMS) system that can provide both variable MSC ADAMS/CAR also provides excellent templates of damping and force in a controllable suspension with energy common suspension configuration, e.g MacPherson strut regenerative feature [12]. So far, very few studies were which will integrate the non-linear behavior to be simulated. conducted on its control strategies. Implementing the SIL and HIL simulations for the EMS system should be an interesting research prospect. 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