A customized version of the IVCS system to provide a coordinated control over the ESP and EPAS smart actuators to meet the system specifications is proposed in this section. The system consists of 6 layers similar to general IVCS architecture, however, layer 1 and 2 are placed in supervisory control block, layers 3 and 4 are put in high-level control block, and layers 5 is sited in
low-level control block and layer 6 is in the smart actuator control block, as
Figure 2-4: Structure of IVCS system for planar motion control
More detailed descriptions of the layers are as follows:
Layer 1. By assumption of having the control authority exclusively on the steering (torque) and brake (slip), the control task are limited to vehicles’ ‘yaw’, ‘lateral’ and ‘longitudinal’ motions, so called vehicle planar motions. We don’t have any direct control over the vehicle’s roll, pitch and bounce because of the actuator limitation. The lateral vehicle dynamics system can be constructed based on vehicle’s yaw velocity (so-called yaw rate) control as well as vehicle sideslip (ratio of lateral velocity to longitudinal velocity) control (Gillespie , 1992; Rajamani, 2012), so the high-level control system states vector is ܠ ൌ [ܸ௫ ܸ௬ ߱௭]். The model reference approach is adopted to derive the reference values, in which, the steady state behaviour of a two DoF vehicle model (so called bicycle model) is used to drive the reference values (Rajamani, 2012; van Zanten, Erhardt, Pfaff, Kost, Hartmann, & Ehret, 1996). This layer is shown as the “Reference
Model” block in Figure 2-4.
Layer 2. The second layer consists of three modules, so called the “State
blocks (see also Figure 2-2). In the absence of some real sensor measurement (due to practical limitation or for cost reduction), we have to use several robust estimation methods to ‘virtually’ measure the required vehicle parameters. More specifically, there is a need for estimation of tyre self-aligning moment (Hsu Y. , 2009), vehicle sideslip (van Zanten A. , 2000) and tyre-road coefficient of friction (Ahn, Peng, & Tseng, 2012). Estimation algorithms are employed in the State Estimator block.
In the State Monitor block, the existing states of the vehicle are compared with reference values to identify three different driving conditions: normal driving, mild and hazardous stability conditions. In stability conditions, the over-steering/Understeering situation is also determined. Normal driving conditions (on dry road, with coefficient of friction ߤ = 1) stands for the lateral acceleration range from zero up to 0.4݃, which corresponds to tyre’s linear region (Smakman, 2000). The lateral accelerations from 0.4݃ up to 0.6݃ is the tyre non-linear working range which is here featured as mild stability condition. Higher lateral accelerations up to maximum saturation limit (i.e ܽ௬ = ݃) is characterised as hazardous stability condition (Milliken & Milliken, 1995) .
In normal driving conditions, the control task is to provide the driver comfort while in mild and hazardous conditions the vehicle stability is the priority. In the comfort mode, the driver steering (torque) trigger the EPAS assist block to generate the relevant assist torque for the sake of driver comfort (Zaremba, Liubakka, & Stuntz, 1998; Post, 1995), whereas, the driver’s command on braking goes directly to the slip control system. When the vehicle tends to move to an unstable region (limited stability) (Takahashi, 2004), the control system switches to mild stability mode, in which the IVCS system will try to stabilise the vehicle by reducing the magnitude of the assist torque and even more by producing a counter steering torque to the steering wheel (Liu, et al, 2008; McCann, 2000; Tanaka, et al, 2007). If the
amount of driver’s steering correction will not be sufficient to stabilise the vehicle, the control mode switch to hazardous stability mode, which represents autonomous brake intervention (Chang, 2007; Ono, et al, 2006). In hazardous mode, the brake control plays the major role because of the steering limitation. The control mode switching will provide by means of a (bumpless) rule-based approach (Asarin, et al, 2000).
Layer 3. Based on Newton’s second law, the derivative of vehicle planar motions,ܠሶ= ൣܸ௫̇ ܸ௬̇ ܸ௭̇ ൧், are proportional to the planar generalised forces and moments, i.e. longitudinal and lateral forces and yaw moment, so the generalised forces and moment vector is
ૌ= [ܨ௫ ܨ௬ ܯ௭]் (2-7)
The high-level controllers consist of three closed loop control laws on vehicle’s states which will be activated in case of mild or hazardous stability conditions. The output of the controllers are the values of the vector ૌ which will stabilise the vehicle if they are applied at the vehicle‘s centre of gravity (ignoring actuators dynamics). There are several (SISO or MIMO) control laws that have been proposed for the vehicle yaw rate and sideslip control which range from PID controllers (Shibahata, Progress and future direction of Chassis control technology, 2005) to more advanced controllers like sliding mode control (Furukawa & Abe, 1997; Rajamani, 2012), fuzzy logic control (Chen, Dao, & Lin, 2010) or H-infinity control (Hirano, Harada, Ono, & Takanami, 1993; Horiuchi, Okada, & Nohtomi, 1999). In this dissertation a novel high-level control law is developed by employing the Youla parameterisation control design approach. (Youla, Jabr , & Bongiorno Jr, 1976)
Layer 4. Considering the steering and brakes as the only available actuators in the vehicle, the low-level control authorises are available only on the front tyres lateral forces, ∆ܨ௬,, ݅= 1,2 (through front steering intervention) and the four tyres longitudinal forces, ∆ܨ௫,, ݅= 1,2,3,4
(through 4 wheels brake intervention) where ݅= 1,2,3,4 indices stands for front left, front right, rear left and rear right tyres respectively. The control input vector can be defined as
ܝ = [∆ܨ௫,ଵ ∆ܨ௬,ଵ ∆ܨ௫,ଶ ∆ܨ௬,ଶ ∆ܨ௫,ଷ ∆ܨ௫,ସ]் (2-8) As the number of generalised forces and moments (ૌ∈ ℝଷ) is less than the number of available actuators (ܝ ∈ ℝ), so the system is redundant (over-actuated) and based on known values of ૌ ,there is not a unique or a direct solution for vectorܝ.The optimum distribution of generalised yaw moment (on the vehicle level) to the relevant actuators forces and moments (on the tyre level) is employed by solving a constrained optimisation problem. A fast, reconfigurable and adaptive control allocation solution is proposed in this dissertation. The proposed control allocation provides several properties to the integrated control system to address the required specifications defined in section 3.2.2, such as low cost execution, fault tolerance and adaptation to vehicle and/or environment parameters changes.
Layer 5. The proposed integrated vehicle dynamics control system is based on steering (torque) and brake (pressure) intervention by means of the EPAS (steering torque control) and EHB (brake hydraulic pressure control) actuators. By assuming: the front tyres lateral forces and the four tyres longitudinal force as the low-level control states; the EPAS, EHB as the controlled plants; and the output of the control allocation block, ܝ, as the reference tracks; the (low-level) control objectives are to design a set of low-level control laws by considering the actuators dynamics such that the output of the plants follow the reference values asymptotically. One closed loop controller based on steering self-aligning moment feedback and four (similar) closed loop controller based on (each) tyre longitudinal slip feedback are designed and implemented in this layer.
Layer 6. Each smart actuator has been equipped with its means of force or moment generating system (so called, low-level effectors). More
specifically, the EPAS generates steering torque by means of an electric motor attached to the steering column (or pinion or rack) and EHB generates longitudinal tyre forces by generating (or changing) the hydraulic pressure on the brake pad through a set of hydraulic valves and an electric pump actuation (Robert Bosch GmbH, 2011). The objective of the (actuator level) control system in this layer is to control the magnitude and/or direction of the forces or moments produced by the electromechanical effectors associated with each actuator such that it follows the reference values from the previous layer asymptotically. These effectors includes DC electric motor closed loop current controller for EPAS (Hu, 2008) and a continuous hydraulic pressure control on four wheels braking for EHB systems (Van Zanten, 2002).
By considering the proposed architecture and various layers of the integrated vehicle dynamics system, as discussed above, the top building blocks of the proposed IVCS system in Simulink® environment are presented in Figure 2-5.
3 System Modelling
Based on the V-model, introduced in the previous chapter, the design phase starts with System modelling4 in which the conceptual and mathematical representations of the system dynamics are constructed. From a control design point of view, the model should be complete to ideally capture the fundamental dynamics of the system and remain simple enough to provide a basis for model based control development. If a model includes sufficient fidelity, then the control performance can be evaluated through simulation and the risk and cost associated with experimental validation will be reduced considerably. (Gerdes & Hedrick, 1999)
The modelling starts with systematic decomposition, in which the control system is considered as a hierarchical composition of several layers of sub-systems. The Simulink® blocks of the IVCS system dynamics, including vehicle dynamics, as well as steering, brake (and engine) dynamics are highlighted in Figure 3-1.
Figure 3-1: The IVCS system dynamics Simulink® blocks
The mathematical modelling of the system dynamics including the vehicle, steering and brake dynamics are presented in this chapter.
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