Abstract
In this chapter, the authors used a comparative study between passive and active suspension of quarter car models with deference intelligent controllers. This study aims to obtain an active suspension that adapts to all types of roads, especially rough and slippery ones, and absorbs shocks resulting from road vibrations, which gives more comfort to passengers and the driver. The results have proven that FOPID gave better results than PID in all types of road testing. The concerns related to the proposed chapter are that the car makers have a fear of the Fractional-Order controller FOPID to the difficulty of achieving it in the industrial field because of the difficulty of its mathematical equations and its high cost.
TopIntroduction
The suspension of cars is discussed in this chapter connection the cars motion and ride feature of vehicles. The authors were used a two degree of freedom (2DOF) of quarter car model because is simple but sufficiently detailed to capture many of the key suspension performance tradeoffs. Such as ride quality (represented by suspension deflection), and packaging (represent by suspension stroke), also known as the rattle space (Ulsoy, Deng, & Calanakci, 2012). The active suspension system is a type of suspension on a car. In general, Suspension is a system for controlling the vertical motion of a vehicle's wheels for the car's body structure rather than passive suspension provided by springs, and dampers, where the motion is determined by the road surface. Active suspension is divided into two classes: real active suspension and semi-adaptive suspension only varies shock absorber finesse to match changing road or dynamic conditions active suspension uses some type of actuator to raise and lower the chassis independently (Saintilan & Shelley, 2013), and (Applegard & Wellstead, 1995).
These technologies allow car manufactures to achieve a greater degree of ride quality and car handling by keeping the tires perpendicular to the road allow a batter traction for the car and control it (Lin & Kanellakopoulos, 1997; Tseng & Hrovat, 2015; Huang, 2015).
Figure 1. Demonstration of the active suspension system of a car
The vehicle contacts the ground through the spring and damper in the suspension system, as in Figure 2. To achieve the same level of stability as the skyhook theory, the vehicle must contact the ground-contacting the spring, and the imaginary line with the damper. Theoretically, if the damping coefficient reaches an infinite value, the vehicle will not vibrate (Sun, Zhao, & Li, 2014).
Figure 2. Quarter car suspension model
Active suspension is the first to be offered, and it uses separate actuators that can exert independent force on the suspension to improve characteristics and performance of car. The defects of this design are high cost, added complication and mass of the apparatus, and the need for frequent maintenance on some implementation. Maintenance can require specialized tools, and some problems can be difficult to diagnose (Dan & Sun, 2018) and (Mohammadi & Ganjefar, 2017). In this chapiter, the authors study the model of an active suspension of quarter car, and applied two types of controllers:
For adjust these controllers the researches used Artificial neural Network and optimization methods.
TopBackground
Suspension design for automobiles has traditionally been a compromise between the three opposing criteria of road handling load-bearing and occupant comfort. The suspension system must support the vehicle, provide directional control during maneuvering and provide effective occupant/cargo isolation from road disturbance. Good ride comfort requires a soft suspension, while sensitivity to applied loads requires a hard suspension (Venhovens, Knaap, & der, 1995). Good handling requires a suspension setup somewhere in between the two, because of these conflicting demands, suspension design must be a compromise, determined largely by the type one must independently determine for load-carrying characteristics, handling, and ride quality (Kashem, Nagarajah, & Eklesabi, 2018).
Key Terms in this Chapter
Artificial Intelligence (AI): Is machine intelligence, as opposed to the intelligence exhibited by non-human creatures and humans, the ability to perceive, synthesize, and infer information. Speech recognition, computer vision, interlanguage translation, and various mappings of inputs are a few examples of activities where this is done.
Road Handling: Is connected to the forces that the tires exert on the road.
Suspension Travel: Is used to describe the difference in relative displacement between the sprung and unsprung masses.
Two Degrees of Freedom: The term “two-degree-of-freedom systems” refers to some dynamic systems that need two independent coordinates, or degrees of freedom, to characterize their motion. The directions of the degrees of freedom may or may not line up.
Artificial Neural Networks (ANN): Computing systems inspired by the biological neural networks that make up animal brains are commonly referred to as neural networks (NNs) or neural nets. Artificial neurons, which are a set of interconnected units or nodes that loosely resemble the neurons in a biological brain, are the foundation of an ANN. Like the synapses in a human brain, each link has the ability to send a signal to neighboring neurons. An artificial neuron can signal neurons that are connected to it after processing signals that are sent to it. The output of each neuron is calculated by some non-linear function of the sum of its inputs, and the “signal” at a connection is a real number. Edges refer to the connections. The weight of neurons and edges often changes as learning progresses.
Ride Comfort: Is directly connected to the acceleration that road-riding passengers experience.
Machine Learning (ML): The machine learning field is a branch of artificial intelligence (AI) that focuses on using data and algorithms to mimic how people learn, gradually increasing the accuracy of its predictions.
Body Motion: They are largely produced by cornering and braking motions and are known as bounce, pitch, and roll of the sprung mass.