To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. If you do not want to generate files in this directory, change the working directory to a suitable directory: Figure 2: Wheel Speed subsystem To control the rate of change of brake pressure, the model subtracts actual slip from the desired slip and feeds this signal into a bang-bang control +1 or -1, depending on the sign of the error, see Figure 2. Logged signals have a blue indicator. This plot shows that the wheel speed stays below vehicle speed without locking up, with vehicle speed going to zero in less than 15 seconds. However, the control of antilock braking systems is a challenging problem due to nonlinear braking dynamics and the uncertain and time-varying nature of the parameters. Simulations are conducted to show the effectiveness of the proposed controller under various road conditions and parameter uncertainties.
This model uses the signal logging feature in Simulink®. In general, Antilock braking systems have been developed to reduce tendency for wheel lock and improve vehicle control during sudden braking especially on slippery road surfaces. There are two main tasks of this dissertation. Computer simulations are performed to verify the proposed control scheme. At the end, the performance of the proposed controller is compared with that of a sliding mode controller, reported in the literature, through simulations of braking on dry and slippery roads. The simulation results indicate that, the wheel slip tracking error is remarkably decreased by the proposed controller.
From these expressions, we see that slip is zero when wheel speed and vehicle speed are equal, and slip equals one when the wheel is locked. The results presented indicate the potential of the approach in handling difficult real-time control problems. Read more about Signal Logging in Simulink Help. We set the desired slip to the value of slip at which the mu-slip curve reaches a peak value, this being the optimum value for minimum braking distance see note below. The simulation is carried on under the low tire-road friction coefficient. This work is an attempt to contribute in this field. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity.
The real engineering value of a simulation like this is to show the potential of the control concept prior to addressing the specific issues of implementation. The control algorithm is derived and subsequently tested on a quarter vehicle model. Addition of automatic tuning-tool can track changes in system operation and compensate for drift, due to aging and parameter uncertainties. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. It is used in this example to illustrate the conceptual construction of such a simulation model.
Dividing the net torque by the wheel rotational inertia, I, yields the wheel acceleration, which is then integrated to provide wheel velocity. Creating a Temporary Directory for the Example During this example, Simulink generates files in the current working directory. . The braking, from that point on, is applied in a less-than-optimal part of the slip curve. This significantly reduces the time needed to prove new ideas by enabling actual testing early in the development cycle. Firstly, to compare the practical results with numerically obtained results.
This paper, therefore proposes a non-linear control design using input-output feedback linearization approach. This is, perhaps, more meaningful in terms of the comparison shown in Figure 5. We used separate integrators to compute wheel angular speed and vehicle speed. Ratio of energy recycling can achieve 16. The model multiplies the friction coefficient, mu, by the weight on the wheel, W, to yield the frictional force, Ff, acting on the circumference of the tire. The model represents a single wheel, which may be replicated a number of times to create a model for a multi-wheel vehicle. This disconnects the slip feedback from the controller see Figure 1 , resulting in maximum braking.
The control objective is to minimize the stopping distance, while keeping the slip ratio of the tires within desired range. The results from the proposed method exhibited a more superior performance and reduced the chattering effect on the braking torque compared to the performance of the standard feedback linearization method. This maximizes the adhesion between the tire and road and minimizes the stopping distance with the available friction. The first plot in Figure 3 shows the wheel angular velocity and corresponding vehicle angular velocity. This paper presents an adaptive neural network-based hybrid controller for antilock braking systems.
Moreover, the operation of the control does not require identification of the traction coefficient between the tire and the road surface. Ff is divided by the vehicle mass to produce the vehicle deceleration, which the model integrates to obtain vehicle velocity. In this scenario, the real-time model would send the wheel speed to the controller, and the controller would send brake action to the model. The resulting signal, multiplied by the piston area and radius with respect to the wheel Kf , is the brake torque applied to the wheel. Secondly, computationally analyse the aerodynamics of a multi-element i. Modeling The friction coefficient between the tire and the road surface, mu, is an empirical function of slip, known as the mu-slip curve.