Neural Network Based Sliding Mode Control of Electronic Throttle

Neural network based tire/road friction force estimation

“…Although we prove convergence of the state estimates for neural networks with fixed hidden layer parameters, it can also be proven for cases when hidden network parameters are trained on-line. For example, using Lyapunov function as in Baric´et al (2005) instead of (32), the convergence can be proven for multi-layer perceptron networks with on-line training of all its parameters. However, one has to be aware of increased computational complexity of the training algorithm.…”

Neural networks based self-learning PID control of electronic throttle

“…. , M) are the networks connecting weights, a m j (k) is the center of the Gaussian potential function, and b m j (k) is the width of the Gaussian potential function, at time k.ŷ(k) in (9) is the approximation of y(k) in (6), and it constructs the plant model. GPFN2 acts as the self-learning PID controller, and the output of GPFN2 is…”

“…Considering everything mentioned before, it is not a surprise that this challenging control problem has attracted significant attention of the research community in the last decade [3][4][5][6][7][8][9]. There are several ETC control strategies that differ in the underlying philosophy, complexity, and the number of sensor signals needed to determine the desired throttle opening.…”

“…Several of the existing control strategies use a linear model of the plant and derive a proportional-integral-derivative (PID) controller with feedback compensator [3]. Variable structure control with sliding mode is an effective method for the control of nonlinear plant with parameter uncertainty, and it has also been applied in the ETC system in [4,5], and [6]. In [4], a discretetime sliding mode controller and observer are designed to realize ETC system by replacing signum functions with continuous approximations.…”

“…A sliding model controller coupled with a genetic algorithm (GA) based variable structure system observer fulfills the demand for high robustness in nonlinear opening of the throttle in [5]. In [6], a neural networks based sliding mode controller is described since neural networks has good learning ability and nonlinear approximation capability. In [7], variable structure control technique based robust control is presented.…”