an adaptive version of growing and pruning rbf neural network has been used to predict the system output and implement linear model-based predictive controller (lmpc) and non-linear model-based predictive controller (nmpc) strategies. a radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.an unscented kalman filter (ukf) algor...

Steer by wire system can be free to design steering force transmission characteristics and angular displacement transfer characteristics, which can realize the coordination of portability, the driver steering road feel and handling stability. The dynamics models of steer by wire and vehicle system are established. Control strategy of front wheel angle based on fuzzy adaptive PID control is put ...

Managing well pressure in petroleum drilling is essential for avoiding instability. An adaptive PID control using the unfalsified procedure is proposed to regulate the pressure. The scheme chooses the right PID parameter from a set of candidate parameters based on the data measurement instead of any hypothetical model. The scheme eliminates the difficulties in tuning the PID even without any pr...

This paper introduces an optimal fuzzy proportional–integral–derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting control...

This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has highly simple and dynamic self-organizing structure, fast online-tuning speed, good generalization...

Since the PID tuning formula of Ziegler-Nichols step test, perhaps the best known, the process control literature is still showing lots of PID control schemes to improve the closed-loop stability. In this paper, using the relationship between the PID and generalized minimum variance (GMV) controller and adjusting online the control weighting factor by an adaptive learning algorithm of first-ord...

This paper analyses the current Internet congestion control mechanisms and presents two control theoretical approaches based on the existing Proportional-Integral-plus-Derivative (PID) controllers. The parameters were selected to ensure the stability of the control loop. It is shown that a modified PID controller offers a quick response, good utilisation and fair share of available bandwidth an...

Adaptive PID controller based on real base function (RBF) network identification by optimal tuning of proportional– integral–derivative (PID) controller parameter is necessary for thematic factory operation of an automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and real base function network (RBF) identification control approach to determine the op...

In this paper, a methodology for Lyapunov-based adaptive PID control for different nonlinearlyparameterized series and parallel PID realizations presented using simple first and second order dominant plants. The corresponding designs are based on using only the tracking error, its derivative, its integral, and the current value of the adaptive gains in order to update the PID gains. The convent...

PID control is used everywhere—in cellular phones, vehicles, process control, heating, ventilation, air conditioning, machine tools, and motor drives. Many PID controllers are found in cars, for example, in engine, cruise, and traction control. PID control is also embedded in instruments like atomic force microscopes and adaptive optics. Because of their widespread use, it is difficult to preci...