This paper investigates the application of fuzzy model and generalised predictive control strategy for the construction of a model-based predictive fuzzy controller for non-linear processes. In this method a GPC controller is used with the Takagi Sugeno Kang (TSK) piece-wise fuzzy modeling approach in an adaptive strategy. The developed method is applied to control a continuous stirred tank rea...

In this paper, a new model predictive PID controller design method for the slip suppression control of EVs (electric vehicles) is proposed. The proposed method aims to improve the maneuverability and the stability of EVs by controlling the wheel slip ratio. The optimal control gains of PID framework are derived by the model predictive control (MPC) algorithm. There also include numerical simula...

This work proposes a predictive controller with interpolation in order to improve the behaviour of a typical MPC when the system presents constraints. Particularly, it is interesting to see how the interpolation, between the solution of the optimal unconstrained problem and other feasible solutions, assures the stability of the system in presence of disturbances. The system in which the control...

In the paper an application of the model predictive controller for the drive system with elasticity and backlash is presented. In the introduction the control problem of the drive system with mechanical backlash is introduced. Next, the mathematical model of the drive is presented. Then the predictive algorithm is described briefly. The performance of the predictive controller is examined in th...

In this paper a neural adaptive control method has been developed and applied to robot control. Simulation results are presented to verify the effectiveness of the controller. These results show that the performance by using this controller is better than those which just use either direct inverse control or predictive control. In addition, they show that the resulting is a useful method which ...

This paper presents a new dual mode nonlinear model predictive controller (NMPC) that is based on the combination of the finite horizon NMPC with the infinite horizon predictive controller (IHMPC). The resulting nonlinear controller is shown to be stable when the IHMPC is globally stabilizing. The main advantage of the proposed controller in comparison to the IHMPC is a better performance as th...

In this paper a Neural Predictive Controller (NPC) designed to control a broad class of process systems. Neural network identification yields nonlinear global model of the unknown system. LevenbergMarquardt (L-M) optimization method is used to find optimal control signal to minimize future errors of the objective function of predictive controller. Inequality constraints of actuators are added t...

A nonlinear predictive controller is presented in the paper. The greatest advantage of predictive functional control (PFC) is very fast and easy calculation of control variable. Combined with the Takagi-Sugeno (TS) fuzzy model, the PFC becomes nonlinear and is called fuzzy PFC (FPFC). The controller was evaluated by simulation on the well-known process, the continuous stirred-tank reactor (CSTR...

In this paper a combined controller is proposed for nonlinear dynamical systems. The controller is constructed by a fuzzy wavelet network and nonlinear model predictive control. Chaotic optimization, which is fast and robust, is applied to generate optimized controlled input in nonlinear model predictive control. The ability of the fuzzy wavelet neural network and the proposed controller is sho...

Bioprocesses have the inherent characteristics of high nonlinearity, parameter uncertainty and information imperfection. These specific natures may cause some difficulties in control. Jacobian linearization method was used in this paper to linearize the bioreactor model firstly, and then a Multipleinput and single output (MISO) model predictive controller was designed for the reactor. PID contr...