A MIMO nonlinear adaptive predictive control strategy is presented in which the wavelet neural network based on a set of orthogonal wavelet functions is adopted. A nonlinear mapping from the network-input space to the wavelons output space in the hidden layer is performed firstly. Then, the output layer uses a linear structure. Its weight coefficients can be estimated by a linear least-squares ...

Motivated by the need to develop more effective methods of controlling uncertain non-linear systems, this paper focus on developing an adaptive fuzzy model-based controller, in which the optimisation variables remain in the fuzzy domain. The scheme uses an on-line fuzzy identification scheme, which is able to generate a fuzzy relational model using the training data from the system. The propose...

An adaptive model-based predictive control scheme is proposed for non-linear systems. This methodology exploits the non-linear modelling capabilities of nonlinear state-space neural networks and the online weights adjustment by means of an unscented Kalman filter. Results from experiments show evidences on its good tracking performance even when the system’s dynamics change.

To combine the design strategies of both preventive control and reactive control, a self-adaptive predictive congestion control model for extreme networks is proposed. First, a model is employed to predict the data traffic. Then the reference trajectory of the traffic is updated, and thereafter transmission flow is adjusted according to the prediction results by optimization of a performance in...

In situ adaptive tabulation (ISAT) is applied to store and retrieve solutions of nonlinear model predictive control (NMPC) problems. ISAT controls approximation error by adaptively building the database of NMPC solutions with piecewise linear local approximations. Unlike initial state or constraint parameterized constrained quadratic programming (QP) solutions, ISAT approximates NMPC solutions ...

A long-range predictive control algorithm for nonlinear processes operating over a wide range is proposed and is based on the Takagi–Sugeno–Kang (TSK) piece-wise linear fuzzy modelling approach. The performance of the fuzzy modelling approach integrated with the proposed control algorithm to form an adaptive control scheme is examined using a series of experiments on a liquid level system and o...

This paper is concerned with the design of Direct Adaptive Model Based Predictive Control (DAMBPC) using subspace identification technique to identify and implement the controller parameters. The direct identification of controller parameters reduces the design effort and computation load which is usually involved with classical adaptive control techniques. The proposed method requires a single...

In this paper, we present a novel solution for realtime, Non-Linear Model Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The proposed controller formulates the Optimal Control Problem (OCP) in terms of flat outputs over an adaptive lattice. In common approximated OCP solutions, the number of discretization points composing the lattice represents a critical upper bound for...