Covers PID control systems from the very basics to the advanced topics This book covers the
design, implementation and automatic tuning of PID control systems with operational …

The control problem was formulated in the previous chapters considering all signals to
possess an unlimited range. This is not very realistic because in practice all processes are …

We consider the problem of multi-step ahead prediction in time series analysis using the non-
parametric Gaussian process model.-step ahead forecasting of a discrete-time non-linear …

Gaussian process models provide a probabilistic non-parametric modelling approach for
black-box identification of non-linear dynamic systems. The Gaussian processes can …

This paper presents a new multi-stage genetic programming (MSGP) strategy for modeling
nonlinear systems. The proposed strategy is based on incorporating the individual effect of …

In the Proportional-Integral-Derivative (PID) controllers the control signal is a linear function
of: the current control error (the proportional part), the past errors (the integral part) and the …

This brief addresses the design of a Nonlinear Model Predictive Control (NMPC) strategy for
exponentially incremental Input-to-State Stable (ISS) systems. In particular, a novel …

Projection to latent structures (PLS) has been shown to be a robust multivariate linear
regression technique for the analysis and modelling of noisy and highly correlated data. It …

Wiener model identification and predictive control of a pH neutralisation process is
presented. Input-output data from a nonlinear, first principles simulation model of the pH …

In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction
and linearization is proposed, considering a long short-term memory (LSTM) artificial neural …