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[کتاب][B] PID control system design and automatic tuning using MATLAB/Simulink
L Wang - 2020 - books.google.com
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 …
design, implementation and automatic tuning of PID control systems with operational …
[کتاب][B] Constrained model predictive control
EF Camacho, C Bordons, EF Camacho, C Bordons - 2007 - Springer
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 …
possess an unlimited range. This is not very realistic because in practice all processes are …
Gaussian process priors with uncertain inputs application to multiple-step ahead time series forecasting
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 …
parametric Gaussian process model.-step ahead forecasting of a discrete-time non-linear …
Gaussian process model based predictive control
Gaussian process models provide a probabilistic non-parametric modelling approach for
black-box identification of non-linear dynamic systems. The Gaussian processes can …
black-box identification of non-linear dynamic systems. The Gaussian processes can …
Multi-stage genetic programming: a new strategy to nonlinear system modeling
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 …
nonlinear systems. The proposed strategy is based on incorporating the individual effect of …
Computationally efficient model predictive control algorithms
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 …
of: the current control error (the proportional part), the past errors (the integral part) and the …
Nonlinear MPC design for incrementally ISS systems with application to GRU networks
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 …
exponentially incremental Input-to-State Stable (ISS) systems. In particular, a novel …
Non-linear projection to latent structures revisited: the quadratic PLS algorithm
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 …
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
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 …
presented. Input-output data from a nonlinear, first principles simulation model of the pH …
Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models
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 …
and linearization is proposed, considering a long short-term memory (LSTM) artificial neural …