Stability issues for First Order Predictive Functional Controllers: Extension to Handle Higher Order Internal Models

Predictive Functional Control (PFC), belonging to the family of predictive control techniques, has been demonstrated as a powerful algorithm for controlling process plants. The first order input/output PFC formulation has been a particularly attractive paradigm for industrial processes, with a combination of simplicity and effectiveness. Though its use of a lag plus delay ARX/ARMAX model is justified in many applications, it may lead to chattering and/or instability. In this paper, instability of first order PFC is addressed,and solutions to handle higher order and difficult systems are proposed

Application of Generalised Predictive Control to a Milk Pasteurisation Process

In this paper, Generalised Predictive Control (GPC) is applied to a milk pasteurisation process, in order to improve pasteurisation temperature control. The controller robustness to plant/model mismatch is investigated, as the process is simulated by a nonlinear Artificial Neural Network (ANN) based model, while the embedded internal model is given by a linear First Principles (FP) model. The GPC controller is furthermore compared to an optimally tuned PID controller

Modeling renewable energy production and CO2 emissions in the region of Adrar in Algeria using LSTM neural networks

This paper addresses the slow-onset crisis of global warming caused by CO2 emissions. Although electrical load is a major influence in a country’s growth and development, it is also one of largest sources of greenhouse gases (GHG), CO2 in particular. Therefore, switching to cleaner energy sources is a clear objective and forecasting electricity load and its environmental cost is a necessary task for electrical energy planning and management. This paper addresses short-term load forecasting of renewable energy (RE) production in the region of Adrar in Algeria with Adrar’s photovoltaic (PV) farm and Kabertene’s wind farm. The forecast is compared to the overall load demand, and the reduced amount of CO2 resulting from using renewable energy instead of fossil fuels is calculated. The forecasting models are Long short-term memory (LSTM) neural networks, which were trained and validated using real data provided by the national state-owned company SONALGAZ. The results show good performance for the forecasting models with PV and wind models achieving a Mean-absolute-error (MAE) of 0.024 and 0.1 respectively, and that RE can help reduce CO2 emissions by up to 25% per hour

Stability issues for First Order Predictive Functional Controllers: Extension to Handle Higher Order Internal Models

Predictive Functional Control (PFC), belonging to the family of predictive control techniques, has been demonstrated as a powerful algorithm for controlling process plants. The first order input/output PFC formulation has been a particularly attractive paradigm for industrial processes, with a combination of simplicity and effectiveness. Though its use of a lag plus delay ARX/ARMAX model is justified in many applications, it may lead to chattering and/or instability. In this paper, instability of first order PFC is addressed,and solutions to handle higher order and difficult systems are proposed

Application of Generalised Predictive Control to a Milk Pasteurisation Process

In this paper, Generalised Predictive Control (GPC) is applied to a milk pasteurisation process, in order to improve pasteurisation temperature control. The controller robustness to plant/model mismatch is investigated, as the process is simulated by a nonlinear Artificial Neural Network (ANN) based model, while the embedded internal model is given by a linear First Principles (FP) model. The GPC controller is furthermore compared to an optimally tuned PID controller

First Principles Modelling of a Pasteurisation Plant for Model Predictive Control

This paper investigates the physical modelling of an industrial pasteuriser plant from a control-oriented point of view. The investigated pasteuriser is based on plate heat exchangers (PHE) of type Clip 10-RM and brazed heat exchangers (BHE) of type CB76 from Alfa Laval. The traditionally highly complex and over parameterised models established for design, dimensioning, and scientific purposes are not suitable for use within a control strategy in their present form. In this paper, a simplified first order first principles (FP) model of the PHE sections as well as the BHE heaters constructing the pasteuriser are developed. An integrated model of the entire pasteuriser is then constructed combining the first principles model obtained for different pasteuriser components. The pasteuriser model parameters are identified, and the model is validated using data obtained during five test protocol sessions performed on an industrial process. Finally an example of a model predictive control (MPC) strategy, using the developed model, is briefly introduced, results are shown and conclusions are drawn

Application of Generalised Predictive Control to a Milk Pasteurisation Process

In this paper, Generalised Predictive Control (GPC) is applied to a milk pasteurisation process, in order to improve pasteurisation temperature control. The controller robustness to plant/model mismatch is investigated, as the process is simulated by a nonlinear Artificial Neural Network (ANN) based model, while the embedded internal model is given by a linear First Principles (FP) model. The GPC controller is furthermore compared to an optimally tuned PID controller

Stability issues for First Order Predictive Functional Controllers: Extension to Handle Higher Order Internal Models

Predictive Functional Control (PFC), belonging to the family of predictive control techniques, has been demonstrated as a powerful algorithm for controlling process plants. The first order input/output PFC formulation has been a particularly attractive paradigm for industrial processes, with a combination of simplicity and effectiveness. Though its use of a lag plus delay ARX/ARMAX model is justified in many applications, it may lead to chattering and/or instability. In this paper, instability of first order PFC is addressed,and solutions to handle higher order and difficult systems are proposed

Linear and Nonlinear Model Predictive Control design for a Milk Pasteurisation Plant

This article investigates the design of linear and nonlinear model predictive controllers (MPCs) in order to improve the control of pasteurization temperature in a milk plant. MPC schemes required the development of a prediction model for use internally within the controller. An artificial neural network (ANN) model of the plant is established and validated. A linearized model is then obtained around the operating point from the ANN model. The linearized and the ANN models are used for prediction for the linear and nonlinear predictive controllers, respectively. The MPC responses are compared with a benchmark PID controller behaviour, the parameters of which have been tuned to minimize the same criteria as used for the predictive controllers

Extension of first order Predictive Functional Controllers to handle higher order internal models

Predictive Functional Control (PFC), belonging to the family of predictive control techniques, has been demonstrated as a powerful algorithm for controlling process plants. The input/output PFC formulation has been a particularly attractive paradigm for industrial processes, with a combination of simplicity and effectiveness. Though its use of a lag plus delay ARX/ARMAX model is justified in many applications, there exists a range of process types which may present difficulties, leading to chattering and/or instability. In this paper, instability of first order PFC is addressed, and solutions to handle higher order and difficult systems are proposed. The input/output PFC formulation is extended to cover the cases of internal models with zero and/or higher order pole dynamics in an ARX/ARMAX form, via a parallel and cascaded model decomposition. Finally, a generic form of PFC, based on elementary outputs, is proposed to handle a wider range of higher order oscillatory and non-minimum phase systems. The range of solutions presented are supported by appropriate examples