Nonparametric nonlinear model predictive control

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Preview-based techniques for vehicle suspension control: a state-of-the-art review

Abstract Automotive suspension systems are key to ride comfort and handling performance enhancement. In the last decades semi-active and active suspension configurations have been the focus of intensive automotive engineering research, and have been implemented by the industry. The recent advances in road profile measurement and estimation systems make road-preview-based suspension control a viable solution for production vehicles. Despite the availability of a significant body of papers on the topic, the literature lacks a comprehensive and up-to-date survey on the variety of proposed techniques for suspension control with road preview, and the comparison of their effectiveness. To cover the gap, this literature review deals with the research conducted over the past decades on the topic of semi-active and active suspension controllers with road preview. The main formulations are reported for each control category, and the respective features are critically analysed, together with the most relevant performance indicators. The paper also discusses the effect of the road preview time on the resulting system performance, and identifies control development trends

Advancing block-oriented modeling in process control

The increasing pressure in industry to maintain tight control over processes has led to the development of many advanced control algorithms. Many of these algorithms are model-based control schemes, which require an accurate predictive model of the process to achieve good controller performance. Because of this, research in the fields of nonlinear process modeling and predictive control has advanced over the past several decades.;In this dissertation, a new method for identifying complicated block-oriented nonlinear models of processes will be proposed. This method is applied for LNL and LLN sandwich block-oriented models and will be shown to accurately predict process response behavior for a simulated continuous-stirred tank reactor (CSTR) and a pilot-scale distillation column. In addition, it will be shown to effectively model the pilot-scale distillation column using closed-loop, highly correlated input data.;Using the block-oriented models identified, a new feedforward control framework has been developed. This feedforward control framework represents the first that compensates for multiple input disturbances occurring simultaneously. Only a single process model is needed to account for all measured disturbances. In addition, it allows a plant engineer to develop the predictive model of the process from plant historical data instead of introducing a series of disturbances to the process to try to identify the model. This has the potential to considerably reduce the cost of implementing an advanced control scheme in terms of time, effort and money. The proposed feedforward control framework is tested on a simulated CSTR process in Chapter 4, and on a pilot-scale distillation column in Chapter 5

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Improved multi model predictive control for distillation column

Model predictive control (MPC) strategy is known to provide effective control of chemical processes including distillation. As illustration, when the control scheme was applied to three linear distillation columns, i.e., Wood-Berry (2x2), Ogunnaike-Lemaire-Morari-Ray (3x3) and Alatiqi (4x4), the results obtained proved the superiority of linear MPC over the conventional PI controller. This is however, not the case when nonlinear process dynamics are involved, and better controllers are needed. As an attempt to address this issue, a new multi model predictive control (MMPC) framework known as Representative Model Predictive Control (RMPC) is proposed. The control scheme selects the most suitable local linear model to be implemented in control computations. Simulation studies were conducted on a nonlinear distillation column commonly known as Column A using MATLAB® and SIMULINK® software. The controllers were compared in terms of their ability in tracking set points and rejecting disturbances. Using three local models, RMPC was proven to be more efficient in servo control. It was however, not able to cope with disturbance rejection requirement. This limitation was overcome by introducing two controller output configurations: Maximizing MMPC and PI controller output (called hybrid controller, HC), and a MMPC and PI controller output switching (called MMPCPIS). When compared to the PI controller, HC provided better control performances for disturbance changes of 1% and 20% with an average improvement of 12% and 20% of the integral square error (ISE), respectively. It was however, not able to handle large disturbance of + 50% in feed composition. This limitation was overcome by MMPCPIS, which provided improvements by 17% and 20% of the ISE for all of types and magnitudes of disturbance change. The application of MMPCPIS on a single model MPC strategy produced almost similar performance for both types of disturbances, while its application on MMPC yielded better results. Based on the results obtained, it can be concluded that the proposed HC and MMPCPIS deserve further detailed investigations to serve as linear control approaches for solving complex nonlinear control problems commonly found in chemical industr

Fuzzy adaptive control system of a non-stationary plant with closed-loop passive identifier

Abstract Typically chemical processes have significant nonlinear dynamics, but despite this, industry is conventionally still using PID-based regulatory control systems. Moreover, process units are interconnected, in terms of inlet and outlet material/energy flows, to other neighboring units, thus their dynamic behavior is strongly influenced by these connections and, as a consequence, conventional control systems performance often proves to be poor. This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller, also exploiting the results coming from an identification procedure that is carried on when an unmeasured step disturbance of any shape affects the process behavior. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective