Two degrees-of-freedom hybrid adaptive approach with pole-placement method used for control of isothermal chemical reactor

Continuous Stirred-Tank Reactors (CSTR) are technological plants often used in the chemical or biochemical industry for the production of various types of chemicals. These systems are very complex from the control point-of-view - mainly because of their nonlinearity. Controlling such processes by means of conventional methods that use controllers with fixed parameters; often produces bad - or even, unacceptable results. This is the right field for so-called "modern" control methods like Robust, Predictive, and Adaptive Control. The control method used in this work is a hybrid adaptive control where the originally nonlinear system is represented by the external linear model whose parameters are recursively identified during the control phase. The pole-placement method with a spectral factorization and two degrees-of-freedom (2DOF) control configuration used in the control synthesis in order satisfy the basic control requirements, for instance: stability, reference signal tracking and disturbance attenuation. Moreover, the resulting controller obtained from the polynomial synthesis is easily programmable and be implemented in control computers. All of the proposed methods were tested by simulations on a mathematical model of an isothermal CSTR, with a complex reaction inside. The results so obtained, demonstrate the applicability of this control method for these kinds of processes. The team used the MATLAB simulation program in this research. Copyright © 2017, AIDIC Servizi S.r.l.CZ.1.05/2.1.00/03.0089, ERDF, European Regional Development Fund; MOE, Ministry of Educatio

Robust control of continuous stirred tank reactor with jacket cooling

Continuous Stirred-Tank Reactors (CSTR) belong to basic technological equipment frequently used in the the production of various types of chemicals. These systems are quite complex with many nonlinearities. So, the conventional linear control with fixed parameters can be questionable or unacceptable. The solution should be found in so-called “non-traditional” control approaches like Adaptive, Robust, Fuzzy or Artificial Intelligent methods. One way is the utilization of selftuning adaptive schemes but computations are quite difficult, clumsy and time-consuming. This paper brings an alternative principle called robust approach. This approach considers a linear system with parametric uncertainty which covers a family of all feasible plants. Then a controller with fix parameters is designed so that for all possible plants the acceptable control behavior is obtained. The two degree of freedom (2DOF) structure for the control law was chosen. All calculation and simulations of mathematical models and control responses was performed in the Matlab and Simulink environment. Copyright © 2019, AIDIC Servizi S.r.l

Nonlinear versus ordinary adaptive control of continuous stirred-tank reactor

Unfortunately, the major group of the systems in industry has nonlinear behavior and control of such processes with conventional control approaches with fixed parameters causes problems and suboptimal or unstable control results. An adaptive control is one way to how we can cope with nonlinearity of the system. This contribution compares classic adaptive control and its modification with Wiener system. This configuration divides nonlinear controller into the dynamic linear part and the static nonlinear part. The dynamic linear part is constructed with the use of polynomial synthesis together with the pole-placement method and the spectral factorization. The static nonlinear part uses static analysis of the controlled plant for introducing the mathematical nonlinear description of the relation between the controlled output and the change of the control input. Proposed controller is tested by the simulations on the mathematical model of the continuous stirred-tank reactor with cooling in the jacket as a typical nonlinear system. © 2015 Jiri Vojtesek and Petr Dostal

Development of adaptive control methodologies and algorithms for nonlinear dynamic systems based on u-control framework

Inspired by the U-model based control system design (or called U-control system design), this study is mainly divided into three parts. The first one is a U-model based control system for unstable non-minimum phase system. Pulling theorems are proposed to apply zeros pulling filters and poles pulling filters to pass the unstable non-minimum phase characteristics of the plant model/system. The zeros pulling filters and poles pulling filters derive from a customised desired minimum phase plant model. The remaining controller design can be any classic control systems or U-model based control system. The difference between classic control systems and U-model based control system for unstable non-minimum phase will be shown in the case studies.Secondly, the U-model framework is proposed to integrate the direct model reference adaptive control with MIT normalised rules for nonlinear dynamic systems. The U-model based direct model reference adaptive control is defined as an enhanced direct model reference adaptive control expanding the application range from linear system to nonlinear system. The estimated parameter of the nonlinear dynamic system will be placement as the estimated gain of a customised linear virtual plant model with MIT normalised rules. The customised linear virtual plant model is the same form as the reference model. Moreover, the U-model framework is design for the nonlinear dynamic system within the root inversion.Thirdly, similar to the structure of the U-model based direct model reference adaptive control with MIT normalised rules, the U-model based direct model reference adaptive control with Lyapunov algorithms proposes a linear virtual plant model as well, estimated and adapted the particular parameters as the estimated gain which of the nonlinear plant model by Lyapunov algorithms. The root inversion such as Newton-Ralphson algorithm provides the simply and concise method to obtain the inversion of the nonlinear system without the estimated gain. The proposed U-model based direct control system design approach is applied to develop the controller for a nonlinear system to implement the linear adaptive control. The computational experiments are presented to validate the effectiveness and efficiency of the proposed U-model based direct model reference adaptive control approach and stabilise with satisfied performance as applying for the linear plant model

Feedback Linearizing Control Strategies for Chemical Engineering Applications.

Two widely studied control techniques which compensate for process nonlinearities are feedback linearization (FBL) and nonlinear model predictive control (NMPC). Feedback linearization has a low computational requirement but provides no means to explicitly handle constraints which are important in the chemical process industry. Nonlinear model predictive control provides explicit constraint compensation but only at the expense of high computational requirements. Both techniques suffer from the need for full-state feedback and may have high sensitivities to disturbances. The main work of this dissertation is to eliminate some of the disadvantages associated with FBL techniques. The computation time associated with solving a nonlinear programming problem at each time step restricts the use of NMPC to low-dimensional systems. By using linear model predictive control on top of a FBL controller, it is found that explicit constraint compensation can be provided without large computational requirements. The main difficulty is the required constraint mapping. This strategy is applied to a polymerization reactor, and stability results for discrete-time nonlinear systems are established. To alleviate the need for full-state feedback in FBL techniques it is necessary to construct an observer, which is very difficult for general nonlinear systems. A class of nonlinear systems is studied for which the observer construction is quite easy in that the design mimics the linear case. The class of systems referred to are those in which the unmeasured variables appear in an affine manner. The same observer construction can be used to estimate unmeasured disturbances, thereby providing a reduction in the controller sensitivity to those disturbances. Another contribution of this work is the application of feedback linearization techniques to two novel biotechnological processes. The first is a mixed-culture bioreactor in which coexistence steady states of the two cell populations must be stabilized. These steady states are unstable in the open-loop system since each population competes for the same substrate, and each has a different growth rate. The requirement of a pulsatile manipulated input complicates the controller design. The second process is a bioreactor described by a distributed parameter model in which undesired oscillations must be damped without the use of distributed control

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems

A constrained optimisation approach for designing reliable robust H∞ control systems

This research addresses passive fault-tolerant control problems for linear uncertain systems via reliable state feedback and output feedback robust H∞ controllers. Structured uncertainties considered are required to satisfy integral quadratic constraints. These controllers are to obtain an absolutely stable closed-loop system with a specified disturbance attenuation level. Solutions to these control problems involve solving parameterised Riccati equation(s). A feasible set of parameters used to solve the Riccati equation(s) is computed using a differential evolution algorithm