Advanced control of industrial processes. Structures and algorithms.
As the title suggests, the book is aimed both to structures and algorithms of advanced control of industrial processes in such a manner that algorithms of advanced feedback control are mainly presented, but also on-line set-point optimization is discussed, in appropriate structures. In this respect, a starting point for the issue is a multi-layered control structure of industrial processes. This has the advantage that it allows reasonable and safe control and management through a decomposition and distribution of tasks and responsibilities into a few well-defined and simpler sub-tasks of a more homogeneous character, mutually interconnected. The basic layers of the multilayer structure are two feedback control layers (regulatory control layers) and the optimization layer. Feedback control can be carried out at two layers (especially for more complex, multivariable processes). The first and lowest one is the direct control layer (basic control layer) which has the main task to maintain the process within prescribed limits defined by the set-point values for its controllers (direct controllers). In order to avoid some emergencies, it can be equipped with certain control logic which is responsible for overriding the control designed for normal operating conditions. In certain complex processes set-points of direct controllers are under supervisory control of advanced set-point controllers. These constitute a (higher) constraint control layer, whose controllers are usually responsible for controlling certain technological constraints influencing product quality. These advanced control concepts can be materialized in real time by using Distributed Control Systems (DCS) and Supervisory Control and Data Acquisition Systems (SCADA), which enable advanced realization of feedback control tasks. Usually they are implemented by algorithms more complex than the classic PID control law. These techniques are now applied mainly at the constraint control layer, where process nonlinearity, its multivariable nature and constraints play an important role. At the same time, they can also be addressed to direct control loops which are difficult for classic PID control, due to large delays, signal constraints or nonlinearities. In this context the task of the optimization layer is to economically evaluate best set-point values for the feedback controllers and the increasing computing power allows applying on-line optimization and automatic adjustment of the set-points to the varying external influences. In this respect optimization usually sets new requirements for feedback control systems, which may be solved by applying advanced control algorithms in the sense defined above. In order to give the reader a feeling for the content and applicability of the results we point out in the sequel the contents of the book: 1. Multilayer control structure: Control system; Control objectives; Control layers; Process modeling in a multilayer structure; Optimization layer; Supervision, diagnosis, adaptation; 2. Model-based fuzzy control: Takagi-Sugeno (TS) type fuzzy systems (Fuzzy sets and linguistic variables; Fuzzy reasoning; Design of TS fuzzy models; TS system as a fuzzy neural network); Discrete-time TS fuzzy control (Discrete TS fuzzy state feedback controllers; Discrete TS fuzzy output-feedback controllers); Continuous-time TS fuzzy control (Continuous TS fuzzy state-feedback controllers; Continuous TS fuzzy output-feedback controllers); Feedforward compensation, automatic tuning; 3. Model-based predictive control (MPC): The principle of predictive control; Dynamic matrix control (DMC) algorithm (Output predictions using step response models; Unconstrained explicit DMC algorithm; Constraining the controller output by projection; DMC algorithm in numerical version; Model uncertainty, disturbances); Generalized predictive control (GPC) algorithm (GPC algorithm for a SISO process; GPC with constant output disturbance prediction; GPC algorithm for a MIMO process; GPC algorithm in numerical version); MPC with state-space process model (Algorithms with measured state; Algorithms with estimated state; Explicit piecewise-affine MPCS constrained controller); Nonlinear predictive control algorithms (Structures of nonlinear MPC algorithms; MPC with nonlinear optimization; MPC nonlinear with successive linearization; MPC with nonlinear prediction and linearization; MPC algorithms using artificial neural networks, comparative simulation studies; Fuzzy MPC (FMPC) numerical algorithms; FMPC explicit unconstrained algorithms); Stability, constraint handling, parameter tuning (Stability of MPC algorithms; Feasibility of constraint sets, parameter tuning); 4. Set-point optimization: Steady-state optimization in multilayer process control structure; Steady-state optimization for model predictive control (MPC steady-state target optimization; Integrated approach to MPC and steady-state optimization; Adaptive MPC Integrated with steady-state optimization; Comparative Example results), Measurement-based iterative set-point optimization under uncertainty (Integrated system optimization and parameter estimation (ISOPE); ISOPE for Problems with Output Constraints). This valuable work is based on an adequate bibliography that is converted into a useful monograph by a careful topic selection and by the significance of applications and worked examples, as well as by results of many simulations based on industrial process models, stemming primarily from petrochemical and chemical industries. The book is highly recommended both to researchers and postgraduate students as well as to control/electrical/electronics engineers, applied mathematicians and any other professionals or scientists interested in the basic structures and algorithms of the advanced control systems.
scientific article; zbMATH DE number 5115941