Robert E. King

Two-stage robust control of substrate concentration for an activated sludge process

A two-stage robust control scheme improving the performance of an Activated Sludge Process is proposed. In the first stage, asymptotic command following the substrate concentration with simultaneous attenuation of the fluctuations of the dissolved oxygen concentration is assured. The first stage is a pure dynamic controller. The second stage is a PID controller. Good performance of the proposed control scheme on the corresponding nonlinear ASP model is illustrated through extensive simulation experiments. The contribution of the paper can be summarized to the derivation of the following two results: An accurate to a wide range of inputs and disturbances, linearized generic model of the ASP and, most important, a linear robust controller that controls accurately the effluent substrate concentration without using measurements of it.

Intelligent hybrid industrial control

Intelligent three term controllers are a natural evolution of the ubiquitous three-term industrial controller that has reigned supreme for almost a century. Through the addition of an embedded discrete-event supervisor, a conventional three-term controller can be endowed with the intelligence necessary to permit pareto-optimum operation over an extended range. Intelligent hybrid controllers provide improved control performance for highly nonlinear processes where conventional controllers are ineffective. We focus on a new type of intelligent logic-based switching controller.

Multivariable fuzzy controller design

Abstract The control of large multivariable industrial processes whose dynamics are either unknown or vague poses a very serious challenge in process control. Over the last fifteen years or so, the process industry has made serious progress in putting fuzzy logic to practice. Indeed numerous successful applications have been reported to date. The design of fuzzy controllers today is very much an art and there are presently few effective design tools available for their development. This fact has, regrettably, limited their application considerably. To this end, this paper describes an integrated development system which permits the controller designer to test hypotheses, examine rule validity and rule conflict, examine the effect of changes in the controller parameters and perform a complete off-line simulation of a proposed multivariable fuzzy controller.

Expert supervision and control of a large-scale plant

The process industry and the cement industry in particular, is rapidly realising the importance of expert systems for the control of large-scale production processes which have hitherto defied solution by conventional methods. Where the operational environment of a process industry is subject to vagueness and uncertainty, then expert control offers new opportunities for increased production, fuel economy, and enhanced product quality.This paper outlines a large-scale expert supervision and control system which was developed as part of a long-term project to apply advanced concepts of CIM to a cement production plant. The system comprises a cluster of nine expert subsystems using fuzzy logic, four of which are arranged in a multilayer architecture to synergistically control a rotary kiln and cooler complex. Experience with the real-time expert system since 1985, when it was first commissioned, has resulted in increases of the order of 4–5% in productivity and energy reduction as well as reduced plant maintenance over conventional manual control.

Qualitative-evolutionary design of greenhouse environment control agents

A novel technique that fuses qualitative reasoning and evolutionary computation in the design of control agents for the control of the environment of a greenhouse is proposed in this paper. Linguistic rules relate the attributes of the greenhouse performance in response to perturbations in the desired set points to the suitability of the agent. De-fuzzification of the fuzzy suitability membership function yields a quantitative measure of the fitness of the agent to satisfy the system performance specifications. The fitness measure is used in a genetic algorithm that performs a stochastic search for the global optimum parameters of the agent.

Prediction of Wastewater Pre-Precipitation Variables Using Self-Organizing Networks

This paper describes the derivation and design of an array of self-organizing networks trained by inductive learning for one step ahead prediction of the outputs of the pre-precipitation stage of a wastewater treatment plant with a view to model predictive control of the stage

A PLC-Based System for Advanced Control

The chapter presents a PLC-based system for advanced control called ASPECT. The ASPECT controller was designed to be an efficient and user-friendly engineering tool for the implementation of parameter-scheduling nonlinear control in the process industry, which is achieved by partial automation of the commissioning procedure. The key to the concept is the self-tuning mechanism. The controller parameters are automatically tuned from a nonlinear process model. The model is determined on the basis of operating process signals by experimental modelling, where an online-learning procedure is used. This procedure is based on model identification using the local learning approach. The two main components of the ASPECT system are the Run-time Module (RTM) and the Configuration Tool (CT). The RTM runs on a PLC or an embedded controller, performing all the main functionality of real-time control, online learning, and control performance monitoring. The CT, used on a personal computer (PC) only during the initial configuration phase, simplifies the commissioning procedure by providing guidance and default parameter values. The performance of the system is demonstrated with simulation experiments on a pH control process and with experimental application to an industrial valve-testing apparatus. In the conclusion, the lessons learned during the development and implementation of the system are discussed.

Intelligent Industrial Control

The process industry. and the cement industry in particular, is at the forefront in applying Artificial Intelligence and Intelligent Systems techniques to the production process. These techniques are being applied to advantage with significant improvements in product quality and productivity. This paper outlines the architecture of a hierarchical Intelligent Production Management and Control System for a cement plant.