Rule-based Control Strategy Research Articles

Effect of speed and accuracy of on-line elemental analysis on flotation control performance

In this paper the control performance of the flotation process is evaluated as a function of the measurement accuracy and sampling frequency of an on-stream analyzer. First, the performance of rule-based control and model predictive control (MPC) strategies is studied using discrete flotation models and the respective performance indices. Next, the net smelter return (NSR) is calculated for varying sampling rate and accuracy combinations using the PI controllers-based control strategy, mechanistic flotation models and the industrial process data as input. The control and economical performance of the process declines strongly when the sampling cycle is increased. The results also indicate that the speed of on-line analysis has a significant effect on the production economics, calculated as the average net smelter return.

Design of a fuzzy-based controller for electric vehicles on Indian roads

This paper presents a novel approach with a rule-based acceleration control strategy for electric vehicles. This proposal has a straightforward goal for minimizing the complexities of the existing controllers used in vehicles. The use of fuzzy logic enables the heuristic rule-based technique to be used, as an efficient solution. The fuzzy logic controller designed here is a combination of fuzzy decision maker and fuzzy speed controller. The fuzzy decision maker is designed in such a way that it takes into account the battery's state of charge, speed, type of terrain, road load, brake, acceleration, and gear as input parameters, which gives the reference speed to the fuzzy speed controller according to the changing situations. The fuzzy speed controller is designed for the d.c. chopper-fed drive for the seperately excited motor. The fuzzy logic controller determines the vehicle speed according to the scenarios in Indian road conditions. Simulation results would specify the performance of the new proposed fuzzy controller. The results have been compared with the conventional controller proportional integral. From the results it has been inferred that the range of the vehicle has been increased, with less error in speed, with the fuzzy logic controller, than with the classical proportional integral controller. This controller has also been implemented in the embedded chip, field programmable gate array (FPGA) and results shown experimentally, which is the future vehicle.

Model-Based Control of Renewable Energy Systems in Buildings

This paper describes a research project concerned with the problem of supervisory control of systems that combine a range of heat sources with active and passive thermal storage. The work is based on a prototype building that includes a ventilated photovoltaic array, solar air and water heating, a biomass-fired boiler, and a stratified thermal store. The supervisory control problem is, for each source, whether to deploy the received energy directly into the building, store it for later use, or reject it to the environment. These decisions are currently made by a building management system programmed with a complex, arbitrary set of rules and setpoints. The considerable number of operational states means that such a control system is very difficult to commission. Analysis of the data for the early stages of building operation indicate strongly that it is unlikely optimal use is being made of the renewable energy sources with this approach. The objective of this research is to investigate the potential effectiveness of replacing the rule-based control scheme with one based on a combination of a building/system model with an optimization algorithm. The existing building, plant, and control systems were modeled using a commercial simulation environment and calibrated against measured data from the building. A period of swing-season operation was used to compare the existing control with that based on an optimal approach. Results indicate that significant improvements in system operation are possible but also that significant improvement in execution time will be needed for any future online deployment.

Simulation of a generic flexible assembly system

During the early 1980s, the concept of a flexible assembly machine was first suggested by Hounsfield (1983). Following this, a variety of research projects have been conducted in an attempt to develop a flexible assembly system that is functional and economically viable. The most significant contribution has been made by the EURICA EU 321 - FAMOS - INFACT* project. However, despite the developments made during these projects, no industrial based system exists today. This paper presents a simulation model of a novel concept for a multi-station flexible automatic assembly machine, and examines the application of a rule based control strategy for the control of a materials handling system used in such a system. * http://www.3.eureka.be/home/

A neural network approach for the on-line estimation of workpiece height in WEDM

In this paper, a feed-forward neural network is used to estimate the workpiece height and distinguish the machining condition in wire electrical discharge machining (WEDM). Some experiments have been carried out to verify the effectiveness of this approach. Based on the on-line estimated workpiece height, a rule-based strategy is proposed to maintain optimal and stable machining. According to the rule-based strategy, servo voltage and power settings can be adjusted correctly to suit the workpiece profile. Experimental results demonstrate that high machining efficiency and stable machining can be achieved by means of the rule-based control strategy.

A Multi-Level Fuzzy Rule Based Control Strategy for Maglev System

A Magnetically Levitated Vehicle System (MAGLEV) is nonlinear complex and imprecise and therefore, difficult to be expressed in terms of a mathematical model. Non-linear feedback control and strategies using Linear small perturbation models do not yield satisfactory results. Such a system is best described by quantitatively and easily handled by human operators, A Fuzzy logic approach based on inputs: outputs and control response is proposed to control the Maglev system. The input and output variables are usually crisp numbers with many permitted levels in an allowed range, and the system parameters are linguistic in nature. A multilevel fizzy logic rule based control strategy is being suggested for the Manley system. The proposed control strategy offers advantages in designing a fuzzy controller for the Maglev system.

The Application of Fuzzy Control Strategy in Servo Feed Control of Wire Electrical Discharge Machining

In this paper, a detailed discussion of the design of a fuzzy controller for servo feed control in wire electrical discharge machining (WEDM) is presented from the viewpoint of industrial application and implementation. The proposed controller contains two control loops. The main loop employs fuzzy logic as a rule-based control strategy for the gap voltage control. The output scaling factor of the fuzzy controller is on-line adjusted to obtain better control performance. A secondary loop is used to maintain machining stability by adjusting the reference voltage. The developed control system is tested under the conditions of approach machining, rough machining, and cutting a workpiece with a corner working path. Experimental results show the developed fuzzy control system is more feasible and effective for commercialised WEDM machines than a proportional controller.

Neural networks for the identification of the aluminium electrolysis process

The proper operation of an aluminium electrolysis cell relies strongly on the ability of the control system to devise an appropriate alumina feeding sequence and on the ability to overcome anodic effects. This complex system is normally controlled using a rule-based control algorithm that stems from the process knowledge acquired over many years of operation. This paper first considers the use of feedforward neural networks to predict, 15 min in advance, two important variables that are directly used in the rule-based control algorithm: the cell resistance and the cell fast dynamic trend indicator. The addition of these two predictions greatly enhanced the operability of the cell. The linear vector quantization (LVQ) neural network was then used to classify the current operating state of the electrolysis cell in view of adapting the rule-based control strategy to each particular state. This classification procedure allowed to devise more appropriate control strategies and to estimate the alumina concentration of the cell.

Rule based control of a periodic activated sludge process

A rule based control strategy for automatically adjusting phase lengths and aeration intensity for an activated sludge nutrient removal process based on a periodic operation is examined using simulations based on the Activated Sludge Model No. 1. The strategy is based on four criterion functions, two which determine the switching of the roles of two nitrifying/denitrifying reactors and two which adjust the dissolved oxygen setpoint levels in the two reactors as functions of ammonia and nitrate concentrations. Trajectory plots of reactor concentrations in the ammonia-nitrate plane are shown to be a useful means of visualizing process and control performance. Together, the trajectories from a working region in the ammonia-nitrate plane, the size and location of which can to some extent be predetermined by selection of the criterion functions. The presented results include the influence of one of the criterion functions on control strategy performance, how an incompatibility between two criterion functions can lead to unsymmetric reactor loading, and the effect of allowing simultaneous nitrification and denitrification during nitrifying periods by reducing the dissolved oxygen level as ammonia is consumed.

Structured PENN Controller for MIMO Process.

This paper presents a neural net controller for a multi-input, multi-output (MIMO) process. The controller is based on the PENN (Policy- and Experience-driven Neural Network) method. The PENN uses two types of knowledge for learning: local experiences obtained from the control results; and global policies based on rule-based control strategy. The structured PENN that is proposed for a MIMO process in this study has a structure in which small controllers for SISO processes are assembled. By use of such a structured assembly, both learning and control for a MIMO process can be accomplished. To prevent the effect of noises observed in the controlled variables, a noise reduction mechanism modifying the error function in the learning algorithm is proposed. The simulation results for a crystal growth process indicate that the proposed controller has the ability to learn the interactions between control variables and to get satisfactory control results.

Rule based control of a periodic activated sludge process

A rule based control strategy for automatically adjusting phase lengths and aeration intensity for an activated sludge nutrient removal process based on a periodic operation is examined using simulations based on the Activated Sludge Model No. 1. The strategy is based on four criterion functions, two which determine the switching of the roles of two nitrifying/denitrifying reactors and two which adjust the dissolved oxygen setpoint levels in the two reactors as functions of ammonia and nitrate concentrations. Trajectory plots of reactor concentrations in the ammonia-nitrate plane are shown to be a useful means of visualizing process and control performance. Together, the trajectories form a working region in the ammonia-nitrate plane, the size and location of which can to some extent be predetermined by selection of the criterion functions. The presented results include the influence of one of the criterion functions on control strategy performance, how an incompatibility between two criterion functions can lead to unsymmetric reactor loading, and the effect of allowing simultaneous nitrification and denitrification during nitrifying periods by reducing the dissolved oxygen level as ammonia is consumed.

A fuzzy-logic approach to industrial control problems

Increasing demands for improved profitability and product quality, together with a growing awareness of the effects of industrial wastage on the environment, is forcing manufacturers to closely examine their process operations. As a consequence there is currently significant research and development activity aimed at improving control system strategies in a variety of industrial sectors. Recent years have witnessed renewed interest in fuzzy logic and rule-based control strategies and, by considering two illustrative industrial case studies, this paper highlights some of the potential advantages.

Development and Evaluation of a Rule-Based Control Strategy for Ice Storage Systems

This paper describes the development and evaluation of a near-optimal control strategy for ice storage systems. The strategy is based upon simple heuristics that were developed from daily and monthly simulations of cooling systems with internal melt, area-constrained ice storage tanks. Dynamic programming was used to obtain the optimal control trajectories which minimized an integrated energy and demand cost function for both the daily and monthly simulations. In addition to leading to simple heuristics, the monthly optimal control results were used as benchmarks to evaluate the performance of both conventional and the new control strategy. For a range of partial-storage systems, load profiles, and utility rate structures, the monthly electrical costs for the rule-based control strategy were, on average, within about 3% of the optimal costs. In contrast, the monthly electrical costs associated with the most common conventional control strategy, chiller-priority control, were as much as 20% greater than optimal, whereas a simple storage-priority strategy yielded costs that were within about 6% of optimal. The rule-based strategy can be easily implemented within a small micro-processor controller and only requires measurements of the system cooling requirement, building electrical usage, and state-of-charge of storage.

Rule-Based Control Algorithm for Active Tuned Mass Dampers

A rule-based control algorithm for active tuned mass dampers (TMDs) is proposed. First, perturbation solutions of the linear quadratic regulator (LQR) feedback gains for the active TMD system are derived. Using these solutions, interaction of the TMD and the actuator force is discussed in detail. Then, an intelligent rule-based control scheme is constructed based on this discussion along with a previously developed semiactive control method. Fuzzy logic is incorporated with the rule bases to implement analytical and/or intuitive knowledge about the TMD into control rules. The algorithm consists of two parts: (1) a variable gain displacement feedback control; and (2) a variable TMD damping control. The first one is applied when the TMD amplitude is small to make the TMD more effective, and the second one is applied when the TMD amplitude is large to dissipate the energy. Treatment of constraints on control force capacity and TMD displacement is emphasized in construction of the controller. Performance of the proposed method is demonstrated by numerical studies of continuous structures with widely spaced natural frequencies subject to seismic excitation.

Control System Design Using Model-Based and Rule-Based Techniques - An Integrated Approach

In recognition of the potential benefits of exploiting the desirable features of both model-based and rule-based control strategies, this paper proposes a combined approach which offers robustness at the expense of a modest increase in computational complexity. By combining the adaptivity of a model-based self-tuning controller with the simplicity of a rule-based fuzzy logic controller, the overall combined controller is shown to offer a realistic approach which produces results that are of a superior nature, in terms of robustness, over either the model-based or rule-based scheme in isolation.

Rule-based thermostat design for night setback of heat pumps

We introduce a computer model for testing heat-pump thermostat performance with regard to energy and cost savings due to night setback. Also introduced are two new control strategies for heat-pump night setback: 1. (i) an inexpensive variation of existing control strategies and 2. (ii) a rule-based control strategy using techniques of artificial intelligence. The computer model is based on a transient system model of the residence, heat-pump, and controller, in response to actual weather data. Seasonal energy usage and costs are computed for each control strategy, subject to the effects of variable heat-pump sizing, building thermal inertia, building heat loss, climatic region, and electric rate structure. Results show that night setback can reduce heating season costs in residential heat-pump systems if the control strategy takes into account the characteristics of both the heat-pump system and the building. If these factors are not considered, night setback can increase heat-pump operating costs. The rule-based control strategy consistently provides positive cost savings above other setback methods, and has the added potential of providing diagnostic information about both the heat-pump system and the residence.

Report on Actual Benefits Arising From The Application of Expert Control Systems in Industrial Semi-Autogenous Grinding Circuits

A new approach to the control of semiautogenous grinding mills is described. The approach is based on the use of on-line models combined with on expert system rule based control strategy. This approach has been shown to provide improvements in productivity of 6-15Z in difficult to control circuits. Two plant applications of this type of control system are presented.

A real-time, rule-based FMS operation control strategy in CIM environment—Part I

Abstract Conventional manufacturing system scheduling methods (even when run on superfast computers) cannot be considered appropriate in a real-time controlled CIM environment because of their inability to react quickly and in an ‘intelligent’ way to dynamic changes in the controlled system. The paper gives a brief overview of the scheduling problem in CIM/FMS architectures, demonstrates different methods of scheduling by showing algorithms and results, as well as outlining and demonstrating some new ways of dynamic scheduling. Part 1 gives a summary of the most important design rules enabling the implementation of a truly dynamic FMS operation control strategy, discusses methods such as loading sequence, balancing, capacity planning and others, and concludes by outlining a new, dynamic scheduling concept and scheduling rules.