Showing papers in "Isa Transactions in 2001"

Improving performance using cascade control and a Smith predictor.

Abstract: Many investigations have been done on tuning proportional-integral-derivative (PID) controllers in single-input single-output (SISO) systems. However, only a few investigations have been carried out on tuning PID controllers in cascade control systems. In this paper, a new approach, namely the use of a Smith predictor in the outer loop of a cascade control system, is investigated. The method can be used in temperature control problems where the secondary part of the process (the inner loop) may have a negligible delay while the primary loop (the outer loop) has a time-delay. Two different approaches, including an autotuning method, to find the controller parameters are proposed. It is shown by some examples that the proposed structure as expected can provide better performance than conventional cascade control, a Smith predictor scheme or single feedback control system.

Computer-based monitoring and fault diagnosis: a chemical process case study

Abstract: Principal component analysis (PCA) for process modeling and multivariate statistical techniques for monitoring, fault detection, and diagnosis are becoming more common in published research, but are still underutilized in practice. This paper summarizes an in-depth case study on a chemical process with 20 monitored process variables, one of which reflects product quality. Data from intervals of “good” operation times are used to determine a PCA model, and then data sets from intervals of “bad” operation times are compared to the model to detect the faulty variable and determine those variables responsible for the poor product quality. The analysis is performed using the PLS_Toolbox 2.01 with MATLAB. The methods used are reviewed summarily, and then results are shown based on typical application of the multivariate statistical techniques. An enhancement is made by using confidence limits on the residuals of each variable for fault detection rather than just confidence limits on an overall residual. Results show that the time required for fault detection is reduced. This approach was suggested in the literature, but its efficacy not demonstrated. Finally, ways to more effectively monitor processes and to more promptly detect and diagnose faults when they occur are identified.

Comparative study of a learning fuzzy PID controller and a self-tuning controller

Abstract: The self-organising fuzzy controller is an extension of the rule-based fuzzy controller with an additional learning capability. The self-organising fuzzy (SOF) is used as a master controller to readjust conventional PID gains at the actuator level during the system operation, copying the experience of a human operator. The application of the self-organising fuzzy PID (SOF-PID) controller to a 2-link non-linear revolute-joint robot-arm is studied using path tracking trajectories at the setpoint. For the purpose of comparison, the same experiments are repeated by using the self-tuning controller subject to the same data supplied at the setpoint. For the path tracking experiments, the output trajectories of the SOF-PID controller followed the specified path closer and smoother than the self-tuning controller.

Adaptive robust motion control for precise trajectory tracking applications

Abstract: This paper presents a robust servo control method for high precision motion control using linear actuators. The controller consists of three components : a simple feedforward compensator, a PID feedback controller, and a RBF (radial-basis function) adaptive compensator, each to fulfill a specific objective. The first two control components can be directly tuned based on only an estimated dominant second-order linear model. The RBF compensator is self-tuning, and it will compensate for remaining uncertainties in the system, residual of the linear model. Rigid proofs are provided, guaranteeing the robust stability of the proposed controller. Experimental results confirm the much superior performance of the 3-tier composite control over a standard motion controller.

A new modified Smith predictor: the concept, design and tuning.

Abstract: The paper presents a new modified Smith predictor (MSP) for processes with a long time delay. The MSP appears as an extension of the double controller-scheme (DCS) proposed by Tian and Gao. The important feature of the MSP is that the trade-off between disturbance rejection and robustness to variations in process parameters can be adjusted by means of a single free parameter. The main contribution of the paper concerns tuning of the MSP, which relies on a combination of magnitude optimum criterion with process parameterisation based on multiple integrals of the open-loop step response. In a simulation study the performance of the MSP is compared with that of two known controllers for time delay systems, i.e. DCS of Tian and Gao and Hagglund's predictive PI controller. The results show the advantage of the MSP compared to the two other controllers.

Experimental design of an optimal phase duration control strategy used in batch biological wastewater treatment.

Abstract: The paper presents the design of an algorithm used in control of a sequencing batch reactor (SBR) for wastewater treatment. The algorithm is used for the on-line optimization of the batch phases duration which should be applied due to the variable input wastewater. Compared to an operation with fixed times of batch phases, this kind of a control strategy improves the treatment quality and reduces energy consumption. The designed control algorithm is based on following the course of some simple indirect process variables (i.e. redox potential, dissolved oxygen concentration and pH), and automatic recognition of the characteristic patterns in their time profile. The algorithm acts on filtered on-line signals and is based on heuristic rules. The control strategy was developed and tested on a laboratory pilot plant. To facilitate the experimentation, the pilot plant was superimposed by a computer-supported experimental environment that enabled: (i) easy access to all data (on-line signals, laboratory measurements, batch parameters) needed for the design of the algorithm, (ii) the immediate application of the algorithm designed off-line in the Matlab™ package also in real-time control. When testing on the pilot plant, the control strategy demonstrated good agreement between the proposed completion times and actual terminations of the desired biodegradation processes.

Tolerable risk guidelines

Abstract: Safety instrumented systems (SIS) should be designed to reduce the amount of risk in a process to a tolerable level. Expressing the tolerable level of risk is one of the most difficult tasks facing organizations trying to comply with the standards that govern SIS use. Organizations are basing their risk decision-making criteria on a variety of benchmarks including industry standards, local and foreign government regulations, practices of industry partners, and a qualitative assessment of what is fair and reasonable. This paper explores the ways that industry and government have made decisions on what risks are tolerable. The paper begins by reviewing qualitative criteria that are evaluated to determine what amount of risk can reasonably be tolerated. The paper also reviews some methods that have been used in government and industry to include financial aspects into the decision-making process. It then proceeds to review some of the quantitative ways that risk is represented. After discussing risk presentation, the paper reviews and compares tolerable risk guidelines that have been set by government and industry. The paper concludes by comparing the amount of risk reduction that can be justified by sociopolitical tolerability guidelines and that which can be justified by cost-benefit analysis.

A comparison of traditional and adaptive control strategies for systems with time delay.

Abstract: A recently developed tuning method is compared to an adaptive Smith Predictor control strategy. The robustness of each method is considered for time-varying plant parameters. Examples with simulations are provided to compare the methods and present conclusions on the advantages and disadvantages of each.

Development of inferential measurements using neural networks.

Abstract: In many industrial processes, the most desirable variables to control are measured infrequently off-line in a quality control laboratory In these situations, use of advanced control or optimization techniques requires use of inferred measurements generated from correlations For well-understood processes, the structure of the correlation as well as the choice of inputs may be known a priori However, many industrial processes are too complex and the appropriate form of the correlation and choice of input measurements are not obvious Here, process knowledge, operating experience, and statistical methods play an important role in development of correlations This paper describes a systematic approach to the development of nonlinear correlations for inferential measurements using neural networks A three-step procedure is proposed The first step consists of data collection and preprocessing Next, the process variables are subjected to simple statistical analyses to identify a subset of measurements to be used in the inferential scheme The third step involves generation of the inferential scheme We demonstrate the methodology by inferring the ASTM 95% endpoint of a petroleum product using data from a domestic US refinery

Composite control of a gyro mirror line-of-sight stabilization platform--design and auto-tuning.

Abstract: In this paper, we propose a composite control scheme for a gyro mirror line-of-sight stabilization system. It comprises of an adaptive controller augmented with a variable structure (VS) to remain efficient in the face of modeling errors and extraneous disturbances. Under an adequate selection of control parameters, the control scheme guarantees an asymptotically stable closed-loop response. An automatic tuning method is developed for the proposed control scheme which explicitly uses inherent properties of the control structure for tuning purposes. Simulation and experimental results are provided to illustrate the operational principles of the proposed scheme and to demonstrate its viability in real practical applications.

Robust PID controller design for non-minimum phase time delay systems

Abstract: A robust PID controller for a non-minimum phase system subject to uncertain delay time is presented in this paper. Utilizing the gain-phase margin tester method, a specification-oriented parameter region in the parameter plane that characterizes all admissible controller coefficient sets can be obtained. The PID controller gains are then directly selected from the parameter region. Henceforth, the designed controller can guarantee the system at least a pre-specified safety margin to compensate for the instability induced by the time delay. A compromise between the robustness and tracking performance of the system in the presence of time delay is achieved. Simulation results indicate that the proposed method performs a good time response, and robustness is obtained effectively.

Predictive PI versus Smith control for dead-time compensation

Abstract: In this paper, the design and analysis of a predictive PI controller, capable of dead-time compensation, and which overcome certain deficiencies of Smith control, is presented. The design is based on a first-order plus dead-time model of the actual process, which is representative of the majority of process dynamics encountered in the industry. Only simple classical closed-loop specifications are required from the user. Simulation examples, including one based on an analog process simulator, are provided to highlight the principles of the proposed scheme and to compare its performance with Smith control.

Application of the SCADA system in wastewater treatment plants.

Abstract: The implementation of the SCADA system has a positive impact on the operations, maintenance, process improvement and savings for the City of Houston's Wastewater Operations branch. This paper will discuss the system's evolvement, the external/internal architecture, and the human-machine-interface graphical design. Finally, it will demonstrate the system's successes in monitoring the City's sewage and sludge collection/distribution systems, wet-weather facilities and wastewater treatment plants, complying with the USEPA requirements on the discharge, and effectively reducing the operations and maintenance costs.

Optimisation of fed-batch culture of hybridoma cells using genetic algorithms

Abstract: In this paper, a program describing a genetic algorithm is used for optimising fed-batch culture hybridoma cells to obtain the highest yield over certain time period. Optimal feed rate trajectories for a single feed stream containing both glucose and glutamine, and separate feed streams of glucose and glutamine are determined via the genetic algorithm. As compared to the optimal constant feed rate regime, optimal varying feed rate trajectories improve the final monoclonal antibodies concentration by 10% for the single feed rate case and by 39% for the multi feed rate case in this simulation. In comparsion with a dynamic programming, GA calculated feed trajectories yield a much higher level of monoclonal antibodies concentration.

Analytical design and analysis of mismatched Smith predictor

Abstract: In this paper, an analytical design method is developed for the mismatched Smith predictor on the basis of the internal model control (IMC) method. Design formulas are given and design procedure is significantly simplified. One important merit of the proposed method is that the response of the closed loop system can be easily adjusted. The relation between the controller parameter and the system response is monotonous. In addition, necessary and sufficient condition for the robust stability of mismatched Smith predictor is also given. It is shown that the proposed method can provide good performance for perfectly matched system and a better response for mismatched system. Several numerical examples are given to illustrate the proposed method.

Guide lines for the tuning and the evaluation of decentralized and decoupling controllers for processes with recirculation.

Abstract: This paper gives guidelines for the pairing, the time response specification, and the tuning for processes with recirculation when decentralized controllers are used. This selection is based on the condition number, which is an indicator of the process directionality, and on the generalized dynamic relative gain (GDRG), which is a measure of the interaction. Simple tuning rules are developed and results are compared to algebraic controllers with decouplers. Performances are evaluated for set-point changes as well as disturbance rejection using the generalized step response (GSR). The GSR gives a 3D graphic of the system response as a function of the input direction.

A new autotuning algorithm for PID controllers using dead-beat format.

Abstract: A novel algorithm for PID controllers based on dead-beat control and fuzzy inference mechanism is presented in this paper. The proposition is an extension of the work by the authors where the PI form of the algorithm was presented. The inclusion of the derivative term makes the method suitable for application in all types of processes including the ones having high rate disturbances. The proposed algorithm seems to be a complete and generalized PID autotuner as can be seen by the simulated and experimental results. In all the cases the method shows substantial improvement over the controller tuned with Ziegler Nichol's formula and the PI controller proposed in R. Bandyopadhyay, D. Patranabis, A fuzzy logic based PI autotuner, ISA Transactions 37 (1998) 227–235.

Winner take all experts network for sensor validation

Abstract: The validation of sensor measurements has become an integral part of the operation and control of modern industrial equipment The sensor under harsh environment must be shown to consistently provide the correct measurements Analysis of the validation hardware or software should trigger an alarm when the sensor signals deviate appreciably from the correct values Neural network based models can be used to on-line estimate critical sensor values when neighboring sensor measurements are used as inputs The underlying assumption is that the neighboring sensors share an analytical relationship The discrepancy between the measured and predicted sensor values may then be used as an indicator for sensor health The proposed Winner Take All Experts (WTAE) network based on a ‘divide and conquer’ strategy significantly reduces the computational time required to train the neural network It employs a growing fuzzy clustering algorithm to divide a complicated problem into a series of simpler sub-problems and assigns an expert to each of them locally After the sensor approximation, the outputs from the estimator and the real sensor readings are compared both in the time domain and the frequency domain Three fault indicators are used to provide analytical redundancy to detect the sensor failure In the decision stage, the intersection of three fuzzy sets accomplishes a decision level fusion, which indicates the confidence level of the sensor health Two data sets, the Spectra Quest Machinery Fault Simulator data set and the Westland vibration data set, were used in simulations to demonstrate the performance of the proposed WTAE network The simulation results show the proposed WTAE is competitive with or even superior to the existing approaches

Verifying common-cause reduction rules for fault tolerant systems via simulation using a stress-strength failure model.

Abstract: Redundant programmable electronic systems are commonly used in many industrial processes for safety protection and high availability process control. Common-cause failures can significantly reduce the benefits of the redundancy designed into this equipment. To improve on this situation, a number of qualitative design rules for reducing common cause failures have been put forth. However, these rules have not previously been subjected to quantitative verification. It is important to understand the magnitude of common cause failures and how this varies with design changes. This information can be used to show how system designs can be improved by lowering common cause failure rates. A stress–strength simulation was created to simulate the failures of a programmable electronic system under different design scenarios and the common cause failure rate was computed for each case. The simulation results not only confirm that the qualitative design rules lowered common cause failure rates but also provide some quantitative assessment of how large the improvements can be in various cases.

Placing all closed loop poles of missile attitude control systems in the sliding mode via the root locus technique.

Abstract: This paper presents a robust control method for uncertain nonminimum phase systems with external disturbances. A systematic design algorithm is developed which links the sliding mode control and the root locus technique. Complete closed-loop pole placement is achieved in addition to the placement of the reduced order equivalent system poles. An integration function is employed in the sliding variable formulation. The output tracking error is guaranteed to vanish. The proposed method was successfully applied to control the angle of attack of a missile attitude control system.

Optimal filtering and Bayesian detection for friction-based diagnostics in machines.

Abstract: Non-model-based diagnostic methods typically rely on measured signals that must be empirically related to process behavior or incipient faults. The difficulty in interpreting a signal that is indirectly related to the fundamental process behavior is significant. This paper presents an integrated non-model and model-based approach to detecting when process behavior varies from a proposed model. The method, which is based on nonlinear filtering combined with maximum likelihood hypothesis testing, is applicable to dynamic systems whose constitutive model is well known, and whose process inputs are poorly known. Here, the method is applied to friction estimation and diagnosis during motion control in a rotating machine. A nonlinear observer estimates friction torque in a machine from shaft angular position measurements and the known input voltage to the motor. The resulting friction torque estimate can be analyzed directly for statistical abnormalities, or it can be directly compared to friction torque outputs of an applicable friction process model in order to diagnose faults or model variations. Nonlinear estimation of friction torque provides a variable on which to apply diagnostic methods that is directly related to model variations or faults. The method is evaluated experimentally by its ability to detect normal load variations in a closed-loop controlled motor driven inertia with bearing friction and an artificially-induced external line contact. Results show an ability to detect statistically significant changes in friction characteristics induced by normal load variations over a wide range of underlying friction behaviors.

Improved process control alarm operation.

Abstract: Alarms are the main connection from the automation to the operator, when addressing process operation outside of its normal function. They are often as much a source of operator overload and consternation as help. Better engineering of the relative role of the operator and automation would materially help overcome the difficulties. Expert systems have been proposed as a solution. But Expert systems are really another form of automation. There remains that aspect of the alarms, which must address our inability to cover and understand a possibly larger domain of the operation not appropriate to traditional controls or present-day automation. Appropriate tools for this domain must support operator discretion and initiative. The paper suggests a set of such general, computer science based, tools requiring only the most basic configuration. They are viewed as implemented on top of those properly detailed alarm displays and interlocks, which reflect the more formal plant operating policies. They include: (a) Various forms of alarm logging and trending; (b) Short, automatically generated, word summaries of alarm activity, which allow low level data to propagate to the highest levels, including: one word and priority summaries; (c) Causal alarm pattern analyses that help the operator to predict or diagnose alarm behavior; (d) Automatic adaptation of alarms and alarm limits to varying process situations; (e) Uniform use of alarm policies to simplify alarm configuration.

Nonlinear mill control.

Abstract: A mill is a mechanical device that grinds mined or processed material into small particles. The process is known to display significant deadtime, and, more notably, severe nonlinear behavior. Over the past 25 years attempts at continuous mill control have met varying degrees of failure, mainly due to model mismatch caused by changes in the mill process gains. This paper describes an on-line control application on a closed-circuit cement mill that uses nonlinear model predictive control technology. The nonlinear gains for the control model are calculated on-line from a neural network model of the process.

A framework for modelling in S88 constructs for scheduling purposes.

Abstract: ISA S88.01 [1] [ISA (ANSI/S88.01.1995), Standard batch control; part 1: models and terminology, Instrument Society of America, 1995] is a standard that provides a methodology for the dissemination of a batch into standard models and provides the terminology for defining the control requirements of batch plants. The nature of S88 is such that it is not only applicable to the development of computer control systems for a batch plant, but also could be utilised within batch management software, i.e. scheduling. This paper presents a method of interpreting multi-purpose/product batch plant into S88 constructs to provide an object oriented framework for the research and development of a batch scheduling Matlab toolbox. Such a framework provides a thorough and efficient method of articulating the model to effectively apply automatic scheduling/optimisation methods.

Output feedback neurolinearization

Abstract: Output-feedback neurolinearization as a new method in model independent input/output linearization has been developed. In this method input-output linearization is done only based on the input–output data of the system. The performance of this new method is compared to the global linearizing control (GLC) and to a conventional PI controller. The systems selected for comparison purposes are temperature control of a CSTR reactor and pH control in a neutralization process. Output feedback neurolinearization, outperforms the other controllers both in setpoint tracking and disturbance rejection. The other important advantage of Output Feedback Neurolinearization is the fact that it does not depend upon the process model at all.

Output-sliding control for a class of nonlinear systems.

Abstract: In this paper, a systematic output-sliding control design methodology for nonlinear multivariable systems is presented. The control law consists of a continuous nominal control and a discontinuous switching control. The former is the equivalent control for the system with all uncertainties at zero and the latter is designed for nonzero uncertainties. Simulation results indicate that the proposed switching control law drives the system state trajectories onto the chosen switching surface in finite time and the output tracking is achieved.

Intelligent system for start-up of a petroleum offshore platform.

Abstract: It is difficult to control and to manage the start-up of a petroleum offshore platform. In order to solve this problem an intelligent system can play an important role, since available qualitative operator and design knowledge can be easily implemented to assist the operator during start-up. This paper describes the integration of an expert system with a fuzzy controller applied to such a process. The intelligent system has many heuristic rules to implement the automation of the start-up procedures, like the opening of many on–off valves while simultaneously monitoring process variables. It also has a fuzzy controller to optimize the opening of the oil wells, in order to minimize the start-up time. This intelligent system is being implemented in the platform P-19 of Petrobras, the Brazilian oil company, in Campos Basin, Brazil. The prototype has been operating since October 1998.

Robust control for a class of time varying systems via switching hypersurface design with output signals.

Abstract: Sliding mode control methods have been used widely since they provide robustness against parameter variations and disturbances. This paper focuses on the problem of a robust output-sliding control design for linear uncertain multi-input multi-output time-varying systems with norm-bounded uncertainty. Output signals are used for the definition of switching hypersurfaces. The formulation of a control law is emphasized. Output tracking can be achieved against a class of time varying parameter variations and external disturbances. The effectiveness of the proposed output-sliding control is confirmed by an application example.

Quantitative feedback synthesis of sampled-data systems with time-delay by approximate Z-transform.

Abstract: In this paper, the equivalent disturbance rejection (EDR) in QFT design methodology is proposed for dealing with sampled-data systems with time-delay This EDR is mainly to overcome the non-minimum phase zero generated by the first order Pade' approximation of the time-delay factor Due to plant parameter uncertainty, the analogue controller is to be designed so that the system response lies within permissible bounds By approximate Z-transform, the analogue controller can be transformed directly into a digital one and then the analogue plant is transformed into the digital plant, with sampling time as a free parameter By adjusting the sampling time, the uncertain sampled-data system can be stabilized In comparison with other approaches, our design framework is much more systematic by using only algebraic manipulations and transparent enough to guide the designer to realize the physical controller for the plant with prescribed bounds on its parameters