Plant Step Response Research Articles

Decoupling Model-Based Fuzzy Logic Control of Temperature and Humidity

The control of room temperature and humidity is important for ensuring of the necessary indoor human comfort for optimal work capacity and effective rest. The plant nonlinearity and the variables coupling require intelligent control techniques in order to satisfy the high performance demands. The present paper suggests a procedure for the design of a simple for industrial implementation fuzzy logic controller on the principle of parallel distributed compensation (PDC) that consists of linear local decoupling two-variable controllers. It is based on a Takagi-Sugeno-Kang (TSK) plant model, derived from experimentally obtained plant step responses using expert knowledge and parameter optimisation via genetic algorithms. The design is applied for the control of the temperature and the relative humidity of a laboratory air-conditioning system. The PDC system outperforms an existing Mamdani two-variable control system with adaptive properties in shorter settling time, higher robustness and reduced overshoot, estimated from simulations.

Optimal PI and PID Temperature Controls for a Dehydration Process

In this work, the drying air temperature for an experimental platform of sliced tomatoes dehydration is optimally regulated by using the very well-known integral control approach which could be equivalent to the PI control when the output system is the whole state. In a first stage, the chosen controller is a PI because the plant step response does not present oscillations with low damping. However, obtained experimental results, applying this controller, present some oscillations in the closed-loop transient response. So, in a second stage, a discrete PID controller is applied and its gains are computed by using a numerical optimization algorithm based on the hill climbing method, which considers a quadratic performance index; the proposed procedure assumes an input delayed discrete model of the plant; therefore, the stability is verified by the closed-loop poles location. The reason to apply an optimal control strategy is motivated by the energy saving and the preserving of a desired closed-loop performance of the process. In order to contrast the advantages to use an optimal controller, the obtained experimental results are compared with a PID controller tuned with the improved Ziegler–Nichols rules, which is optimized only considering a criterion depending on the error (the energy consumption it is not involved). Moreover, the performance given by optimal PI control is also compared with an optimal state feedback control. The synthesized controllers (computed gains) are programmed in an industrial programmable logic controller Siemens S7-1200, and they are applied to a sliced tomato dehydration plant. According to the obtained results, when the optimally tuned PID controller is applied, it produces energy saving rates, more uniform drying conditions for the product and a better closed-loop plant performance (shorter settling time and nonovershoot, which are desired features in this type of process), with respect to other tested controllers. The presented experimental results by using the PI and PID optimized controllers allow to verify the feasibility of our proposal.

Effects of Plant Identification on the Generalized Predictive Controllers Design

Abstract The effects of the estmated plant models accuracy on the control system signals quality after generalized predictive controllers design are studied in the paper. Two identification approaches are used for different in structures discrete-time models – by optimization procedure in Optimization Toolbox based on the plant step response as a standard deterministic plant characteristic and by functions in System Identification Toolbox after experiments with random signals on the plant. The generalized predictive controllers are design according to the estimated models. The processes in the simulated control systems are analyzed concerning the effects of different kind plant models on the designed controllers.

Second order inverse response process identification from transient step response

Simple algorithms for identification of inverse response models from step response are difficult to obtain because analytically the solution of a system of coupled nonlinear equations is required. In this article we propose a simple identification procedure for second order inverse response processes, based on the plant step response. The algorithm provides the model parameters in a sequential way, thus avoiding the solution of a nonlinear equation system. Moreover the algorithm is flexible because it can be suited to user requirements, thus modifying the algorithm performance. Finally error bounds on the identified parameters are provided which are useful if the model is used for control design purposes.

New tuning rules of PI-like controllers with transient performances for monotonic time-delay systems

In this note, new tuning methods for Proportional-Integral (PI) are proposed for monotonic time delay systems. Like the Ziegler–Nichols method, the proposed technique requires only parameters obtained from the plant step response. Unlike conventional techniques, the resultant tuning parameters offer the advantage of obtaining stable response with prescribed dynamic behavior with relation to the maximum overshoot. The idea of the design is simple and relies on basics learned in an introductory control course.

Design of PI and PID controllers with transient performance specification

Proportional-integral-derivative (PID) controllers are widely used in industrial control systems because of the reduced number of parameters to be tuned. The most popular design technique is the Ziegler-Nichols method, which relies solely on parameters obtained from the plant step response. However, besides being suitable only for systems with monotonic step response, the compensated systems whose controllers are tuned in accordance with the Ziegler-Nichols method have generally a step response with a high-percent overshoot. In this paper, tuning methods for proportional-integral (PI) and PID controllers are proposed that, like the Ziegler-Nichols method, need only parameters obtained from the plant step response. The methodology also encompasses the design of PID controllers for plants with underdamped step response and provides the means for a systematic adjustment of the controller gain in order to meet transient performance specifications. In addition, since all the development of the methodology relies solely on concepts introduced in a frequency-domain-based control course, the paper has also a didactic contribution.

Performance-based control system design automation via evolutionary computing

This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD) automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal “off-the-computer” controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing, with good closed-loop performance and robustness in the presence of practical constraints and perturbations.

Evolutionary system identification in the time domain

This paper develops a genetic algorithm based technique that may be used to identify multivariable system identification directly from plant step response data. Using this technique, globally optimized models for linear and non-linear systems can be identified without the need for a differentiable cost function or linearly separable parameters. Results are validated against a benchmark identification problem and a laboratory test-rig for continuous and discrete-time systems.

Numerically Robust Computer Aided Markov-Equivalent CGPC design

Properly tuned CGPC control algorithms equipped with anticipated filtering constitute numerically efficient designs. The new approach is based on two principles: mean-square low-order Markovian approximation of the plant's step response within a properly determined time interval and a rational restricted Markov-equivalent redesign of the model of the plant. The objective of this paper is to robustify the numerical procedure.

046 Easy design of deadbeat control using plant Step Response Only

046 Easy design of deadbeat control using plant Step Response Only

Easy design of deadbeat control using plant step response only

The work deals with a simple design of a digital pole-assignment control of unknown single input-single output, time-invariant linear plants. For this aim only the pertinent plant step response has to be known. The controller parameters result from a straightforward evaluation of the step response, without using any explicit procedure of plant structure and parameter identification, provided that the plant is minimum-phase. The condition of minimum phase leads, however, to certain constraints related to the choice of the sampling rate. Sometimes additional measures may be taken, especially if the continuous transfer function itself is nonminimum-phase.

New stability tests and performance bounds for differential linear repetitive processes

Abstract This paper develops a new set of stability tests for differential unit memory linear repetitive processes based on plant step response data. These tests are then shown to produce, at no extra computational cost, unique computable information concerning system performance which is central to practically based control objectives. Computational aspects and open research problems are also discussed.

Easy Design of Deadbeat Control using Plant Step Response Only

The work deals with a simple design of a digital pole-assignment control of unknown single input-single output, time-invariant linear plants. For this aim the pertinent plant step response has to be known only. The controller parameters result from a straightforward evaluation of the step response, without using any explicit procedure of plant structure and parameters identification, provided that the plant is minimum-phase. The condition of minimum phase leads, however, to certain constraints related to the choice of the sampling rate. Sometimes additional measures may be taken, especially if the continuous transfer function itself is nonminimum-phase. The paper presents several ways to solve this problem. The approach belongs to the class of signal-based, identification-free ones. It can be mathematically supported using the algebraic control theory of Kučera [1]. The structure of the proposed controller is somewhat different from the conventional one. The controller, which is ahvays stable, is able to stabilize unstable plants, too. The design method is sufficiently robust for engineering practice. The basic idea is applied in an adaptive controller under development.

Simulation-based stability tests and performance bounds for differential non-unit memory linear repetitive processes

A new set of stability tests for differential non-unit memory linear repetitive processes is developed using plant step response data. These tests then lead, at no extra computational cost, to unique information concerning two crucial aspects of the behaviour of these processes. The benefits of this information in the formulation and solution of practically feasible control policies are clearly shown by the initial results on this general area reported here. Finally, some open research problems are noted.

Identification and Robust Control of pH in a CSTR using Step Response Data

This paper presents a design procedure and experimental results for identification and robust controller design for a pH-neutralization in a CSTR, based on the classical network synthesis using Laguerre functions. The coefficients of the Laguerre network are calculated from plant step response data. This design procedure inludes (1) derivation of a nonlinear transformation based on the experimentally obtained reaction (titration) curve to linearize the feedback, (2) estimation in the linearized space of a nominal model and its associated modelling error, and (3) design of a robust internal model controller with its filter selected to reflect the difference between open- and closed-loop system dynamics. The experimental results show that robust stability and performance are achieved for both cases of the process titration curve being time-invariant and time-varying between given bounds.

Advanced process control for injection molding

AbstractThis paper deals with the application of advanced control strategies to an injection molding process. Of particular interest is the control of one variable during the cooling phase of the molding process. Due to the cyclic dynamic nature of injection molding, the controlled variable must be regulated over a short cycle; the full process is completed within 10 seconds. Therefore, a key control objective is rapid setpoint regulation. A closed‐loop strategy has been implemented for the regulation of pressure within the mold during the cooling cycle. First order plus dead time models of the process have been identified from plant step responses. The performance of several control algorithms for this process are compared in simulation studies. These algorithms have given comparable regulatory and servo responses. Finally, the effectiveness of the closed loop controi system has been demonstrated experimentally using the PI algorithm. Simulated and experimental results are in excellent agreement.

The use of Quantitative Robustness Analysis for the Design of Multivariables Tuning Regulators

Abstract A new design procedure for multivariable tuning controllers of PI-type is proposed. The well known approach is extended, particularly with regard to an additional robustness analysis of the closed-loop system. This provides decisive benefits for practical controller tuning, since the number of on-plant experiments is reduced drastically and the presupposed experimental conditions are more realistic. The design, completely working in the time-domain, starts from experimentally gathered data of the plant's step response matrix H(t). An exact model is not required. Measurement errors of H(t) and other uncertainties of the nonparametrical impulse response model used, are dealt with by means of multivariable upper error bounds. A suitable tuning controller for the approximate model is found by simulation based on convolution. In the next step a quantitative robustness analysis is used, to decide whether the incompletely known real closed-loop system fulfils the design requirements, too. Safe predictions with respect to stability and transient performance on the closed-loop are derived. The approach considered results in an efficient computer aided controller design which has successfully been tested by several examples concerning industrial application.

A Concept of Industrial Adaptive Control Based on Robust Design Principle and Multi-Model Approach

The discussed concept of industrial control strategy for single loop temperature control in plastics extrusion contributes to problem solutions according to computer aided or automatic proper tuning of a standard PID-Type controller. The adaptive feature is realized by a controller parameter scheduling. The scheduling depends on actual plant characteristics identified during start-up phase or after significant set-point changes. The available controller parameter sets {KP, TI, TD} are gained by a systematic search of the best compromise of a multiple performance description of simulated closed loop step response. Robustness of each controller parameter set is met using a family of plant models representing a nominal, an upper, and a lower boundary of plant step response data. The multi model plant description results from least square identification. No changes of the standard PID controller structure is necessary; a tuning loop only superimposes the well known digital control loop leading to a more easy to use PID control device which behaves robust and adaptive.