New Predictor and 2DOF Control Scheme for Industrial Processes with Long Time Delay

© 2018 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] To address the difficulty of controlling industrial processes with long time delay, a novel design of dead-time compensator (DTC) is introduced, which can be used to predict the undelayed output response of any process (no matter stable or unstable) such that the control design may be focused on the delay-free part of the process for performance optimization. Based on the undelayed output estimation, a two-degree-of-freedom (2DOF) control scheme is analytically developed for optimizing the set-point tracking and disturbance rejection, respectively. By proposing the desired transfer functions, the corresponding controllers are analytically derived based on commonly used low-order process models. A notable advantage is that there is a single adjustable parameter in the proposed DTC, as well as in each controller, which can be monotonically tuned to meet a good tradeoff between the prediction (or control) performance and its robustness. Illustrative examples from the literature and a practical application to a temperature control system of a jacketed reactor are used to demonstrate the effectiveness of the proposed predictor-based control scheme.This work was supported in part by the NSF China under Grant 61633006 and Grant 61473054; in part by the National Thousand Talents Program of China, the PROMETEOII/2013/004, Conselleria d'Educacio, Generalitat Valenciana, and TIN2014-56158-C4-4-P-AR; in part by the Ministerio de Economia y Competitividad; and in part by the FPI-UPV 2014 Grant Program from the Universidad Politecnica de Valencia, Valencia, SpainLiu, T.; García Gil, PJ.; Chen, Y.; Ren, X.; Albertos Pérez, P.; Sanz Diaz, R. (2018). New Predictor and 2DOF Control Scheme for Industrial Processes with Long Time Delay. IEEE Transactions on Industrial Electronics. 65(5):4247-4256. https://doi.org/10.1109/TIE.2017.2760839S4247425665

Analytical design of a generalised predictor-based control scheme for low-order integrating and unstable systems with long time delay

In this study, the problem of controlling integrating and unstable systems with long time delay is analysed in the discrete-time domain for digital implementation. Based on a generalised predictor-based control structure, where the plant time delay can be taken out of the control loop for the nominal plant, an analytical controller design is proposed in terms of the delay-free part of the nominal plant model. Correspondingly, further improved control performance is obtained compared with recently developed predictor-based control methods relying on numerical computation for controller parameterisation. The load disturbance rejection controller is derived by proposing the desired closed-loop transfer function, and another one for set-point tracking is designed in terms of the H-2 optimal control performance specification. Both controllers can be tuned relatively independently in a monotonic manner, with a single adjustable parameter in each controller. By establishing the sufficient and necessary condition for holding robust stability of the closed-loop control system, tuning constraints are derived together with numerical tuning guidelines for the disturbance rejection controller. Illustrative examples taken from the literature along with temperature control tests for a crystallisation reactor are used to demonstrate the effectiveness and merit of the proposed method.This work was supported in part by the National Thousand Talents Program of China, NSF China Grants 61473054, the Fundamental Research Funds for the Central Universities of China, and the Grants TIN2014-56158-C4-4-P and PROMETEOII/2013/004 from the Spanish and Valencian Governments.Chen, Y.; Liu, T.; García Gil, PJ.; Albertos Pérez, P. (2016). Analytical design of a generalised predictor-based control scheme for low-order integrating and unstable systems with long time delay. IET Control Theory and Applications. 10(8):884-893. https://doi.org/10.1049/iet-cta.2015.0670S88489310

Improved cascade control structure for enhanced performance

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Fractional - order tilt integral derivative controller design using IMC scheme for unstable time - delay processes

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Evolutionary learning and global search for multi-optimal PID tuning rules

With the advances in microprocessor technology, control systems are widely seen not only in industry but now also in household appliances and consumer electronics. Among all control schemes developed so far, Proportional plus Integral plus Derivative (PID) control is the most widely adopted in practice. Today, more than 90% of industrial controllers have a built-in PID function. Their wide applications have stimulated and sustained the research and development of PID tuning techniques, patents, software packages and hardware modules. Due to parameter interaction and format variation, tuning a PID controller is not as straightforward as one would have anticipated. Therefore, designing speedy tuning rules should greatly reduce the burden on new installation and ‘time-to-market’ and should also enhance the competitive advantages of the PID system under offer. A multi-objective evolutionary algorithm (MOEA) would be an ideal candidate to conduct the learning and search for multi-objective PID tuning rules. A simple to implement MOEA, termed s-MOEA, is devised and compared with MOEAs developed elsewhere. Extensive study and analysis are performed on metrics for evaluating MOEA performance, so as to help with this comparison and development. As a result, a novel visualisation technique, termed “Distance and Distribution” (DD)” chart, is developed to overcome some of the limitations of existing metrics and visualisation techniques. The DD chart allows a user to view the comparison of multiple sets of high order non-dominated solutions in a two-dimensional space. The capability of DD chart is shown in the comparison process and it is shown to be a useful tool for gathering more in-depth information of an MOEA which is not possible in existing empirical studies. Truly multi-objective global PID tuning rules are then evolved as a result of interfacing the s-MOEA with closed-loop simulations under practical constraints. It takes into account multiple, and often conflicting, objectives such as steady-state accuracy and transient responsiveness against stability and overshoots, as well as tracking performance against load disturbance rejection. These evolved rules are compared against other tuning rules both offline on a set of well-recognised PID benchmark test systems and online on three laboratory systems of different dynamics and transport delays. The results show that the rules significantly outperform all existing tuning rules, with multi-criterion optimality. This is made possible as the evolved rules can cover a delay to time constant ratio from zero to infinity based on first-order plus delay plant models. For second-order plus delay plant models, they can also cover all possible dynamics found in practice

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Abstract−An analytical design method for PI/PID controller tuning is proposed for several types of processes with time delay. A single tuning formula gives enhanced disturbance rejection performance. The design method is based on the IMC approach, which has a single tuning parameter to adjust the performance and robustness of the controller. A simple tuning formula gives consistently better performance as compared to several well-known methods at the same degree of robustness for stable and integrating process. The performance of the unstable process has been compared with other recently published methods which also show significant improvement in the proposed method. Furthermore, the robustness of the controller is investigated by inserting a perturbation uncertainty in all parameters simultaneously, again showing comparable results with other methods. An analysis has been performed for the uncertainty margin in the different process parameters for the robust controller design. It gives the guidelines of the M s setting for the PI controller design based on the process parameters uncertainty. For the selection of the closed-loop time constant, (τ c ), a guideline is provided over a broad range of θ/τ ratios on the basis of the peak of maximum uncertainty (M s ). A comparison of the IAE has been conducted for the wide range of θ/τ ratio for the first order time delay process. The proposed method shows minimum IAE in compared to SIMC, while Lee et al. shows poor disturbance rejection in the lag dominant process. In the simulation study, the controllers were tuned to have the same degree of robustness by measuring the M s , to obtain a reasonable comparison

Improved control of integrating cascade processes with time delays using fractional - order internal model controller with the Smith predictor

This article proposes an enhanced control of integrating cascade processes. The importance of the scheme shows by controlling a double integrating process with time delay under significant parametric uncertainties and load disturbances. A novel design is proposed based on the Smith predictor principle and uses a fractional-order internal model controller in the outer loop. The required fractional-order tuning parameter follows the desired gain and phase margins. At the same time, another tuning parameter, such as the fractional filter’s time constant, is calculated from the desired performance constraint. Numerical analysis and comparison are performed to showcase the feature of the hybrid structure. The simulation results show that the fractional-order internal model controller–based scheme provides enhanced tracking and faster regulation capabilities and works well under nominal and parameter uncertainty conditions

Design of Low-Order Controllers using Optimization Techniques

In many applications, especially in the process industry, low-level controllers are the workhorses of the automated production lines. The aim of this study has been to provide simple tuning procedures, either optimization-based methods or tuning rules, for design of low-order controllers. The first part of this thesis deals with PID tuning. Design methods or both SISO and MIMO PID controllers based on convex optimization are presented. The methods consist of solving a nonconvex optimization problem by deriving convex approximations of the original problem and solving these iteratively until convergence. The algorithms are fast because of the convex approximations. The controllers obtained minimize low-frequency sensitivity subject to constraints that ensure robustness to process variations and limitations of control signal effort. The second part of this thesis deals with tuning of feedforward controllers. Tuning rules that minimize the integrated-squared-error arising from measurable step disturbances are derived for a controller that can be interpreted as a filtered and possibly time-delayed PD controller. Using a controller structure that decouples the effects of the feedforward and feedback controllers, the controller is optimal both in open and closed loop settings. To improve the high-frequency noise behavior of the feedforward controller, it is proposed that the optimal controller is augmented with a second-order filter. Several aspects on the tuning of this filter are discussed. For systems with PID controllers, the response to step changes in the reference can be improved by introducing set-point weighting. This can be interpreted as feedforward from the reference signal to the control signal. It is shown how these weights can be found by solving a convex optimization problem. Proportional set-point weight that minimizes the integrated-absolute-error was obtained for a batch of over 130 different processes. From these weights, simple tuning rules were derived and the performance was evaluated on all processes in the batch using five different feedback controller tuning methods. The proposed tuning rules could improve the performance by up to 45% with a modest increase in actuation