CLOSED-LOOP IDENTIFICATION FOR PARAMETRIC MODELS USING FREQUENCY RESPONSE TECHNIQUES

A parametric identification technique via closed-loop testing using frequency response techniques is presented in this study. Under an operation of proportional control, a single dynamic test (such as an arbitrary pulse or step change) in the set point was introduced to the system. The closed-loop transients could numerically be translated into frequency response data by Fourier integral transforms, and the parameters of a given process model were then be obtained by a least-squares fit in the frequency domain based on Parseval's theorem. This identification technique could apply not only for self-regulating processes but also for integrating or open-loop unstable processes. Furthermore, the proposed technique was also extended to the controller settings for the feedforward-feedback control system. Simulation results demonstrated that the proposed technique could yield good model parameters and can be applied in many different types of processes.     

Tuning digital PI controllers for minimal variance in manipulated input moves applied to imbalanced systems with delay

Determining the discrete-time proportional plus integral (PI) controller tuning parameters to achieve the smallest possible variance in the manipulated input moves, for a given variance in the controlled output, is the subject of this article. Previous researchers have developed tuning rules for PI and PI permutated nonlinear controllers to achieve what is commonly referred to as “level-flow smoothing”, or “averaging level control”, on imbalanced or integrating processes with delay, such as liquid level and gas pressure systems. The intent of this note is to demonstrate a new and simple technique of tuning digital PI controllers which utilizes either open or closed-loop historical data to estimate the process gain, dead-time and expected flow disturbance magnitude from which the digital PI tuning constants can be easily derived. By parameterizing the closed-loop system as a function of the PI tuning constants, we can simultaneously minimize the expected variation in the process input move and output responses while at the same time ensuring nominal stability of the overall system. In order to demonstrate the technique, an illustrative example is included which highlights the new procedure on an oil refinery liquid surge drum level process.     

System identification and PID controller tuning using band-pass filters

An online recursive system identification technique using band-pass filters has been developed to estimate the continuous-time frequency response of any process control system at a number of discrete frequencies. Tuning of PID controllers employs a gradient optimization technique, based on the estimated discrete frequency response of the process rather than a transfer function model of the process. The performances of the identification technique and controller tuning algorithm are demonstrated by a simulation and by experimental results on a distillation column composition control.     

Generic model controller tuning for chemical processes with input saturation

Control in the face of process input constraints is very common and of great practical importance in the processing industries. Generic Model Control (GMC) is a model‐based control framework for both linear and nonlinear systems. In this paper, a constrained GMC controller tuning approach using a nonlinear least squares technique is proposed. This tuning approach is simple to apply. For a SISO GMC control system with input saturation, the tracking performance is significantly improved by adding a simple heuristic switching strategy. The effectiveness of the proposed controller tuning approach is demonstrated using dynamic simulations and MIMO real‐time experiments.     

Closed-loop tuning of process control systems

A technique for tuning PID controllers, involving a single dynamic test (such as a step or pulse change in set point) implemented during closed-loop operation, is proposed. The resulting transient data are used to predict the frequency response of the open-loop process which in turn is used to compute the optimum controller settings. Simulation results reveal that this technique provides reliable tuning constants even when such practical problems as process noise and unanticipated load upsets arise during implementation. A comparison with the recently proposed tuning alternative (Yuwana and Seborg, A.I.Ch.E. J.28, 434, 1982; Jutan and Rodriguez, Can. J. Chem. Engng62, 802, 1984) confirms that in general the suggested procedure, apart from being more flexible, yields also relatively better results.     

OPTIMAL TUNING OF PID CONTROLLERS FOR SINGLE AND CASCADE CONTROL LOOPS

Design of one parameter tuning of three-mode PID controller was developed in this present study. The integral time and the derivative time of the controller were expressed in terms of the time constant and dead time of the process. Only the proportional gain was observed to be dependent on the implemented tunable parameter in which the stable region could be predetermined by the Routh test. Extension of the concept towards designing cascade PID controllers was straightforward such that only two parameters for the inner and outer PID controllers required to be tuned, respectively. The optimal tuning correlative formulas of the proportional gain for single and cascade control systems were obtained by the least square regression method.     

ADAPTIVE DOMINANT POLE DESIGN OF PID CONTROLLERS BASED ON A SINGLE CLOSED-LOOP TEST

A novel method, based on a single experimental test under proportional feedback, has been developed to tune PID controllers on-line. The tuning method involving an identification scheme and a dominant pole design technique is ideal for automatic tuning. It also provides an adaptive algorithm to adjust the controller settings to achieve the desirable control performance satisfying the prescribed decay ratio and stability margin. A simulation study demonstrates that the method is valid for processes with large dead-times as well as open-loop underdamped processes.     

A tuning of the nonlinear PI controller and its experimental application

Many industrial chemical process control systems consist of conventional PID and nonlinear controllers, even though many advanced control strategies have been proposed. In addition, nonlinear control methods are widely used even for linear processes to achieve better control performance compared with linear PID controllers. However, there are few tuning methods for these nonlinear controllers. In this work, we suggest new controller tuning methods for the error square type of nonlinear PI controller. These control methods can be applied to a large number of linear and nonlinear processes without changing control structures. We also propose new tuning rules for integrating processes. In addition, we suggest application guidelines for performing the proposed tuning rules at the pilot scale multistage level control system. Finally, in this work we confirmed good control performances of the proposed tuning methods through both simulation studies and experimental studies.     

PARAMETER IDENTIFICATION AND THE SELF-TUNING ADAPTIVE CONTROL BY A SENSITIVITY APPROACH

Parameter deviation identification and optimal controller tuning are derived by the sensitivities of the parameters. There are non-iterative and iterative algorithms which can be used for parameter identification, no matter the data come from an open-loop test or from a closed-loop operation. The controller tuning is based on the parameter sensitivities of an optimal regulator. The same tuning algorithm has been proved satisfactory for the PID control of the first order process with delay. By sequential implementations of both the identification and tuning, a self-tuning adaptive control system can be obtained. Numerical examples show the feasibility of such algorithms.     

PARAMETER IDENTIFICATION AND THE SELF-TUNING ADAPTIVE CONTROL BY A SENSITIVITY APPROACH

Parameter deviation identification and optimal controller tuning are derived by the sensitivities of the parameters. There are non-iterative and iterative algorithms which can be used for parameter identification, no matter the data come from an open-loop test or from a closed-loop operation. The controller tuning is based on the parameter sensitivities of an optimal regulator. The same tuning algorithm has been proved satisfactory for the PID control of the first order process with delay. By sequential implementations of both the identification and tuning, a self-tuning adaptive control system can be obtained. Numerical examples show the feasibility of such algorithms.     

PID Controller Design Based on the Time Domain Information of Robust IMC Controller Using Maximum Sensitivity

The IMC (Internal Model Control) controller based on robust tuning can improve the robustness and dynamic performance of the system. In this paper, the robustness degree of the control system is investigated based on Maximum Sensitivity (Ms) in depth. And the analytical relationship is obtained between the robustness specification and controller parameters, which gives a clear design criterion to robust IMC controller. Moreover, a novel and simple IMC-PID (Proportional-Integral-Derivative) tuning method is proposed by converting the IMC controller to PID form in terms of the time domain rather than the frequency domain adopted in some conventional IMC-based methods. Hence, the presented IMC-PID gives a good performance with a specific robustness degree. The new IMC-PID method is compared with other classical IMC-PID rules, showing the flexibility and feasibility for a wide range of plants.     

Flexible Methane Production Using a Proportional Integral Controller with Simulation-Based Soft Sensor

Anaerobic digestion plants have the potential to produce biogas on demand to help balance renewable energy production and energy demand by consumers. A proportional integral (PI) controller is constructed and tuned with a novel tuning method to control biogas production in an optimal manner. In this approach, the proportional part of the controller is a function of the feeding rate and system's degree of stability. To estimate the degree of stability, a simulation-based soft sensor is developed. By means of the PI controller, the requirement for gas storage capacity of the digester is reduced by approximately 30 % compared to a constant, continuous feeding regime of the digester.     

In‐line viscosity control in an extrusion process with a fuzzy gain scheduled PID controller

In the extrusion process, rapidly tracking the set point of quality factor and eliminating its variation to reduce the off‐specification product is important. In this study, the fuzzy gain‐scheduled proportional‐integral‐derivative (PID) controller is used to control the melt viscosity during extrusion processing. A second‐order model related to the viscosity and the extruder screw speed is developed empirically to approximate the extrusion system. It is concluded that, in comparison to the well‐known Zeigler‐Nichols PID tuning control scheme, the performances of the proposed control strategy is preferable both in simulation and implementation. © 1999 John Wiley & Sons, Inc. J Appl Polym Sci 74: 541–555, 1999     

TUNING PID CONTROLLER FOR OPEN-LOOP UNSTABLE PROCESSES WITH TIME DELAY

A calculation method of PID controller tuning for the first- and the second-order open-loop unstable process models with time delay is presented in this study. Optimum PID controller tuning data based on the models and minimum IAE criterion were obtained via Powell searching technique, and these data were then empirically correlated into several multiple-regression equations by a least-squares method. Thus PID controller tuning based on the models can easily be obtained by the calculation of these correlated equations. Simulation with a reset-feedback PID control algorithm has demonstrated that the proposed tuning method based on the first-order model can provide better results than the latest studies. In addition, simulation has also unveiled that tuning results based on the second-order models are superior to the first-order model for a higher-order process.     

A novel two-dimensional PID controller design using two-dimensional model predictive iterative learning control optimization for batch processes

It is known that the key indicators of batch processes are controlled by conventional proportional–integral–derivative (PID) strategies from the view of one-dimensional (1D) framework. Under such conditions, the information among batches cannot be used sufficiently; meanwhile, the repetitive disturbances also cannot be handled well. In order to deal with such situations, a novel two-dimensional PID controller optimized by two-dimensional model predictive iterative learning control (2D-PID-MPILC) is proposed. The contributions of this paper can be summarized as follows. First, a novel two-dimensional PID (2D-PID) controller is developed by combining the advantages of a PID-type iterative learning control (PIDILC) strategy and the conventional PID method. This novel 2D-PID controller overcomes the aforementioned disadvantages and extends the conventional PID algorithm from one-dimension to two-dimensions. Second, the tuning guidelines of the presented 2D-PID controller are obtained from the two-dimensional model predictive control iterative control (2D-MPILC) method. Thus, the proposed approach inherits the advantages of both PID control, PIDILC strategy, and 2D-MPILC scheme. The superiority of the proposed method is verified by the case study on the injection modelling process.     

Application of a novel approach for DMC predictive controller design by response surface analysis in a fed-batch bioreactor

This work presents a novel approach for tuning a predictive DMC controller implemented in a fed-batch penicillin bioreactor in order to stabilize the dissolved oxygen concentration by agitation speed manipulation. The operating process variables were calculated by a deterministic and non-structured model solved by a fourth order Runge-Kutta-Gill numerical technique with variable steps. The parameters of the model were obtained from experiments and the literature. The estimated parameters of the DMC controller were model, prediction and control horizons, suppression factor and reference trajectory. The tuning approach employed complete factorial design in order to estimate the influence of these parameters on the integral of the absolute error between the controlled variable and the set point. Response surface analysis provided the optimal parameters. This study showed negligible influence of model, prediction and control horizons while the suppression factor and reference trajectory were very important for the controller. Another important feature of the DMC controller was the that the parameters had negligible influence on each other making design of the controller easier. The performance of the DMC controller was evaluated using several delay times and sample periods of the controlled variable. The behavior of this predictive controller was better than a PID controller tuned by the Modified Simplex method.     

Neurocontrol of a multi-effect batch distillation pilot plant based on evolutionary reinforcement learning

The time cost of first-principles dynamic modelling and the complexity of nonlinear control strategies may limit successful implementation of advanced process control. The maximum return on fixed capital within the processing industries is thus compromised. This study introduces a neurocontrol methodology that uses partial system identification and symbiotic memetic neuro-evolution (SMNE) for the development of neurocontrollers. Partial system identification is achieved using singular spectrum analysis (SSA) to extract state variables from time series data. The SMNE algorithm uses a symbiotic evolutionary algorithm and particle swarm optimisation to learn optimal neurocontroller weights from the partially identified system within a reinforcement learning framework. A multi-effect batch distillation (MEBAD) pilot plant was constructed to demonstrate the real world application of the neurocontrol methodology, motivated by the nonsteady state operation and nonlinear process interaction between multiple distillation columns. Multi-loop proportional integral (PI) control was implemented as a reduced model, reflecting an approach involving no modelling or significant controller tuning. Rapid multiple input multiple out nonlinear controller development was achieved using SSA and the SMNE algorithm, demonstrating comparable time and cost to implementation in relation to the reduced model. The optimal neurocontroller reduced the batch time and therefore the energy consumption by 45% compared to conventional multi-loop SISO PI control.     

On-line process identification and PID controller autotuning

We propose a new and simple on-line process identification method for the automatic tuning of the PID controller. It does not require a special type of test signal generators such as relay or P controller only if the signals are persistently exciting. That is, a user can choose arbitrary signal generators such as relay, a P controller, the controller itself, pulse signal and step signal generator because it needs only the measured process output and the controller output. It can incorporate nonlinearities due to actuator saturation or manual mode operation during identification work and shows a good robustness to measurement noises, nonlinearity of the process and disturbances. The proposed autotuner combined with the identification method and tuning rule using a model reduction shows good control properties compared with previous autotuning methods.     

多变量解耦自抗扰控制在气体流量装置中的应用

针对气体流量装置实验管路流量、压力耦合系统,通过机理法和阶跃响应法建立了其数学模型,并利用自抗扰解耦控制算法实现其解耦控制,以保证气体流量计性能测试过程的稳定性和控制快速性。对于气体流量装置多变量系统,自抗扰控制算法将耦合以及所有的内部不确定性和外部扰动都归结到总扰动中,通过扩张状态观测器和控制律对总扰动进行估计和补偿,使原系统被解耦成两个单输入单输出的子系统并利用PD控制器完成控制。自抗扰控制算法使系统在实现解耦的同时既减弱算法对于模型的依赖,又提高了系统的鲁棒性。仿真和实验结果表明,与PID控制算法相比,自抗扰控制算法调节时间更快,解耦效果更好,对扰动的抑制效果更优,性能鲁棒性更强。     

Multi‐controller scheme for load rejection and set‐point tracking

To acheive complete compensation for loads, a novel multi‐controller scheme with feedforward control is proposed. This scheme has four controllers, a set‐point controller, two load controllers, and a feedforward controller. This results in the separation of the load response from the set‐point response in a closed‐loop system. These four controllers can then be designed independently to achieve good system performance for both set‐point tracking and load rejection. One of the load controllers can be chosen as a proportional controller; this guarantees physical realizability and provides excellent compensation. The results of simulation and real time control show that the proposed multi‐controller scheme is superior to a double‐controller system and a Smith predictor in the presence of large uncertainty in process dynamics especially for load disturbances.