Adaptive regulation of super-heated steam temperature: a case study in an industrial boiler

This paper describes the application of the MUSMAR predictive adaptive controller to the regulation of super heated steam temperature in a commercial boiler. The boiler considered produces 150 t/h of steam at maximum load, used both for electric energy production in a turbine and industrial use. The combination of predictive and adaptive techniques, relying on multiple models redundantly estimated, allows a continuous adjustment of the controller tuning for tracking plant dynamics variations. This paper describes experiments actually performed on the plant with adaptive predictive control, in particular in the presence of load changes. A reduction of steam temperature fluctuations with respect to an optimized cascade of PI controllers is observed.     

Closing the gap between planning and control: A multiscale MPC cascade approach

A one-to-many, multiscale model predictive control (MPC) cascade is proposed for closing the gap between production planning and process control. The gap originates from the fact that planning and control use models at different scales, and the gap has existed since the first planning tool was deployed. Multiscaleness has been at the core of the challenge to coordinating heterogeneous solution layers, and there has been a lack of systematic treatment for multiscaleness in a control system. The proposed MPC cascade is devised as a plantwide master MPC controller cascading on top of multiple (n) slave MPC controllers.¹ The master can use a coarse-scale, single-period planning model as the gain matrix of its dynamic model, and it then can control the same set of variables that are only monitored by the planning tool. Each slave controller, using a fine-scale model, performs two functions: (1) model predictive control for a process unit, and (2) computation of proxy limits that represent the current constraints inside the slave. The master's economic optimizer amends the single-period planning optimization in real time with the slave's proxy limits, and the embedded planning model is thus reconciled with the MPC models for process units in the sense that the master's optimal solution now honors the slave's constraints. With this new approach, the proposed MPC cascade becomes the plantwide closed-loop control system that performs the reconciled planning optimization in its master controller and carries out the just-in-time production plan through its slave controllers.     

Decentralized model predictive control of a multi-evaporator air conditioning system

Temperature control of multiple zones with a multi-evaporator vapor compression system is a common problem in modern air conditioning. Due to the coupled system dynamics, standard decoupled controllers can interfere with each unit′s performance. This paper proposes an architecture that is decentralized and modular, avoiding competing controllers and the practical difficulty of implementing a centralized controller. A model predictive control (MPC) supervisor calculates evaporator cooling and pressure setpoints for each zone, balancing temperature regulation with energy efficiency; these setpoints are tracked by local level controllers, which rely upon MPC's ability to respect constraints in order to maintain safe, efficient operation.     

Welding current and arc voltage control in a GMAW process using ARMarkov based MPC

A predictive functional controller based on ARMarkov model structure has been designed to control welding current and arc voltage in a GMAW process. The closed loop system performance is investigated through computer simulations and is compared by those achieved from implementing two commonly used controllers i.e. PI and feedback linearization based PID. The local stability of the closed loop system is analyzed in the presence of uncertainties in the linearized model of the process as well as the control parameters. Finally it is shown that the proposed controller performs like a PI controller along with a pre-filter compensator.     

Performance evaluation of two industrial MPC controllers

This paper presents case studies of the performance evaluation of two industrial multivariate model predictive control (MPC) based controllers at the Mitsubishi chemical complex in Mizushima, Japan: (1) a 6-output, 6-input para-xylene (PX) production process with six measured disturbance variables that are used for feedforward control; and (2) a multivariate MPC controller for a 6-output, 5-input poly-propylene splitter column with two measured disturbances. A generalized predictive controller-based MPC algorithm has been implemented on the PX process. Data from the PX unit before and after the MPC implementation are analyzed to obtain and compare several different measures of multivariate controller performance. The second case study is concerned with performance assessment of a commercial MPC controller on a propylene splitter. A discussion on the diagnosis of poor performance for the second MPC application suggests significant model-plant-mismatch under varying load conditions and highlights the role of constraints.     

Control system design and input shape for orientation of spherical wheel motor

This paper presents control system design of a multi degrees-of-freedom (DOF) spherical wheel motor (SWM) in a class of ball-joint-like direct drive actuators to control orientation of the shaft. Three controllers (model based open-loop (OL), two closed-loop (CL) controllers) based on a push-pull torque model have been developed from rotor dynamics and magnetic field model referred to here as Distributed Multipole (DMP) model which provides accurate torque computation. The model based OL controller along with three control input shapes has been examined for the inclination control. Their results offer physical intuition, practical effectiveness, and also demonstrate the accuracy of magnetic field and torque computation. Then, two feedback controllers, a PD controller with and without the observer, have been developed for regulating its rotor inclination and experimentally evaluated against the OL controller. Finally, the performance on each controller has been compared to show the effect of the controllers on transient response. The experimental results verify control system design and demonstrate the motion capability of the SWM. While the experimental results illustrate the ability to control, they also reveal constraints and limitations of the controllers and provide insights for future design of control systems for the SWM.     

MPC-based dual control with online experiment design

We present two dual control approaches to the model maintenance problem based on adaptive model predictive control (mpc). The controllers employ systematic self-excitation and design experiments that are performed under normal operation, resulting in improved control performance with smaller output variance and less control effort. Our control formulations offer a novel approach to the question of how to excite the plant input to generate informative data within the context of mpc and adaptive control. One controller actively tries to reduce the parameter-estimate error covariances; the other controller maximizes the information in the signals for enhanced learning. Our approach differs from existing ones in that we let our controllers converge to standard certainty equivalence (ce) mpc when the parameter uncertainty decreases or more information is generated, and as a result we avoid plant excitation when the uncertainty is low or enough information has been generated. We demonstrate that the controllers work well with a large number of tuning configurations and also address the issue of models that are not admissible for control design.     

Model predictive control of resonant systems using Kautz model

The scope of this paper broadly spans in two areas: system identification of resonant system and design of an efficient control scheme suitable for resonant systems. Use of filters based on orthogonal basis functions (OBF) have been advocated for modelling of resonant process. Kautz filter has been identified as best suited OBF for this purpose. A state space based system identification technique using Kautz filters, viz. Kautz model, has been demonstrated. Model based controllers are believed to be more efficient than classical controllers because explicit use of process model is essential with these modelling techniques. Extensive literature search concludes that very few reports are available which explore use of the model based control studies on resonant system. Two such model based controllers are considered in this work, viz. model predictive controller and internal model controller. A model predictive control algorithm has been developed using the Kautz model. The efficacy of the model and the controller has been verified by two case studies, viz. linear second order underdamped process and a mildly nonlinear magnetic ball suspension system. Comparative assessment of performances of these controllers in those case studies have been carried out.     

Simultaneous closed-loop tuning of cascade controllers based directly on set-point step-response data

This study presents a novel closed-loop tuning method for cascade control systems, in which both primary and secondary controllers are tuned simultaneously by directly using set-point step-response data without resorting to process models. The tuning method can be applied on-line to improve the performance of existing underperforming cascade controllers by retuning controller parameters, using routine operating data. The goal of the proposed design is to obtain the parameters of two proportional-integral-derivative (PID)-type controllers, so that the resulting inner and outer loops behave as similarly as possible to the appropriately specified reference models. The tuning rule and optimization problem related to the proposed design are derived. Based on the rationale behind cascade control, the secondary controller is designed based on disturbance rejection to quickly attenuate disturbances. The primary controller is designed to accurately account for the inner-loop dynamics, without requiring an additional test. In addition, robustness considerations are included in the proposed tuning method, which enable the designer to explicitly address the trade-off between performance and robustness for inner and outer loops independently. Simulation examples show that the proposed method exhibits superior control performance compared with the previous (model-based) tuning methods, confirming the effectiveness of this novel tuning method for cascade control systems.     

Enhanced design of cascade control systems for unstable processes with time delay

In this paper, optimal H₂ internal model controller (IMC) is designed for control of unstable cascade processes with time delays. The proposed control structure consists of two controllers in which inner loop controller (secondary controller) is designed using IMC principles. The primary controller (master controller) is designed as a proportional-integral-derivative (PID) in series with a lead-lag filter based on IMC scheme using optimal H₂ minimisation. Selection of tuning parameter is important in any IMC based design and in the present work, maximum sensitivity is used for systematic selection of the primary loop tuning parameter. Simulation studies have been carried out on various unstable cascade processes. The present method provides significant improvement when compared to the recently reported methods in the literature particularly for disturbance rejection. The present method also provides robust closed loop performances for large uncertainties in the process parameters. Quantitative comparison has been carried out by considering integral of absolute error (IAE) and total variation (TV) as performance indices.     

Simulation and control of hybrid electric vehicles

This research develops a typical model for a parallel hybrid electric vehicle. Model predictive controllers and simulations for this model have been built to verify the ability of the system to control the speeds and to handle the transitional period for the clutch engagement from the pure electrical driving to the hybrid driving. If the output constraints are the measured speeds and the unmeasured torques which are not strictly imposed and can be violated somewhat during the clutch engagements, a modified model predictive controller with soften output constraints has been developed. Simulations show that the new model predictive controller can control the speeds very well for rapid clutch engagements, which enhance the driving comfort and reduce the jerk on the parallel hybrid electric vehicles.     

Cascade generalized predictive controller: two in one

The paper presents a cascade generalized predictive controller. The cascade control task is performed by one predictive controller and the cascade feature is incorporated in a special predictor. Simulation results are presented comparing the performances of the proposed control algorithm to traditional cascade loops including two PI or two GPC controllers. The paper investigates the effects of noise filter on the robustness of the control loops in the cascade control structure. It shows, that with the proposed predictor it is possible to adjust independently the robustness of the inner and outer loops, meanwhile in the traditional cascade loop there are cross effects in this sense. Finally a real time application of the proposed algorithm is presented: the cascade GPC was tested in the oxygen control loop of an experimental fluidized bed boiler.     

Multi-objective constraint optimizing IOL control of distillation column with nonlinear observer

Performance of input–output linearizing (IOL) controllers suffers due to constraints on input and output variables. This problem is successfully tackled by augmenting IOL controllers with quadratic dynamic matrix controller (QDMC). However, this has created a constraint-mapping problem for coupled MIMO systems like distillation column. A multi-objective optimization problem needs to be solved to map the constraints on inputs. A suitable transformation technique is proposed to convert this multi-objective optimization problem to a single objective one. This makes the controller less computationally intensive and easy to implement. This controller (IOL-QDMC) along with nonlinear observer is implemented on a binary distillation column for dual composition control. Its performance is evaluated against a quadratic dynamic matrix controller (QDMC) and input–output linearization with PI controller (IOL-PI).     

深海自持式智能浮标双闭环模糊PID定深控制

针对深海自持式智能浮标运动模型非线性、强耦合性的特点,提出了一种基于双闭环反馈回路的模糊比例-积分-微分（proportion-integral-derivative,PID）定深控制器.根据浮标的浮力调节机构,分析了浮标的运动过程,建立了非线性运动方程.针对外环深度反馈回路,设计了模糊控制器.基于内环速度反馈回路与模糊控制器,设计了联级模糊PID定深控制器.传统PID定深控制器超调量5.6%,最终在目标深度±30 m范围内震荡,而双闭环模糊PID定深控制器在相同的上升时间内,超调量2.0%,深度误差控制在1.0%以内.存在外界扰动的情况下,通过双闭环模糊PID定深控制器的调节,浮标仍可以稳定在目标深度内.仿真结果表明,所建立的双闭环模糊PID定深控制系统具有良好的控制效果和稳定性.     

Model analysis and performance analysis of two industrial MPCs

This paper presents two case studies on the performance evaluation and model validation of two industrial multivariate model predictive control (MPC) based controllers: (1) a 7-output, 3-input MPC with three measured disturbance variables for controlling a part of kerosene hydrotreating unit (KHU) and (2) a 8-output, 4-input MPC with five measured disturbances for controlling a part of naphtha hydrotreating unit (NHU). The first case study focuses on potential limits to control performance due to constraints and limits set at the time of controller commissioning. The root causes of sub-optimal performance of KHU are successfully isolated. Data from the NHU unit with MPC ‘on’ and with MPC ‘off’ are analyzed to obtain and compare several different measures of multivariate controller performance. Model quality assessment for the two MPCs are performed. A new model index is proposed to have a measure of simulation ability and prediction ability of a model. Closed-loop identification of KHU and closed-loop identification of NHU are conducted using the asymptotic method (ASYM) proposed by Zhu (1998).     

The Mimo Predictive Pid Controller Design

This paper is concerned with the design of Multi‐Inputs and Multi‐Outputs (MIMO) predictive PID controllers, which have similar performance to that obtainable from model‐based predictive controllers. A new PID control structure is defined which incorporates the prediction of future outputs and uses future set point. A method is proposed to calculate the optimal values of the PID gains from generalised predictive control results. A decentralized version of the predictive PID controllers is presented and the stability of the closed loop system is studied. Simulation studies demonstrate the superior performance of the proposed controller compared with a conventional PID controller. The results are also compared with generalised predictive control solutions.     

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method.     

Predictive controller with estimation of measurable disturbances. Application to an olive oil mill

This paper describes the application of a predictive controller that deals with measurable disturbances in the extraction process in an olive oil mill. The work focuses on the thermal part of the process, where the raw material is prepared for the mechanical separation. The system under consideration can be viewed as composed of several changing-level stirred tanks. The paper shows the development of the controller based upon a model obtained from first principles combined with experimental results and validated with real data. Strong disturbances and large time delays appear in the process, so predictive control strategies have been tested under linear and nonlinear simulation. Finally, they have been implemented on the real plant. A study about the consideration of different models for the estimation of measurable disturbances along the prediction horizon has been carried out, showing that a good performance can be obtained by the use of an appropriate model. A new idea that can improve periodic disturbance rejection in Model Based Predictive controllers is presented.     

高超声速飞行器的调度离线预测控制

本文将调度预测控制的思想应用于离线鲁棒预测控制,设计了高超声速飞行器计算有效的调度离线预测控制器.首先在不同的平衡点离线设计一系列控制规则,实际实施时只需要在不同的控制器之间进行切换,避免进行在线优化,大幅度减少了在线计算时间.通过估计局部控制器的稳定域,保证了切换控制器的稳定性.另外在确保良好控制品质的同时,还能够保证所有输入和状态均在给定约束范围.仿真试验表明,提出的方法能实现速度和高度较大范围的指令跟踪,所有输入和状态均在给定约束范围内;相比于在线鲁棒预测控制方法,仿真运行时间减少,可以实现高超声速飞行器的实时控制.     

A simple output-feedback strategy for the control of perfused mammalian cell cultures

This paper presents a framework for the multivariable robust control of perfusion animal cell cultures. It consists of a cascade control structure and an estimation algorithm, which provides the unmeasurable variables needed in the design of the control law, and ensures the regulation of the cell and glucose concentrations at imposed levels by manipulating the bleed and the dilution rates. The cascade control structure uses a feedback linearizing controller in the inner loop and linear (PI) controllers in the outer loops, and requires the measurement of the cell concentration and the glucose concentration in the bioreactor. Two approaches are provided: the first one assumes the availability of an approximate model of the process kinetics and uses an extended Kalman filter (EKF) to estimate the system states; the second approach does not require the prior knowledge of the process kinetics. These are estimated from the available measurements using sliding mode observers (SMO). A receding horizon optimization algorithm is employed to (periodically) tune the gains of the outer loop controllers. The proposed framework is easy to implement and tune, and may be applied to a general class of perfusion cell culture systems. Its effectiveness and robustness are illustrated by means of simulation results.