The Application of a New PID Autotuning Method for the Steam/Water Loop in Large Scale Ships

In large scale ships, the most used controllers for the steam/water loop are still the proportional-integral-derivative (PID) controllers. However, the tuning rules for the PID parameters are based on empirical knowledge and the performance for the loops is not satisfying. In order to improve the control performance of the steam/water loop, the application of a recently developed PID autotuning method is studied. Firstly, a ‘forbidden region’ on the Nyquist plane can be obtained based on user-defined performance requirements such as robustness or gain margin and phase margin. Secondly, the dynamic of the system can be obtained with a sine test around the operation point. Finally, the PID controller’s parameters can be obtained by locating the frequency response of the controlled system at the edge of the ‘forbidden region’. To verify the effectiveness of the new PID autotuning method, comparisons are presented with other PID autotuning methods, as well as the model predictive control. The results show the superiority of the new PID autotuning method.

[1]  Sheng Liu,et al.  Effect of Control Horizon in Model Predictive Control for Steam/Water Loop in Large-Scale Ships , 2018, Processes.

[2]  Robin De Keyser,et al.  A novel auto-tuning method for fractional order PI/PD controllers. , 2016, ISA transactions.

[3]  Tore Hägglund,et al.  Automatic tuning of simple regulators with specifications on phase and amplitude margins , 1984, Autom..

[4]  Dana Copot,et al.  Simple Alternatives to PID-Type Control for Processes with Variable Time-Delay , 2019, Processes.

[5]  Gholamreza Vossoughi,et al.  Multivariable robust adaptive sliding mode control of an industrial boiler-turbine in the presence of modeling imprecisions and external disturbances: A comparison with type-I servo controller. , 2015, ISA transactions.

[6]  Tariq Samad,et al.  A Survey on Industry Impact and Challenges Thereof [Technical Activities] , 2017, IEEE Control Systems.

[7]  Jianhong Lu,et al.  Multivariable Fuzzy PID Control System for Deaerator's and Condenser's Levels in a Thermal Powerunit , 2002, Cybern. Syst..

[8]  Guo Run-qing.  Three-element Drum Water-level Cascade Control System Featuring a Self-disturbance-resistant Controller , 2008 .

[9]  Karl Johan Åström,et al.  Drum-boiler dynamics , 2000, Autom..

[10]  Li Sun,et al.  Multi-objective optimization for advanced superheater steam temperature control in a 300 MW power plant , 2017 .

[11]  P. Olin.  A Mean-Value Model for Estimating Exhaust Manifold Pressure in Production Engine Applications , 2008 .

[12]  A. K Raja,et al.  Power Plant Engineering , 1998 .

[13]  Yuanqing Xia,et al.  Active disturbance rejection control for power plant with a single loop , 2012 .

[14]  Clara-Mihaela Ionescu,et al.  A Theoretical Framework to Determine RHP Zero Dynamics in Sequential Interacting Sub-Systems , 2019, Algorithms.

[15]  Dong Peng,et al.  Decoupling control based on PID neural network for deaerator and condenser water level control system , 2015, 2015 34th Chinese Control Conference (CCC).

[16]  Robin De Keyser,et al.  Nonlinear Predictive Control Applied to Steam/Water Loop in Large Scale Ships , 2019 .

[17]  Zhenlong Wu,et al.  A New PID Controller Design with Constraints on Relative Delay Margin for First-Order Plus Dead-Time Systems , 2019, Processes.

[18]  Guang Yang,et al.  Fuzzy Control of Deaerator Water-Level in Nuclear Power Station Based on MATLAB/Simulink , 2013 .

[19]  Cristina Muresan,et al.  Robust fractional-order auto-tuning for highly-coupled MIMO systems , 2019, Heliyon.

[20]  Le Wei,et al.  Adaptive Backstepping-Based Composite Nonlinear Feedback Water Level Control for the Nuclear U-Tube Steam Generator , 2014, IEEE Transactions on Control Systems Technology.

[21]  Philippe Moulin,et al.  Control of a turbocharged Diesel engine fitted with high pressure and low pressure exhaust gas recirculation systems , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[22]  Ming Su,et al.  Application of adaptive Grey predictor based algorithm to boiler drum level control , 2006 .

[23]  Robin De Keyser,et al.  IMC based PID Control Applied to the Benchmark PID18 , 2018 .

[24]  Jerzy Sasiadek,et al.  ADAPTIVE BACKSTEPPING CONTROL OF A POWER PLANT STATION MODEL , 2002 .

[25]  Xiao Wu,et al.  Fuzzy modeling and predictive control of superheater steam temperature for power plant. , 2015, ISA transactions.

[26]  Chen Laijiu,et al.  MULTIVARIABLE FUZZY PID CONTROL SYSTEM FOR DEAERATOR'S AND CONDENSER'S LEVELS IN A THERMAL POWERUNIT , 2002 .

[27]  Robin De Keyser,et al.  Calibration of UR10 Robot Controller through Simple Auto-Tuning Approach , 2018, Robotics.

[28]  Peng Wang,et al.  Application of PID Neural Network Decoupling Control in Deaerator Pressure and Deaerator Water Level Control System , 2014, AsiaSim.

[29]  Clara M. Ionescu,et al.  Towards a generic optimal co-design of hardware architecture and control configuration for interacting subsystems , 2019, Mechatronics.

[30]  Cristina I. Muresan,et al.  Autotuning of a Robust Fractional Order PID Controller , 2018 .

[31]  Xiaobing Kong,et al.  Nonlinear fuzzy model predictive iterative learning control for drum-type boiler–turbine system , 2013 .

[32]  Hamed Moradi,et al.  Sliding mode control of drum water level in an industrial boiler unit with time varying parameters: A comparison with H∞-robust control approach , 2012 .

[33]  Clara M. Ionescu,et al.  Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study , 2019 .

[34]  Sheng Liu,et al.  A Robust PID Autotuning Method for Steam/Water Loop in Large Scale Ships , 2018 .

[35]  Li Sun,et al.  On Tuning and Practical Implementation of Active Disturbance Rejection Controller: A Case Study from a Regenerative Heater in a 1000 MW Power Plant , 2016 .

[36]  Robin De Keyser,et al.  Distributed Model Predictive Control of Steam/Water Loop in Large Scale Ships , 2019, Processes.

[37]  Ricardo Cajo,et al.  Evaluation of Algorithms for Linear and Nonlinear PID Control for Twin Rotor MIMO System , 2015, 2015 Asia-Pacific Conference on Computer Aided System Engineering.

[38]  Clara M. Ionescu,et al.  Universal Direct Tuner for Loop Control in Industry , 2019, IEEE Access.

[39]  Johari Halim Shah Osman,et al.  Second-order sliding mode fault-tolerant control of heat recovery steam generator boiler in combined cycle power plants , 2013 .

[40]  Robin De Keyser,et al.  Comparative evaluation of a novel principle for PID autotuning , 2017, 2017 11th Asian Control Conference (ASCC).

[41]  C. W. Chan,et al.  Nonlinear Multivariable Power Plant Coordinate Control by Constrained Predictive Scheme , 2010, IEEE Transactions on Control Systems Technology.

[42]  Robin De Keyser,et al.  A robust PID autotuning method applied to the benchmark PID18 , 2018 .

[43]  Xiangjie Liu,et al.  Economic model predictive control of boiler-turbine system , 2018, Journal of Process Control.

[44]  Dana Copot,et al.  Hands-on MPC tuning for industrial applications , 2019 .