A geometric approach to multivariable control system design of a distillation column

A multivariable control problem of a distillation column is considered, where the object is to maintain two output variables, the compositions of the distillate and the bottom product at some desired values by manipulating the reflux flow rate and the boil-up rate.Based on a linearized model, a geometric approach is applied to the design problem of disturbance rejection control. In other words, a feedback control strategy is desired which enables the complete rejection of the effect of disturbances on both output variables.In obtaining the feedback control, the problem of how many and what state variables are to be measured and fed back has been made clear. In this control strategy, only five state variables are fed back. Thus, only five columns of the feedback gain matrix have non-zero values. Furthermore, two out of these five columns are uniquely determined, and the other three columns can be assigned arbitrary values and used for pole assignment of the controlled system.For the disturbances in composition and flow rate of the feed stream, Δx_F and ΔL_F, the effect of the disturbance Δx_F is completely rejected by the feedback controller, but the effect of the disturbance ΔL_F can only be eliminated from the output Δx_D.A digital simulation of a distillation column composed of nine plates, a condenser and a reboiler was carried out to confirm these results and to show that the linearized model used in this paper is valid for fairly large step changes.     

Object-oriented approach applied to ANFIS modeling and control of a distillation column

Neurofuzzy networks are hybrid systems that combine neural networks with fuzzy systems, and the Adaptive Neuro-Fuzzy inference system (ANFIS) is a particular case in which a fuzzy system is implemented in the framework of an adaptive neural network. This neurofuzzy approach represents an effective structure to the modeling of plant dynamics, and the oriented-object programming environments offer an intuitive way to address this task. In this paper the MODELICA object-oriented environment has been applied to the ANFIS modeling and indirect control of the heavy and light product composition in a binary methanol-water distillation column by using the adaptive Levenberg–Marquardt approach. The results obtained demonstrate the potential of the adaptive ANFIS scheme under MODELICA for the dual control of composition both for changes in set points with null stationary error even when disturbances are present.     

Disturbance observer-based tracking control with prescribed performance specifications for a class of nonlinear systems subject to mismatched disturbances

This work investigates simultaneous prescribed performance tracking control and mismatched disturbance rejection problems for a class of strict-feedback nonlinear systems. A novel control scheme combining prescribed performance control, disturbance observer technique, and backstepping method is proposed. The disturbance estimations are introduced into the design of virtual control law design in each step to compensate the mismatched disturbances. To further improve the control performance, a prescribed performance function characterizing the error convergence rate, maximum overshoot, and steady-state error is used to construct the composite controller. The proposed controller guarantees transient and steady-state performance specifications of tracking error and provides much better disturbance attenuation ability simultaneously. Rigorous stability analysis for the closed-loop system is established by direct Lyapunov function method. It is shown that all the states in the resulting closed-loop system are stable, and the tracking error evolves within the prescribed performance boundaries and asymptotically converges to zero even in the presence of mismatched external disturbances. Finally, theoretical results are illustrated and demonstrated by two simulation examples.     

A computer-aided design tool for robustness analysis and control-relevant identification of Horizon Predictive Control with application to a binary distillation column

This paper describes a MATLAB-based computer-aided design tool, IRA-HPC, which accomplishes integrated system identification and robustness analysis for Horizon Predictive Control (HPC), a model predictive control algorithm implemented on the Application Module of the Honeywell TDC 3000 distributed control system. The tool addresses lifecycle as well as functional aspects of the technology, with the goal of making advanced control principles more accessible to the practising control engineer. IRA-HPC systematically performs the various stages of system identification in a control-relevant framework (addressing input design, parameter estimation, and model validation from the standpoint of the final purpose of the model, which is control system design), followed by robust HPC controller tuning using the Structured Singular Value (μ) paradigm as a basis. The benefits of the tool are shown experimentally in the modelling and control of a methanol/isopropanol pilot-scale distillation column, interfaced to an industrial-scale real-time computing testbed. The example demonstrates the practical feasibility of this tool and its benefits in terms of simplifying the choices of design variables in integrated identification and control design.     

确保鲁棒性能的PID控制器参数域的一种确定方法

研究了将混合灵敏度的H∞范数作为PID控制器的鲁棒设计指标时,控制器参数的可行域确定问题．首先将混合灵敏度的H∞范数要求转化为一族复系数多项式的稳定性要求,再加上基本闭环特征多项式的稳定性,通过求解这些含参数多项式的稳定性,解出参数的可行域．在求解多项式稳定性的过程中,采用了推广的Hermite—Biehler定理．     

基于iSIGHT平台DOE方法的螺旋桨敞水性能优化设计

传统的螺旋桨设计方法已经满足不了进一步提升其性能的要求,并且现代环境的变化不再仅仅要求螺旋桨某一性能的最优,而是多方面综合性能的最优.iSIGHT多学科优化设计平台提供了完整的设计综合环境和先进的优化设计方法,能够完成设计过程的自动化和智能的设计探索,确定最佳设计参数.基于iSIGHT平台的实验设计方法建立的螺旋桨敞水性能优化方法使螺旋桨效率和最小压力系数都有提高,实现了优化目的.     

Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms

While optimization studies focusing on real-world buildings are somewhat limited, many building optimization studies to date have used simple hypothetical buildings for the following three reasons: (1) the shape and form of real buildings are complex and difficult to mathematically describe; (2) computer models built based on real buildings are computationally expensive, which makes the optimization process time-consuming and impractical and (3) although algorithm performance is crucial for achieving effective building performance optimization (BPO), there is a lack of agreement regarding the proper selection of optimization algorithms and algorithm control parameters. This study applied BPO to the design of a newly built complex building. A number of design variables, including the shape of the building’s eaves, were optimized to improve building energy efficiency and indoor thermal comfort. Instead of using a detailed simulation model, a surrogate model developed by an artificial neural network (ANN) was used to reduce the computing time. In this study, the performance of four multi-objective algorithms was evaluated by using the proposed performance evaluation criteria to select the best algorithm and parameter values for population size and number of generations. The performance evaluation results of the algorithms implied that NSGA-II (with a population size and number of generations of 40 and 45, respectively) performed the best in the case study. The final optimal solution significantly improves building performance, demonstrating the success of the BPO technique in solving complex building design problems. In addition, the findings on the performance evaluation of the algorithms provide guidance for users regarding the selection of suitable algorithms and parameter settings based on the most important performance criteria.