Simultaneous design and control of processes under uncertainty: A robust modelling approach

This paper presents a new methodology to integrate process design and control. The key idea in this method is to represent the system’s closed-loop nonlinear behaviour as a linear state space model complemented with uncertain model parameters. Then, robust control tools are applied to calculate bounds on the process stability, the process feasibility and the worst-case scenario. The new methodology was applied to the simultaneous design and control of a mixing tank process. The resulting design avoids the solution of computationally intensive dynamic optimizations since the integration of design and control problem is reduced to a nonlinear constrained optimization problem.     

Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results

In this paper, the development of data-driven design of process monitoring and fault diagnosis (PM-FD) systems is reviewed and some recent results are presented. A major objective of this work is to sketch a process input–output data based framework of designing PM-FD systems for dynamic processes. The main focus of our study is on the data-driven design of observer-based PM-FD systems, which are, thanks to their high robustness and real-time ability, suitable for industrial applications.     

The scenario approach for systems and control design

The ‘scenario approach’ is an innovative technology that has been introduced to solve convex optimization problems with an infinite number of constraints, a class of problems which often occurs when dealing with uncertainty. This technology relies on random sampling of constraints, and provides a powerful means for solving a variety of design problems in systems and control. The objective of this paper is to illustrate the scenario approach at a tutorial level, focusing mainly on algorithmic aspects. Its versatility and virtues will be pointed out through a number of examples in model reduction, robust and optimal control.     

Nonlinear system identification and control of chemical processes using fast orthogonal search

The use of the fast orthogonal search (FOS) method is presented for model estimation and control of nonlinear chemical processes. FOS provides a nonlinear approximation used in an inner-loop that allows for simpler linear control methods to be used as an outer-loop controller. It is a straightforward, simple-to-use method for linearization of systems based on orthogonal system identification. The control concept is derived from the method of inverse dynamic control (IDC). The novel combination of this with the FOS method of system identification results in a very efficient and effective method of control. The method is demonstrated and tested on two nonlinear chemical process control simulations and the results are shown to compare very favourably to published results on the same problems.     

化工过程工艺与控制系统集成设计研究进展

传统的序贯设计法将过程工艺设计与控制系统的设计分割成两个独立的部分进行设计,其忽略了工艺设计与过程动态性能之间的内在联系,因而无法得到整体性能最优的过程系统。过程工艺与控制系统集成设计方法相较于传统的分步序贯设计方法能获得更优的设计参数和更好的控制效果。近年来,该领域取得了大量研究成果。本文从两方面对近年来该领域的研究进展进行综述。首先,根据过程工艺与控制集成设计在求解方法上的不同,从基于可控性指标的方法、动态规划方法、鲁棒方法、嵌入式控制优化方法等方面进行综述,并详细讨论各种方法优缺点。其次,从文献中采取不同控制策略的角度,对该领域的研究进展进行综述。最后对过程工艺与控制系统集成优化设计方法进行了总结和展望。     

Quantitative feedback design of air and boost pressure control system for turbocharged diesel engines

For modern diesel engines, variable geometry turbocharger (VGT) is used to boost engine power output. In addition, exhaust gas recirculation (EGR) is utilized to reduce engine out NO_x emission. To realize these functions, a multivariable control system needs to control both VGT and EGR valve to deliver desired intake manifold (or boost) pressure, and desired EGR flow rate. This two-input and two-output system is nonlinear with cross-couplings between the boost and EGR responses to the input actuators, the system parameters are varying with different engine operating conditions. This paper proposes a closed loop design of a multivariable VGT/EGR control system for a turbocharged diesel engine. The control system is synthesized based on quantitative feedback theory to maintain robust stability and performance via sequential MIMO loop shaping in the frequency domain. Experiment results are included from a turbocharged diesel engine to show the effectiveness of the proposed control design.     

Robust controller design for a class of nonlinear uncertain chemical processes

This article considers the issue of designing robust controllers for single-input/single-output nonlinear chemical processes whose uncertainties satisfy the so-called generalized matching condition. The nominal system (mathematical model) is assumed to be input–output linearizable and the only assumption on uncertainties is that they are bounded. A design methodology of combining the techniques of the differential geometric feedback linearization, the sliding mode control strategy and the adaptive state feedback is presented. Based on the nominal system and the related bounds of uncertainties, a hybrid nonlinear controller, which is more practicable and easily implemented than many other existing ones in the literature, is proposed. A Lyapunov-based approach is utilized to guarantee the robust stability and behavior of the closed-loop system. For demonstrating the effectiveness and applicability of the proposed scheme, we applied it to the control of a continuously stirred tank reactor (CSTR) in the presence of uncertainties including unmodeled side reaction, measuring error, and/or extra unmeasured disturbances. The potential use of a sliding observer along with the proposed scheme is also investigated in this work. Extensive simulation results reveal that the proposed scheme appears to be a practical and promising approach to the robust control of nonlinear uncertain chemical processes.     

Advanced methods of process computer control for industrial processes

In recent years, the requirements for the performance of multilevel process control, including feedforward and feedback control, monitoring and optimization have increased. Applying process computers and micro computers, the functions of analog equipment and hardwired logic devices cannot only be replaced. Extended or quite new methods can be realized improving the performance of multilevel process control. These advanced methods for process control are characterized by: more sophisticated, better adjusted control algorithms, forecasting of process variables, estimation of not directly measurable variables, computer aided design of algorithms and adaptive or selftuning algorithms. The basis of these advanced methods are mathematical models of the processes and their signals, often gained by the process computer itself during on-line operation.The present paper discusses first how process models in open and closed loop can be obtained by on-line identification methods. Then, based on these models, the computer aided design of control algorithms, adaptive control algorithms and adaptive steady-state on-line optimization will be regarded. Monitoring of not direct measurable variables will be mentioned. For some methods, practical results with real and simulated processes are shown. Interactive process computer software packages are used which can easily be transferred to other process computers.     

Analytical results for the multi-objective design of model-predictive control

In model-predictive control (MPC), achieving the best closed-loop performance under a given computational capacity is the underlying design consideration. This paper analyzes the MPC tuning problem with control performance and required computational capacity as competing design objectives. The proposed multi-objective design of MPC (MOD-MPC) approach extends current methods that treat control performance and the computational capacity separately – often with the latter as a fixed constraint – which requires the implementation hardware to be known a priori. The proposed approach focuses on the tuning of structural MPC parameters, namely sampling time and prediction horizon length, to produce a set of optimal choices available to the practitioner. The posed design problem is then analyzed to reveal key properties, including smoothness of the design objectives and parameter bounds, and establish certain validated guarantees. Founded on these properties, necessary and sufficient conditions for an effective and efficient optimizer are presented, leading to a specialized multi-objective optimizer for the MOD-MPC being proposed. Finally, two real-world control problems are used to illustrate the results of the tuning approach and importance of the developed conditions for an effective optimizer of the MOD-MPC problem.     

Interactions between control and process design under economic model predictive control

Economic model predictive control (EMPC) is a model-based control scheme that integrates process control and economic optimization, which can potentially allow for time-varying operating policies to maximize economic performance. The manner in which an EMPC operates a process to optimize economics depends on the process dynamics, which are fixed by the process design. This raises the question of how process and EMPC designs interact. Works which have addressed process and control design interactions for steady-state operation have sought to simultaneously develop process designs and control law parameters to find the most profitable way to operate a process that is able to prevent process constraints from being violated and to optimize capital costs in the presence of disturbances. Because EMPC has the potential to operate a process in a transient fashion, this work first focuses on how EMPC and process design interact in the absence of disturbances. Using small-scale process examples, we seek to understand the fundamental nature of the interactions between EMPC and process design, including how these interactions can impact computational complexity of the controller and the design procedure. We subsequently utilize the insights gained to suggest controller design variables which might be considered as decision variables for a simultaneous process and control design problem when disturbances are considered.     

A sliding mode control scheme for non-minimum phase non-linear uncertain input-delay chemical processes

This paper considers the robust control of non-linear uncertain chemical processes in the presence of input-delay and inverse response. A novel and systematic sliding mode control (SMC) scheme, which integrates a time-advanced non-linear predictor and a statically equivalent output map (SEOM), is proposed to compensate for the process’s input-delay and to circumvent the negative effect of inverse response. A Lyapunov-based approach is utilized to ensure the robust control performance of the proposed SMC system. To demonstrate the effectiveness and applicability of the SMC control strategy, we applied it to the regulation control of a Van de Vusse reactor in the presence of input-delay, non-minimum phase behavior, and diversified uncertainties such as unmodeled side reaction, measuring error, parameter uncertainties, and/or extra unmeasured disturbances. The potential use of a sliding observer along with the proposed scheme is also investigated in this work. Extensive simulation results reveal that the proposed SMC design methodology is applicable and promising for the robust control of non-linear, uncertain, non-minimum phase, time-delay chemical processes.     

Analyses of cognitive processes in train traffic control

The study of complex information processing and problem-solving situations in practice is discussed, in particular the methods of analysing the cognitive aspects in train dispatcher's work. Some parallels are drawn with related work in processing industries. A prerequisite for the approach to the issue was that the results would have to yield very concrete starting-points for the design of technical aids. The research started from the assumption that it would be extremely difficult to derive design indications from apparently observable actions of the train dispatchers. This led to the choice of a combination of a preliminary simulation study followed by analysis of cognitive processes in the train dispatcher's behaviour in every day practice. It turned out to be helpful for the analysis to draw parallels between the cognitive processes in the behaviour of the train dispatchers and the way of working/thinking shown by test subjects in complex problem-solving experiments.     

New filters for internal model control design

In this paper we study the design of a new two-degree-of-freedom filter for the internal model control (IMC) method. The new filter alleviates some disadvantages of the standard IMC filter when the IMC method is applied to unstable plants that do not have non-minimum-phase zeros. We show that by employing the new filter, the resulting system has a flatter frequency response, better stability robustness, and little overshoot in the step response. Furthermore, one of its design parameters can be related directly to the closed-loop bandwidth and the other parameter can be used to control the recovery time after an overshoot has occurred in the step response. These features are important in the application of the IMC method to a new approach of adaptive robust control. Examples are given in the paper to illustrate the new filter design.     

An improved nonlinear control design for series DC motors

A series DC motor must be represented by a nonlinear model when nonlinearities such as magnetic saturation are considered. To provide effective control, nonlinearities and uncertainties in the model must be taken into account in the control design. In this paper, the recursive design method is applied to generate nonlinear control, nonlinear PI control, and robust control, and these controls are shown to be efficient and robust in the simulation study compared to existing results.     

A tutorial review of economic model predictive control methods

An overview of the recent results on economic model predictive control (EMPC) is presented and discussed addressing both closed-loop stability and performance for nonlinear systems. A chemical process example is used to provide a demonstration of a few of the various approaches. The paper concludes with a brief discussion of the current status of EMPC and future research directions to promote and stimulate further research potential in this area.     

Robust H∞ control design for systems with structured parameter uncertainty

In a recent paper a unification of the H₂ (LQG) and H_∞ control-design problems was obtained in terms of modified algebraic Riccati equations. In the present paper these results are extended to guarantee robust H₂ and H_∞ performance in the presence of structured real-valued parameter variiations (ΔA, ΔB, ΔC) in the state space model. For design flexibility the paper considers two distinct types of uncertainty bounds for both full- and reduced-order dynamic compensation. An important special case of these results generates H₂/H_∞ controller designs with guaranteed gain margins.     

Modelling for control analysis and design in complex industrial separation and liquefaction processes

Dynamic simulation of complex industrial systems is discussed, and a summary is presented of over a decade of work in the modelling, simulation and control of cryogenic separation and liquefaction processes. The work includes not only successful applications but also the development of tools to facilitate the construction of the simulation flowsheets and their effective use in control system analysis and design. The use of these tools and of two commercial dynamic simulation packages is reviewed. The question of what is a required level of modelling detail in dynamic simulation applications is addressed.     

Single screw extrusion control: A comprehensive review and directions for improvements

Polymer extrusion is usually a complex process, particularly due to the coupled nature of process parameters, and hence highly prone to fluctuations. Although a number of different approaches have been attempted in research/industry over the last few decades for extrusion control, it is still experiencing some problems in achieving consistent product quality. Presently, most of the polymer processing extruders are equipped with PID controllers mainly for the control of the screw speed and barrel temperatures in their set limits. It seems that only both of these controllers are commonly used as the major aids of process control to achieve the required melt quality. Although, the quality of the melt output (i.e., a thermally homogeneous melt output which is constant in quantity and quality over the time) is the key variable in polymer extrusion, only a few control techniques are available which make control decisions by observing the actual melt flow quality. Therefore, the development of new control strategies which consider the actual melt quality, perhaps incorporating industrially popular nonlinear techniques such as artificial intelligence, should be highly valuable. In this work, a critical evaluation is made on the state-of-the-art of the previous control approaches in polymer extrusion in industry and research while identifying their limitations. Then, some of the possible directions for future research and also to develop an advanced process control strategy are presented by eliminating a few of the existing limitations.     

Cross-directional control of sheet and film processes

Sheet and film processes include polymer film extrusion, coating processes of many types, paper manufacturing, sheet metal rolling, and plate glass manufacture. Identification, estimation, monitoring, and control of sheet and film processes are of substantial industrial interest since effective control means reduced usage of raw materials, increased production rates, improved product quality, elimination of product rejects, and reduced energy consumption. This paper reviews recent developments in sheet and film process control with particular attention to the effectiveness of existing techniques at addressing the critical aspects of sheet and film processes.