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Fundamentals of Controller Performance Assessment
Published in Raghunathan Rengaswamy, Babji Srinivasan, Nirav Pravinbhai Bhatt, Process Control Fundamentals, 2020
Raghunathan Rengaswamy, Babji Srinivasan, Nirav Pravinbhai Bhatt
As a result, there is a need for automated techniques to identify such anomalous behaviour. Interestingly, what might be visually simple to identify is, in general, difficult to automate. Once the problem is identified, then the root cause for these problems should be diagnosed for further corrective action. For example, if the output is oscillatory around the setpoint, how does one rectify the problem? It turns out that the approach to reduce oscillations would depend on the root cause for oscillations. As a result, the first step in mitigation is the diagnosis of the cause for poor performance. Similar remarks can be made about variability and drifts in the process. Finding the root cause for anomalous behaviour is called control loop performance diagnosis.
Control of the Pulp and Paper Making Process
Published in William S. Levine, Control System Applications, 2018
To design a process which produces low variability product, a true integration of the process and control design disciplines is required. The old way of designing the process in steady state, and adding the controls later, has produced the pulp and paper mills of today, which, as variability audits have already shown, are variable far in excess of potential. Control algorithm design follows a very general methodology and is largely based on linear dynamics. When thinking about control loop performance, the engineer pays no attention to the actual behavior of the process. For instance, the most important phenomena in the pulp and paper industry concern pulp slurries and the transport of fiber in two- or three-phase flow. The physics that govern these phenomena involve the principles of Bernoulli and Reynolds and are very nonlinear. The linear transfer function is a necessary abstraction to allow the control engineer to perform linear control design, the only analysis that can be done well. Yet in the final analysis, control only moves variability from one stream to another, where hopefully it will be less harmful. Yet the process design is not fixed. What about the strategy of creating new streams?
Process Optimization and Control
Published in Jose A. Romagnoli, Ahmet Palazoglu, Introduction to Process Control, 2020
Jose A. Romagnoli, Ahmet Palazoglu
Initially we focused on one process control activity—keeping the controlled variables at their specified set-points. The next question is What about Process Optimization? In an industrial environment all these activities are organized in the form of a hierarchy, with required functions at lower levels and desirable yet optional functions at the higher levels (Figure 20.1). At the top of the hierarchy, Business Management is tasked with creating a corporate policy and organizing, planning, controlling, and directing an organization's resources to achieve the desired enterprise-wide objectives. Site and plant management level watches over and organizes the daily operations of manufacturing plants and related sites, overseeing production, so that the plants are running smoothly and efficiently. Scheduling and Optimization level deals with supply chain management, raw material/product planning, while process optimization covers single unit as well as plantwide optimization. Advanced supervisory control involves issues associated with multivariable control tasks (as well as, e.g., model predictive control) which will provide optimal set-points to the regulatory control level. Basic process control encompasses single-loop feedback controllers as well as more advanced techniques as we have seen throughout the book. This may include control-loop performance monitoring as well. Finally at the base level, we have the validation of the sensors and actuators which may include limit checking and other data pre-processing techniques. Naturally, process data (flows, temperatures, etc.) as well as enterprise data (commercial, financial information) are used to make decisions throughout this hierarchy.
Optimal robust state-feedback control of nonlinear systems: minimal time to target
Published in International Journal of Control, 2021
The current paper, which concentrates on optimal feedback control, builds upon the studies of Yu and Hammer (2016a, 2016b) and Choi and Hammer (2018a, 2018c), where open-loop minimal-time control is considered. The performance of optimal feedback controllers is never inferior to that of open-loop controllers. This observation is a consequence of the fact that the feedback function ϕ of Figure 1 is a function of the time and the state; open-loop control is the special case where ϕ is a function of the time only. Thus, optimisation over the class of feedback functions includes optimisation over open-loop controllers and, therefore, feedback performance is never inferior to open loop performance.
A new control strategy applied in X-ray source to improve imagining quality
Published in Journal of the Chinese Institute of Engineers, 2021
Peng Mao, Mao Zhang, Yuanchao Liu, Weiping Zhang
After achieving the plant and the open loop frequency response, the closed loop frequency response will be evaluated. Thus, stability information such as bandwidth, gain margin and phase margin can be obtained to evaluate the control loop performance.