Hierarchical control

A SOFLC is a two-level hierarchical control system that is comprised of ... 

Hierarchical Control, in which a three-level command hierarchy is established each lower-level echelon element keys on those in the next higher echelon on each time step of the evolution. 

The three hierarchical levels are interconnected by information flowing from the strategic level via the tactical level to the operational level, and the other way around. From upper to the lower level, the information flow is related to the environment on the strategic level, which is the organizational values and norms. However, as Thompson (Thompson, 1967) identified, the tactical control level can allow the operational level to operate as a relatively closed system. The tactical level provides a buffer between the uncertain environment and stability of resources required for uninterrupted production on the operational level. In this way the influences from the external environment on the operational level will be reduced to a minimum. The information flow going from lower to upper level is related to the operational process or transformations. The top down flow provides the restrictions and conditions for the transformation, while the bottom up flow provides information about the status of inputs, outputs, and resources of the transformations. The horizontal information flows are between different control elements on one hierarchical control level. 

By reducing the complexity of the steering element the analysis process aims to retrieve an ineffective control element. However, the actual interrelations between the different hierarchical control levels must be known. No literature exists to accurately describe how the different hierarchical control levels are interrelated. Therefore, the author has established the relationships, as discussed by Korvers (Korvers et al., 2001b), see Figure 31. 

Figure 31 A simplified model of how the hierarchical control processes are interconnected. 

Please note how the steering or the organizational values and norms on the next-higher level can also be questioned (second-loop learning) in Figure 31. Moreover, note that the external environment becomes increasingly important in the higher hierarchical control levels. 

Identify the ineffective control processes Identify the accompanying control processes and the initial ineffective control elements on the subsequent hierarchical control levels that let the prioritized precursors occur. 

Identify the latent conditions Identify the conditions causing the control elements on the different hierarchical control levels to be ineffective. 

In practice the processes controlling the identified and prioritized precursors are identified by taking the theoretical hierarchical control model, as shown in Figure 31, as a reference. The controlling processes identified in practice are linked to their theoretical counterparts to constitute the control processes of the identified precursors. When these control processes are identified, the ineffective control elements are identified by the flow scheme, depicted in Figure 33. 

This flow scheme structurally checks whether or not a control element is working effectively. This flow scheme is used as a guideline on each successive hierarchical control level (operational, tactical, strategic), starting on the operational level. 

The number of initial ineffective control elements on each hierarchical control level (=where) and the corresponding latent conditions leading to these ineffective control elements (=why) will be discussed. The results of the discussion will be reflected on the number of affected safety barriers (=consequences). Furthermore, the individual affected safety barriers will be combined, to find possible alignments of affected safety barriers that enable accidents (=risks). Finally, the weaknesses of the current safety management system are indicated by the previous findings. 

Stage 4, the identification of the initial ineffective control element, can only be identified on the operational control level. Due to limited information (in spite of selecting accidents on the basis of information-richness), hierarchical control levels cannot be identified. 

To test whether the results are also statistically significant, a chi-square test for contingency is applied to the results obtained. However, as the values retrieved from the cases shown in Figure 43 are very small, the total number of different hierarchical control levels in that company, were added to that company s total. This led to the categorized results as shown in Table 18, upon which the chi-square test for contingency is applied. 

Luetje CW, Patrick J (1991) Both alpha- and beta-subunits contribute to the agonist sensitivity of neuronal nicotinic acetylchohne receptors. J Neurosci 11 837-845 MameU-EngvaU M, Evrard A, Pons S, Maskos U, Svensson TH, Changeux JP, Faure P (2006) Hierarchical control of dopamine neuron-firing patterns by nicotinic receptors. Neuron 50 911-921 Mansvelder HD, McGehee DS (2000) Long-term potentiation of excitatory inputs to brain reward areas by nicotine. Neuron 27 349-357... 

The two-time-scale behavior of the material balance of integrated processes with large recycle suggests the use of a hierarchical control structure with two tiers of control action ... 

Practical considerations in implementing the hierarchical control framework developed above concern the availability of manipulated inputs to address the control objectives in the slow time scale (it is possible that dim(us) < dim(ys)), as well as achieving a tighter coordination between the distributed and supervisory control layers. Both issues are effectively addressed by using a cascaded control configuration, which extends the choice of controlled variables in the slow time scale to include the setpoints y)p of the distributed controllers. 

Remark 3.3. The hierarchical control structure proposed in this chapter (illustrated in Figure 3.4) is dissimilar to the composite control approach reviewed in... 

Figure 3.4 Hierarchical control relies on separate, but coordinated, fast and slow controllers, designed on the basis of the respective reduced-order models, to compute the values of the separate inputs that influence the fast and slow dynamics of the process. Tighter coordination between the distributed and supervisory control layers is achieved by using a cascaded configuration.

Section 2.2, in that two layers of control action involving separate controllers are proposed, whereas composite control relies on a single (possibly multivariable) controller with two components, a fast one and a slow one. Thus, the hierarchical control structure accounts for the separation of the flow rates of the process streams into two groups of inputs that act upon the dynamics in the different time scales. On the other hand, composite controller design (Figure 2.9) presupposes that the available manipulated inputs impact both the fast and the slow dynamics and relies on one set of inputs to regulate both components of the system dynamics. 

Remark 3.4. In the context of the present chapter (and of the remainder of the book), the term hierarchical control structure reflects the use of two (or multiple) coordinated tiers of control action, and should not be confused with hierarchical plant-wide controller design strategies (see, e.g. Ponton and Laing 1993, Luyben et al. 1997, Zheng et al. 1999, Antelo et al. 2007, Scattolini 2009, and references therein), which use the term hierarchy to denote a set of guidelines, to be followed in sequence, for designing the control system for a chemical plant. 

The interactions between units do, however, become significant over long periods of time processes with recycle exhibit a slow, core dynamic component that must be addressed in any effective process-wide control strategy. This chapter presented an approach for systematically exploiting this two-time-scale behavior in a well-coordinated hierarchical controller design. The proposed framework relies on the use of simple distributed controllers to address unit-level control objectives in the fast time scale and a multivariable supervisory controller to accomplish process-wide control objectives over an extended time horizon. 

The resulting hierarchical control structure is represented schematically in Figure 5.2. Note that, while controller design proceeds in a bottom-up manner, starting from the fastest time scale, during the operation of the process there will exist a tight top-down interconnection via control cascades between the supervisory and regulatory layers. 

The presence of three distinct time horizons in the process dynamics, as evinced by the analysis above, warrants the use of a hierarchical control structure that addresses the distributed (unit-level) and plant-wide control objectives separately. 

We proposed a method for deriving nonlinear low-dimensional models for the dynamics in each time scale. Subsequently, we proposed a hierarchical controller design framework that takes advantage of the time-scale multiplicity, and relies on a multi-tiered structure of coordinated decentralized and supervisory controllers in order to address distributed and process-level control objectives. 

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