Process inputs/outputs

Model-free adaptive (MFA) control does not require process models. It is most widely used on nonlinear applications because they are difficult to control, as there could be many variations in the nonlinear behavior of the process. Therefore, it is difficult to develop a single controller to deal with the various nonlinear processes. Traditionally, a nonlinear process has to be linearized first before an automatic controller can be effectively applied. This is typically achieved by adding a reverse nonlinear function to compensate for the nonlinear behavior so that the overall process input-output relationship becomes somewhat linear. It is usually a tedious job to match the nonlinear curve, and process uncertainties can easily ruin the effort. 

Compliance Policy Guides Computerized Drug Processing Input/Output Checking, 1987 7132a.07. 

Production processes All unit production processes in the plant need to be audited to check for process inputs, outputs, operating condihons and controls, and process efficiency. 

Data acquisition/recording Signal processing Input/output... 

This section deals with the problem of estimating the parameters of a reduced order FSF model from process input-output data using the least squares algorithm. 

Given the process input-output data generated from the relay experiment, the parameter vector 0 can be estimated using a recursive algorithm. Here, we propose to use the recursive least squares algorithm (Goodwin and Sin, 1984) given as follows... 

If we define a process surface as a hyper-plane derived from a multiple set of process inputs/output relationships, it will be possible to relate inputs to outputs, and tune the processor by altering the rulebase and comparing the effect on the process surface. Each point on the plane has its coordinate, [x y z], defining the position within the envelope relating inputs, [x y] to output [z]. This means that the fuzzy processor can be tuned by shaping the process surface, rather than by adjusting numerical gains. 

Once a proper PRBS signal has been identified and process data has been collected, one should determine the cross correlation of the process input-output signals and the autocorrelation of the process output. 

Another tricky question arises when facing life cycles of multi-output processes. Inputs, outputs and the related environmental impacts must then be allocated to products. This can be done according to physical properties of the product flows (mass or energy flows). If this is not possible or not justifiable the usual way is to allocate according to the economic value of the products (prices). In our study, both mass and price allocation have been applied to highlight the influence of this allocation procedure on the outcome of the analysis. 

Processes are the fundamental building blocks of a company s operations, and both the understanding and improvement in processes determine their excellence. Processes transform inputs (actions, methods, and operations) into outputs. They are the steps by which value is added to satisfy the expectations of customers. The processes interact with other processes in the supply chain, as outputs from one process form the inputs to another. Each process is therefore a part of a larger process so that an organization can be seen as a network of interconnected processes. In addition to the inputs and outputs, a process is defined by controls (a set of rules that determine how the process is to be performed), and the needed resources. Hence, we may describe a process as, process = /(inputs, outputs, controls, resources). 

In self-tuning control, the parameters in the process model are updated as new data are acquired (using on-line estimation methods), and the control calculations are based on the updated model. For example, the controller settings could be expressed as a function of the model parameters and the estimates of these parameters updated on-line as process input/output data are received. Self-tuning controllers generally are implemented as shown in Fig. 16.26 (Astrom and Wittenmark, 1995). 

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