Management control systems thesis

Rumelt described strategy as a type of problem solving in

Management control systems thesis

Management control systems thesis

Many industrial processes function under varying operating conditions, and this generally presents a problem for the design of the control system. Effectively, both conventional and advanced control systems currently operating in industry are designed incorporating fixed parameters. These parameters can be adjusted to meet specific operating conditions but when these conditions vary, the process dynamics changes and the performance of the control system deteriorates, resulting in oscillations and instability.

The ADEX controller incorporates an adaptive mechanism that carries out a real time process dynamics identification and a controller parameter self-tuning adjustment that avoids deterioration of control performance when operating conditions or process dynamics change.

It predicts, guides and stabilizes the evolution of process variables in a desired way, and customizes the controller configuration for the various process domains of operation to yield what is defined as Optimized Adaptive Control Performance.

An optimized control system is able to drive the process variables along desired trajectories by using suitably bounded control signals, and achieve and maintain in real time process stability in their optimal operating points, in spite of changes in process dynamics and operational context, and the presence of noise and perturbations.

Management control systems thesis

ADEX Optimized Adaptive Control Systems are characterized by utilizing optimized control strategies which integrate ADEX controllers in such a way that they are capable of auto-adjusting to changes in the process dynamics and operational context anticipated or notcompensate for the effects of noise and perturbations measurable or unmeasurable acting on industrial processes, and provide the performance and stability of an optimized control system, minimizing energy consumption and emissions while maximizing productivity and equipment integrity.

Prediction mechanism supported by self-learning adaptation in real time providing precise control even under influence of perturbations and varying process dynamics.

Combines expert control with adaptive predictive control for better management, robustness and flexibility, incorporating plant knowledge. No need for upfront modelling. No need for programming. Simple configuration steps only. No need for on-line tuning.

Can also be embedded in applications. Original patent on Adaptive Predictive Control: It presented the update on methodology, stability theory and industrial applications of APC systems.The objective of this thesis is to review the management control system of the Urited States Navy Flight Operations (OFC-O!) funds at the Headquarters, Commander, Naval .

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MBA | Cal State LA