Model reference adaptive controllers MRACs — incorporate a reference model defining desired closed loop performance Gradient optimization MRACs — use local rule for adjusting params when performance differs from reference. Stability optimized MRACs Model identification adaptive controllers MIACs — perform system identification while the system is running Cautious adaptive controllers — use current SI to modify control law, allowing for SI uncertainty Certainty equivalent adaptive controllers — take current SI to be the true system, assume no uncertainty Nonparametric adaptive controllers Explicit parameter adaptive controllers Implicit parameter adaptive controllers Multiple models — Use large number of models, which are distributed in the region of uncertainty, and based on the responses of the plant and the models. One model is chosen at every instant, which is closest to the plant according to some metric. Adaptive control based on discrete-time process identification Adaptive control based on the model reference control technique  Adaptive control based on continuous-time process models Adaptive control of multivariable processes  Adaptive control of nonlinear processes Concurrent learning adaptive control, which relaxes the condition on persistent excitation for parameter convergence for a class of systems   Adaptive control has even been merged with intelligent techniques such as fuzzy and neural networks and the new terms like fuzzy adaptive control has been generated.
A sample of possible research topics in this area is presented below. In addition to the following sample topics, please contact us at consulting etcoindia.
The concept of demand forecasting is diminishing as more and more companies are now focusing on getting accurate and timely demand information rather than depending upon forecasts. This is carried out by effective integration of information from all the nodes of the supply chain and disseminating upstream as well as downstream.
However, there are many industries that will continue to depend upon push strategy and demand forecasting. The students may like to study about the drawbacks of traditional forecasting methods like time series forecasting, moving averages, trend analysis, etc.
Many companies want to incorporate real time data in their forecasting models and focus on forecasting for shorter periods.
This requires lots of additional knowledge over and above the traditional ways of working upon past demand data. The modern forecasting models may be based on accurate knowledge of customer segments, major factors that influence forecasting accuracy, information integration, bullwhip effect, scenario planning, simulations, external factors, risks, and causal Fishbone or Ishikawa analysis.
Most of the studies may be qualitative or triangulated. Aggregation is carried out by a company to determine the levels of pricing, capacity, production, outsourcing, inventory, etc. Aggregation planning helps in consolidation of the internal and external stock keeping units SKUs within the decision and strategic framework for reducing costs, meeting demands and maximising profits.
It may be viewed as the next step of either demand forecasting push strategy or demand information accumulation pull strategy for carrying out estimations of the inventory level, internal capacity levels, outsourced capacity levels, workforce levels, and production levels required in a specified time period.
Aggregation planning in modern supply chains is carried out using advanced planning tools comprising of 2D layout maps, 3D spatial maps, structural maps, data association with map items, spatial data mining, location-aware data mining, analytical hierachy planning, etc.
The students may like to conduct qualitative case studies and modeling-based quantitative studies to research about modern practices of aggregation planning in various industrial and retail sectors.
I Global Supply Chains: In the modern world, suppliers in a country are facing direct competition from international suppliers as if the latter are operating within the country. This has happened due to modernization of information management and dissemination, supply routes, payment channels, electronic contracts, leading to improved reliability and reduced lead times of international suppliers.
E-Supply Chains are linked with E-Businesses that use Internet as their medium for accepting orders and payments, and then using the physical channels to deliver the products. E-supply chain is an excellent example of pull strategy and short term demand forecasting. Information flow across the supply chain is instantaneous because both end points and the intermediate agents work through a single Internet enabled portal.
E-Bay and Amazon are viewed as the two most successful companies using this concept at global scales with built-in electronic contract signing and management, electronic payment processing, and electronic delivery processing. The students can find various case studies on E-Supply chains, although the empirical theories are still evolving.
The research studies would be quite challenging, modern and unique as the field is still evolving. K Supply Chain Risk Management: Supply chain risk management is gaining immense popularity due to globalization of competitive landscapes, and growing threats and uncertainty.
Risk management in supply chains is directly linked with supply chain agility and hence it needs to be done in very organized and objective manner, incorporating quantitative models.
The root of the problems lie somewhere in the uncertainties in upstream as well as downstream flows of materials, funds, and information. For example, if there are errors in calculating economic order quantities EOQ and reorder levels, the ordering process may not synchronize well with the lead-times.
On the other hand, the lead-times are uncertain due to various delay factors and fluctuation in costs if a transportation mode is changed. Holding inventory is the safest haven for logistics managers, but I am sure the top management of any organisation will never like it.Port Manteaux churns out silly new words when you feed it an idea or two.
Enter a word (or two) above and you'll get back a bunch of portmanteaux created by jamming together words that are conceptually related to your inputs.. For example, enter "giraffe" and you'll get .
Vol.7, No.3, May, Mathematical and Natural Sciences. Study on Bilinear Scheme and Application to Three-dimensional Convective Equation (Itaru Hataue and Yosuke Matsuda).
New direct adaptive active noise control algorithms in case of uncertain secondary path dynamics. / Kouno, Toshikazu Sano, Akira. / New direct adaptive active noise control algorithms in case of uncertain secondary path The second one is a fully direct adaptive feedforward control algorithm which is effective in a case when all of the.
Cervical cancer is the second most common cancer in women and the leading cause of cancer-related death in many developing countries. 1 Although well-organized programs for Papanicolaou screening.
Adaptive optimal control for continuous-time linear systems based the result is a direct adaptive control algorithm the effectiveness of the algorithm. An error-entropy minimization algorithm for tracking control of nonlinear stochastic systems decentralized adaptive control for interconnected nonlinear systems.
Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that .