Modelling and Control of Real-Time Systems using MATLAB
Mathematical models of real plants are commonly created to investigate their behavior in response to different inputs. Because of the risk of plant failure and the high cost of testing, implementing a new control scheme on an operating plant is not recommended. Control engineers can analyse the output of a control system in the in presence of dynamic disturbances using computer simulations of mathematical models without disturbing the actual system.
Furthermore, the operator can examine the plant's reaction in crucial situations by simulating a mathematical model, which is never suggested in a real-time operation of plant. Therefore, mathematical models can be used instead of actual plants, and they are important, effective, and common tools for simulation study of any real process.
Thus, the focus of this internship is to make to learn about the modelling of real-time systems such a MAGLEV system, Half Quadrotor, Continuous stirred tank reactor (CSTR) etc. Further, advance control schemes will also be implemented to control such sophisticated non-linear systems.
Month 1
Literature survey, Basics of mathematical modelling, simulation and control, case study of real-time systems such as MAGLEV system, Half Quadrotor, Continuous stirred tank reactor (CSTR) etc.
Month 2
Basics of MATLAB/Simulink
Month 3
Implementation of Mathematical modelling in MATLAB
Month 4
Validation of designed mathematical model
Month 5
Designing of controller
Month 6
Results, discussion and documentation
Optimization Toolkit Design in LabVIEW
LabVIEW is one of the prominent programming platforms due to its simplicity and robustness towards hardware design. It finds application in different fields of engineering such as industrial automation, signal processing, instrumentation, control system design and so forth. LabVIEW has a rich ecosystem of toolboxes and libraries for simplifying the improvement in numerous zones of engineering, however, it lacks in optimization toolbox for advanced meta-heuristic algorithms.
This is a serious deficiency as there is an astonishing improvement in the field of meta-heuristic algorithm in recent years. Thus, the motive of the internship is to design recently acclaimed advance meta-heuristics in LabVIEW environment. Standard testbed of non-trivial functions will also be implemented and the designed algorithm must be benchmarked on that standard testbed of non-trivial functions. Further, the designed toolkit may also be tested for real-world optimization task.
Month 1
Literature survey, basic introduction of LabVIEW environment
Month 2
Basics of optimization and evolutionary computation
Month 3
Study of mechanism of advance optimization algorithms
Month 4
Design and development of standard testbed of non-trivial functions and a real-life application of designed toolkit
Month 5
Validation and comparative study of results
Month 6
Discussion and documentation of applied methods as well as results
Soft Computing using MATLAB
Soft Computing is a modern approach to computation that is inspired by the human mind's extraordinary ability to reason and learn in an environment of uncertainty and imprecision. It is motivated from the intelligent biological behaviors such as genetics, evolution, ant behavior, owls hunting mechanism, foraging method of flying squirrels, particle swarming, and human nervous systems etc. Nowadays, soft computing techniques provide a better solution in comparison to the traditional techniques for complex real-time problems even in the absence of mathematical model. Thus, the approaches of soft computing are becoming very popular in the scientific world and therefore the objective of this internship is to provide you a detailed exposure of soft computing such as fuzzy logic, meta-heuristics etc. Moreover, real-life applications of soft computing will also be explored and implemented.