Model based design

Model based design

The dawn of the electrical age brought with it various novel, innovative and advanced control systems. It was as early as 1920's when the two strands of technology, control theory and control system, came together to produce large scale integrated systems. In the early days controls systems were commonly found in the industrial environment. Large process facilities started using Electronic process controller for regulating continuous variables like temperature, pressure and flow rate. Electrical relays build into ladder-like networks were one of the first discrete control devices to automate an entire manufacturing process. In the year 1969, the first computer-based controllers were introduced, these were the early programmable logic controllers (PLC) , all it did was to mimic the operations of already available discrete control technology using the out-dated relay ladders. But with the advent of PC technology a drastic shift in trends can be noticed in the process and discrete control market, an off-the-shelf desktop loaded with adequate hardware and software can run an entire process unit, they can execute complex and established PID algorithms or work as a Distributed Control System (DCS) This is one side of the coin where the control system is used to manufacture products. The other distinctive part of the coin is where the control system becomes a part of the end product being manufactured.

In the 1950s and 1960's the push to Space generated a lot of interest in embedded control systems. Control engineers were busy building control system that can be fused and shipped as part of the end product e.g. flight simulators, engine control units etc. It gained a lot of momentum primarily in automotive and aerospace sector. But by the end of the twentieth century we saw that the excitement has spilled to other industries too and embedded control systems has turned out to be a ubiquitous part of our modern society. Even White goods like washing machines and air-conditions have complex and advanced control algorithms embedded and executing inside them making them a much more "smart and intelligent" appliances.


As is the case with any competitive market, the control engineers are under a constant pressure to finish their projects within tight schedule and at low cost so that the companies can get their products to market faster since it's a known fact that whoever does it will have an added advantage over their competitors. Specifically in some cases, the control system is part of a larger system such as flight controllers used in an aircraft. Here it is vital to deliver the control system on schedule so that you don't hold up the overall development timeline. In addition to this control design engineers are challenged to provide predictable performance and develop complex, competitive features for the products they deliver.

Also traditionally the control design engineers get to do a complete test of their software design only in the later part of the design effort when the actual peripheral prototype hardware and real-time embedded targets are available. This is a highly inefficient method as on discovering an error at this stage they have to go back to their design table to correct the errors and come out with a fresh prototype resulting in a drastic increase in the time and money involved in developing the final product.

When developing embedded control systems, designers are squeezed by two trends— shrinking development cycles and growing design intricacy. The divide-and-conquer strategy for developing these complex systems means coordinating the resources of people with expertise in a wide range of disciplines. The traditional, text based approach of embedded system design is not efficient enough to handle such advanced, complex systems.

Model-Based Designing

The control engineers have begun addressing these challenges with more sophisticated tools. The most popular out of them is the Model-based design (MBD) tool. MBD is a mathematical and visual method of addressing the problems associated with designing complex control systems, and is being used successfully in many motion control, industrial equipment, aerospace, and automotive applications. It provides an efficient approach for the four key elements of the development process cycle ("V" diagram): modeling a plant(system Identification), analyzing and synthesizing a controller for the plant, simulating the plant and controller, and deploying the controller thus integrating all these multiple phases and providing a common framework for communication throughout the entire design process. This model-based design paradigm is significantly different from the traditional design methodology. Rather than using complex structures and extensive software code, designers can now define advanced functional characteristics using continuous-time and discrete-time building blocks. These built models along with some simulation tools can lead to rapid prototyping, software testing and verification. Not only is the testing and verification process enhanced, but also, in some cases, hardware-in-the-loop simulation can be used with the new design paradigm to perform testing of dynamic effects on the system, more quickly and much more efficiently.

The important steps in MBD approach are:
# System identification is the initial step in the model-based control design process. It is an iterative process in which the plant model is identified by acquiring raw data from the actual real world system, process it and choose a mathematical algorithm that can be used to identify a mathematical model of the actual system under consideration. Various kinds of analysis and simulations can be performed using this model before we can use it to design a model-based controller.
# The second step in model-based control design process is to analyze and synthesize a controller. Dynamic characteristics of the plant are identified using the Mathematical model conceived from the previous step. A controller can be then be synthesized based on the dynamic characteristics of the plant.
# Before deploying the controller it is important to investigate the time response of the dynamic system to complex, time-varying inputs. This is done in the third step of Model-Based design approach by simulating offline a simple LTI or a non-linear model of the plant with the controller. Simulation allows specification, requirements, and modeling errors to be found immediately, rather than waiting until later in the design effort.

Some of the notable advantages the MBD offers in comparison to the traditional approach are:
* With MBD the engineers can locate and correct errors early in system design, where the time and financial impact of system modification is minimized.
* Design reuse is facilitated with MBD, both for system upgrading and for developing derivative systems with expanded capabilities.
* MBD provides a common design environment for all developers, facilitating general communication, data analysis, and system verification between different development groups.

Advantage of Graphical Techniques

Modeling and simulation tools have been in use from a very long time, but they are highly inefficient and inadequate to deal with the advanced and complex nature of the modern control systems since these tools are totally non-graphical in nature. Because of the limitation of graphical tools design engineers used to heavily rely on traditional text-based programming and mathematical models. And this used to be a major cause of concern since developing models in text-based programs wasn't just difficult and time consuming but also highly prone to errors. Also debugging the model and correcting the errors used to be a tedious process. It required a lot of trial and error cases before a final fault free model can be created, as mathematical model used to undergo unseen changes during the translation of the model through the various design stages.

These challenges are overcome by the use of graphical modeling tools. The modern day graphical tools cover all aspects of design. These tools are a very generic and unified graphical modeling environment, they reduce the complexity of model designs by breaking them into hierarchies of individual design blocks. Due to this designers achieve multiple level of model fidelity by simply substituting one block element with another. Graphical models are also the best way of documenting engineer's ideas. It helps engineers to conceptualize the entire system in much better way and simplifies the process of transporting the model from one stage to the other in the design process. Boeing's simulator EASY5 was among the first modeling tools to be provided with a graphical user interface. This was followed by many other tools.

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