In the early days, control systems were common in the industrial environment. Large process facilities used electronic process controller for regulating continuous variables like temperature, pressure and flow rate. Electrical relays built into ladder-like networks were one of the first discrete control devices to automate an entire manufacturing process.
It was in the year 1969 that the first computer-based controller was introduced. All this programmable logic controller (PLC) did was to mimic the operations of the already available discrete control technology using the outdated relay ladders.
But with the advent of PC technology, a drastic shift in trends can be noticed in process and discrete control. An off-the-shelf desktop loaded with adequate hardware and software can run an entire process unit. It can execute complex and established proportional integral derivative (PID) algorithms or work as a distributed control system. This is one side of the coin where the control system is used to manufacture products. On the other side of the coin is the control system becoming a part of the end product being manufactured.
In the 1950s and 1960s, the push to Space generated a lot of interest in embedded control systems. Control engineers were busy building control systems that could be fused and shipped as part of the end product, e.g., flight simulators, engine control units, etc.
Embedded control systems gained, momentum primarily in the automotive and aerospace sectors. By the end of the twentieth century, we saw the excitement spilling into other industries too and embedded control systems have now turned out to be a ubiquitous part of our lifestyle. Even white goods like washing machines and air-conditioners have complex control algorithms executing inside them for smarter and intelligent functions.
Challenges for control engineers
As is the case with any competitive market, control engineers are under constant pressure to complete their projects within a tight schedule and at low cost so that the companies can get their products to the market faster. 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 dont hold up the overall development timeline. In addition to control design, engineers are challenged to provide predictable performance and develop complex, competitive features for the products they deliver.
Traditionally, 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 the engineers have to go back to their design table to correct the errors and come out with a fresh prototype, which results in a drastic increase in the time and money spent on 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.
Approaching with model-based designing
Model-based design (MBD) is a mathematical and visual method of addressing the problems associated with designing complex control systems that 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 modeling a plant (system identification), analyzing and synthesizing a controller for the plant, simulating the plant and controller, and deploying the controller 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- and discrete-time building blocks. The models built, along with some simulation tools, can lead to rapid prototyping, software testing and verification. Not only is the testing and verification process enhanced but, in some cases, hardware-m-the-loop simulation can also be used with the new design paradigm to perform testing of dynamic effects on the system more quickly and more efficiently.
The important steps in MBD approach are:
1. System identification.
ystem 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, processing it and choosing a mathematical algorithm that can be used to identify a mathematical model of the actual system under consideration. Various kinds of analyses and simulations can be performed using this model before we can use it to design a model-based controller.
2. Designing a controller.
The second step in model-based control design is to analyse and synthesize a controller. Dynamic characteristics of the plant are identified using the mathematical model conceived from the previous step. A controller can then be synthesized based on the dynamic characteristics of the plant.
3. Simulating the plant and the controller.
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 the model-based design approach by simulating offline a simple linear time-invariant (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 prominent advantages of MBD over the traditional approach are:
1. Engineers can locate and correct errors early in system design, where the time and financial impact of the system modification is minimised.
2. Design reuse is facilitated, both for system upgrading and for developing derivative systems with expanded capabilities.
3. A common design environment for all developers facilitates general communication, data analysis and system verification between different development groups.
Benefits of graphical techniques
Modeling and simulation tools have been in use for a 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 rely heavily on traditional text-based programming and mathematical models. And this was a major cause of concern since developing models in text-based programs wasnt 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 stages before a final fault-free model can be created, as mathematical model used to undergo unseen changes during translation of the model through the various design stages.
Graphical modeling tools overcome these challenges. Today, graphical tools cover all the aspects of design. These tools provide a very generic and unified graphical modeling environment that reduces the complexity of model designs by breaking them into hierarchies of individual design blocks. Due to this, designers achieve multiple levels of model fidelity by simply replacing one block element with another. Graphical models are also the best way of documenting engineers ideas. These help engineers conceptualize the entire system in much better way and simplify the process of transporting the model from one stage to the other in the design process.
Boeings EASY 5 simulator was among the first modeling tools to be provided with a graphical user interface. Many other tools followed it.
NI Lab VIEW, a highly productive and flexible graphical development environment from National Instruments, provides an entire suite of graphical tools for dynamic system design and simulation, called the Nl Lab VIEW Control Design and Simulation Bundle. The bundle includes graphical tools like system identification toolkit for identifying large multivariable models of high-order systems, control design toolkit for analyzing and synthesizing controllers, simulation module for investigating time-dependent behavior of complex engineering systems, simulation interface toolkit to create custom Lab VIEW user interface for interactively verifying other third-party simulation models, and state diagram toolkit for editing and debugging the models at both the framework and the functional levels.