Adaptive Control:
Adaptive Control is a control technique applied when a controller needs to adapt to varying states of condition that are constant or uncertain. It applies parameter estimation to ensure robust control of the system. It can be termed as intelligent control which further considers it as a method of control system designed to emulate certain attributes related to predictive control and human intelligence but most interestingly, its features are not limited to computational methodologies, logical postulations, and control strategies.
Currently, control as an aspect of artificial intelligence uses traditional control mechanisms to perform error detection, analysis, and correction, however, there is a new dawn in this aspect of futuristic technology where it will be more of intelligent control of complex systems. These complex systems will span from production and industrial systems to domestic and military devices since, in them, there are underlying challenges that can’t effectively be solved by the already existing control methodologies. These apply primarily to areas where heuristic methods might be required to enhance control legislation or where new control functions need to be established while the system remains in operation. Learning from experience and planning control actions may be necessary. Human operators have performed certain roles in the past so that when high-level decision-making techniques are needed for reasons of confusion, these techniques are used by humans, attributed to intelligent behavior.
The three major types of controllers we shall be looking at helping us achieve this will be;
Programmable Logic Controllers
The Programmable Logic Controller is a special device that shares similarities with a personal Computer but it is designed to perform effectively under harsh industrial environments – such as extreme temperatures, wet, dry, and/or dusty conditions. They are programmed to operate automated industrial systems, chemical process plants, or a wastewater treatment plant.
It is important to design and implement ideas based on a particular need when using PLC. To achieve this some knowledge on PLC programming needs to be drawn. Proportional Integral Derivative (PID) is a tool when adapted into the PLC will help control and reduce error.
A PLC can be programmed in a textual or graphical form that utilizes the logic controlling process. But the widely used is graphical that includes;
Ladder Diagrams (LD)
Function Block Diagram (FBD)
Sequential Function Chart (SFC)
Ladder Diagrams (LD)
Ladder logic is the most common form of PLC programming. It is also known as relay logic. Relay contacts used in relay-controlled systems are interpreted using the logic of the ladder.
Function Block Diagram (FBD)
Functional Block Diagram (FBD) uses a graphical method to program multiple functions in PLC. The benefit of using FBD is that it is possible to choose any number of inputs and outputs on a functional block. You may link the output of one function block to the input of another by using multiple inputs and outputs.
Sequential Function Chart (SFC)
Sequential Function Chart (SFC) applies function graphical language to interact and operate attached devices. It helps you concisely visualize complex processes.
Computer Numerical Controllers
Computer Numerical Control (CNC) is a technique that uses software embedded in microprocessors to control and automate machine systems. It is mostly used in the manufacturing industries for machining of various parts. Adaptive CNC uses different existing mechanisms already embedded to learn how to detect and handle errors.
Each item to be manufactured gets a custom computer program, usually written in a G-code language, stored in, and eventually executed by the machine control unit (MCU) – a microcomputer attached to the machine. The program includes instructions and parameters that the machine tool will follow, like the feed rate of the materials and the positioning and speed of the components of the tool. Mills, lathes, routers, grinders, and lasers are typical machine tools with CNC automated operation. It can also be used to monitor non-machine equipment, such as welding, electronic assembly, and filament winding machines.
The machine is thought to have more precision, complexity, and repeatability than what is possible through manual machining. It is also capable of greater precision, speed, and versatility, along with capabilities like contour machining, which enables the milling of contoured shapes, such as those created in 3D designs. For the maximum operation of the CNC machine, systems are integrated with Computer-Aided Design/Manufacturing (CAD/M) software, which gets integrated into the written program. Integration with ERP software and related applications, like enterprise asset management software, can promote operational intelligence processes, and help enhance plant efficiency and maintenance.
Fuzzy Logic Controllers
Adaptive fuzzy logic control is applied when there is a need to manage the uncertainties of systems. It uses a non-linear time-varying system to tune itself at points of deviation in other to correct the process. Regardless, it uses 0 to 1 to represent absolute falseness and absolute truth respectively. The basic steps for implementing Adaptive Fuzzy Logic Control are as follows;
Consideration of observable data – This is done to calculate the performance of the controller.
Adjustment of Controller Parameters – Controller performance helps to achieve the calculation of adjustment of controller parameter.
Improvement in performance of controller – This adjusts the controller parameter so that the controller performance will be improved.
Considerations reached to choose an adaptive Fuzzy Logic Controller are
Can the system be completely approximated by means of a fuzzy model?
If the device can be totally approximated by a fuzzy model, are the parameters now readily available?
If a system cannot be approximated totally, can it be done in bits?
If a system can be approximated by a series of fuzzy models then does the model have the same format with different parameters, or just have different formats?
If a system can be approximated by a set of fuzzy models with the same format, all with a different number of parameters, are all of these parameters easily available?
In conclusion, adaptive controllers ensure that error in a system is absent by learning the sequence in process delivery concerning the environment as its reference parameter. In application, it is used for altitude control in aerospace, traffic control in transport systems, enterprise decision making in business organizational settings, control of hypervelocity interceptor for the military, temperature control in HVAC systems, pattern recognition and classification, etc. A well-developed model integrating any of these controllers in a process will help to achieve an optimal output response.