What is a Control System in robotics
What is a Control System?
In common, a control system is a part of a bigger system that regulates the behavior of the larger movement itself.
The control system of a machine or computer is the set of programs in the operating method that communicates with the CPU and peripherals in sequence to run the computer.
In a CNC device, the control system likely consists of a controller cabinet (logic board) and several hydraulic actuators managed by control valve switches.
With the improvement of technology, control systems have become more advanced and intelligent.
In the old days, involuntary switches were used for controlling a system, and then came relays, then programmable logic controllers (PLCs), microcontrollers, and now, microprocessors.
All the control systems can be broadly classified below two headers: sequential control systems and feedback control systems.
What is a Self-learning Control System?
The self-learning or adaptive control system is a kind of advanced smart feedback-based control system.
Actuators: This part of a system gives the output function of the system. For example, the atmospheric cylinder of the automatic door closing system is the output system. Hydraulic and pneumatic cylinders, electric and hydraulic motors are utilized as actuators in most utmost systems.
System Execution Parameter Sensors: Performance of the actuators is sensed using certain sensors to get the feedback signals about how much deviation exists in the actuator performance. Mechanical sensors, laser-based sensors, including proximity sensors are a few names of the huge variety of sensors applied in the industry.
Environment Parameter Sensors: These sensors are used for observing the change in the running environments. The signals from these sensors help in filtering the control parameters otherwise designed for the exemplary operating environment.
Data Acquisition System: The signals sent by the System Performance Parameter Sensors as well as the Environment Parameter Sensors are obtained and converted to use data or information by the data acquisition system.
Knowledge Base: Useful data or information is stored orderly here. As the system grows, the size of the knowledge base advances and so performs the self-learning control system.
Decision-Making System: This is the brain. It gives optimized signals to the actuators based on the information, knowledge, and present condition.
Applications of Adaptive Self-learning Control Systems
Active suspension systems: These exceptional automobile suspension systems use separate actuators to maintain the suspensions for the individual wheels.
When a wheel operates over a bump in the road, the control system senses it and decides to release some tension (pressure) from the actuator attached to that wheel, which in turn allows the suspension of the wheel to increase, without disrupting the rest of car.
When one wheel of the car attains a depression or pothole in the road, the system builds the hydraulic pressure of that actuator in such a way that the actuator exerts the suspension of that wheel downward, and thus the rest of the car doesn’t go destabilized.
Auto-pilot or cruise control systems: The autopilot is an intelligent control system used essentially for aircraft to fly without the demand for human interventions. The system continuously observes the system parameters (like engine acceleration, vibrations, engine heat, and airspeed) as well as the climate parameters (like altitude, humidity, and wind speed) and make best flying decision continuously. Related systems are utilized for spacecraft and ships as well.
Adaptive mobile robots: The adaptive control system of sensible mobile robots help in gaining and applying knowledge from the neighboring environment. It learns about things like new barriers, and the motion of the robot keeps progressing.
Conclusion
The self-learning adaptive control system is a set of advanced intelligent feedback-based administration system. It has complicated feedback and environment sensors, a decision-making system, a database, and work-producing actuators. The system makes smart operating decisions based on modern operating conditions and past encounters. This kind of control system is most proper for applications where whole prior information of the time-varying command parameters is not possible. For example, the weight of an airplane continuously declines as the fuel is consumed, thus increasing the range the airplane can fly on the unused fuel. While planning this kind of system, it must be kept in mind that these applications cannot yield a dysfunctional control system, so system dependability is supreme.
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