Industrial
The next evolution in industrial automation: self-aware motion control
By Jeff DeAngelis, vice president of industrial communications and motion control, industrial and healthcare business unit at Analog Devices
I
The next evolution in industrial automation requires a machine to independently adapt its performance parameters to complete an assigned task from a factory operator or reconfigure itself to optimise its behaviour based on input from a productivity- enhanced artificial intelligence (AI) observer algorithm. The value of a self- aware machine is its ability to maximise productivity, extend the operational lifespan of equipment, and reduce maintenance costs.
The journey to self-aware motion control
Self-awareness describes a system with knowledge of itself based on an understanding of its capabilities and the system’s performance goals. In fact, self-aware motion control systems require the implementation of multiple control loops that interpret sensor inputs and desired system parameters and provide the capability to compare their own operational behaviour vs. the desired system performance. To accomplish these goals and create self-aware motion control systems, we need to create an adaptive motion control agent to monitor the systems actions, and dynamically adapt its performance based on the drive system’s work environment.
Our focus in this article is to provide an approach to achieve a self-awareness motion control system by using an autonomous agent to detect and monitor the continuously changing working environment conditions. These conditions are derived from a series of nested closed loop real-time performance models that take in motion parameters from the field level drives. Once an electrical and mechanical model of the drive system is
14 September 2022
Element I: Goal or task: A clear goal or task of the system needs to be established for the system to achieve. In our example this means “move the beer mug from point A to point B in the best way to not spill any beer". Element II: Desired system behaviour: Once this goal is established, then the next level of self-aware motion control map initiates the desired motion behaviour. For our beer mug example this would be “use a linear motion to move the beer mug while automatically adjusting its motion to compensate for the varying weight and size of the beer mug within the required control safety limits of the mechanical system”.
Figure 1. Automation pyramid
derived, this model is used to compare and adjust the desired system performance requested at the supervisory, planning, or management levels of the automation pyramid (Figure 1). As a new desired system performance is requested from any level above the supervisory part of the automation pyramid, a set of new control parameters is transmitted to the adaptive control portion of the motion control system. The system then responds by modulating its performance to match the new performance request.
The two main benefits in achieving a self- aware motion control system are the ability to self-regulate and maximise performance of the motion control system automatically in real-time. This new capability provides
Components in Electronics
the supervisory, planning, and management levels of the automation pyramid the opportunity to modulate the self-aware motion control system by implementing a productivity enhancement. In addition, an AI-enabled software algorithm can be used to modulate system performance to achieve a better factory-wide outcome. Let us examine a self-aware motion control concept map to better understand the four fundamental elements required to implement a self-aware motion control system.
Self-aware motion control concept
map: To implement this level of self-aware motion control, we need to develop a control system map. Figure 2 represents the four key elements needed to successfully implement self-aware motion control.
Once the goal and desired system behaviour is established, the adaptive control engine dynamically drives convergence between the core drive system kinematics and its companion mechanical system, by auto-tuning the motion control drive and its integrated mechanical system to achieve peak operating performance while operating in its unique work environment.
Element III: Core drive system: At the heart of the self-aware motion control system is its kinematics. The challenge is to observe, learn, and monitor the performance capabilities of the motor and drive systems. To create a working model of the drive system, an intelligent observer needs to be implemented to gain the fundamental understanding of its motion parameters and its physical limits. This is achieved through a Field-Oriented Controller (FOC) with dedicated position sensors or a sensor-less FOC approach to learn how the motor is stressed and reacts in its operating environment. By monitoring and auto-tuning the control parameter values from the motor’s torque-
www.cieonline.co.uk
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62
