Industrial Automation 2.0 Evolution: Self-Aware Motion Control

The next evolution in industrial automation requires machines to independently adjust their performance parameters to complete tasks assigned by factory operators or reconfigure themselves to optimize their behavior based on input from productivity-enhancing artificial intelligence (AI) algorithms . The value of self-aware machines is that they can maximize productivity, extend the operating life of equipment, and reduce maintenance costs.

A journey of self-aware motion control

Self-awareness refers to the system's self-recognition of itself based on its understanding of its capabilities and system performance goals. In fact, a self-aware motion control system requires the implementation of multiple control loops to interpret sensor inputs and expected system parameters and allow comparison of the system's own operating behavior with expected system performance. In order to achieve these goals and create a self-aware motion control system, it is necessary to create an adaptive motion control agent to monitor system operation and dynamically adjust its performance based on the working environment of the drive system. In ADI's view, a self-aware motion control system can be built by using an automatic 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, which all use the motion parameters of field-level drives. After the electrical and mechanical model of the drive system is derived, the model can be used to compare and adjust the expected system performance requested at the supervisory, planning, or management level of the automation system pyramid (as shown in Figure 1). When a new expected system performance is requested from any level above the supervisory part of the automation system pyramid, a new set of control parameters needs to be transmitted to the adaptive control part of the motion control system. The system then responds by adjusting its performance to match the new performance request.

Figure 1. Automation system pyramid

The two major benefits of implementing a self-aware motion control system are the ability to self-regulate and automatically maximize the performance of the motion control system in real time. This new capability provides opportunities for the supervision, planning, and management levels of the automation system pyramid, allowing self-aware motion control systems to be adjusted by implementing productivity enhancements. In addition, AI-enabled software algorithms can be used to adjust system performance to achieve better results factory-wide. For greater visualization, ADI has designed a self-aware motion control concept diagram to better understand the 4 basic elements required to implement a self-aware motion control system.

Self-aware motion control concept diagram: In order to achieve this level of self-aware motion control, a control system diagram needs to be developed. Figure 2 shows the four key elements required to successfully implement self-aware motion control.

Element I: Goal or Mission: A clear goal or mission needs to be established for the system. In the example, this means "move a beer glass from point A to point B in the best possible way without spilling any beer."

Element II: Intended System Behavior: With this goal in mind, the next level of the self-aware motion control graph initiates the intended motion behavior. In the beer glass example, this would be “use linear motion to move the beer glass while automatically adjusting its motion to compensate for different beer glass weights and sizes within the required mechanical system control safety limits.”

Once the goals and expected system behaviors are determined, the adaptive control engine enables dynamic actuation convergence between the core actuation system kinematics and its accompanying mechanical systems by automatically adjusting the motion control actuation and its integrated mechanical systems to achieve peak operating performance while operating in the unique operating environment.

Figure 2. Conceptual diagram of self-aware motor control

Element III: Core Drive System: At the heart of a self-aware motion control system is its kinematics. The challenge is to observe, learn, and monitor the performance levels of the motor and drive system. To create an effective model of the drive system, an intelligent observer needs to be implemented to gain a fundamental understanding of its motion parameters and its physical limitations. This can be achieved using a field-oriented controller (FOC) with dedicated position sensors or a sensorless FOC approach to understand how the motor is forced and reacts in the operating environment. The drive system response can be further optimized by monitoring and automatically adjusting the control parameter values ​​from the motor torque-flux current loop, velocity loop, and its positioning loop. After the datagrams of this information are collected and fed into the intelligent observer, an optimization algorithm is implemented to ensure that the motion control parameters are calculated and that the underlying motion control algorithm converges to form an optimal set of motion parameters (see Figure 3). Now that an indirect motion model has been created to model and optimize the motion of the drive system, the next level of self-aware motion control solutions can be implemented by introducing an adaptive control engine. The motion control values ​​are currently optimized with the TMCL-IDE automatic tuning motion control tool from Trinamic (now part of Analog Devices).

Figure 3. Monitoring and automatic adjustment of torque-flux current, speed, and position loops

Element IV: Adaptive Control: With the kinematics and FOC auto-tuning capabilities of the system in place, we can now focus on implementing the next level of self-aware motion control—the adaptive control engine. This level of intelligent motion focuses on communicating the desired system behavior to the adaptive control engine (see Figure 4). This system behavior is provided by production employees, factory supervisors, or generated based on artificial intelligence productivity algorithms that collect factory data in a network of smart sensors. Once the desired behavior is communicated to the adaptive control engine, the self-aware motion control system begins to dynamically reconfigure the drive system operating parameters to match the desired system behavior. Some examples of these desired behaviors include requesting an increase in factory throughput or extending the operating life of a motor by operating in a safe mode. As the motion control system automatically adjusts its motion control parameters to achieve this new requested performance level, the adaptive control system continuously monitors the closed-loop system to maintain its desired performance level. This state is maintained even if the drive system experiences changes due to wear in the mechanical system, or even if the motor operating environment changes. The system has now reached the final level of self-aware motion control.

Figure 4. Adaptive control model

Perhaps the best way to demonstrate this concept is to use a real-world example (see Figure 5). For example, a bartender wants to accurately deliver a full beer from the side of the bar to the customer without spilling a drop of beer in the process. How can this be achieved? If a self-aware motion control system is used, it becomes very simple. The goal of this task is to deliver beer from the bartender (point A) to the customer sitting at the bar (point B) in the fastest possible time without spilling the beer. The delivery system in this example is a coaster with a built-in weight detector that detects the weight of beer glasses of various sizes and uses linear motion to move the beer glasses across the bar. Imagine that a self-aware motion control system can deliver beer to customers in the fastest possible time, and if the customer puts the empty or half-empty beer glass back to the coaster so that it can be refilled or discarded, the system will automatically adjust its speed and performance. In addition, if the bartender uses different sized glasses to serve other types of drinks to customers, the system can also optimize efficiency.

Figure 5. Examples of real-world applications of self-aware motion control systems (different load masses)

Although it sounds a bit incredible, self-aware motion control technology is now evolving rapidly and will surely enter people's lives and work in the near future. Imagine that when the equipment in the entire factory uses self-aware motors and smart sensors, the smart factory will be far beyond people's imagination. By then, potential faults of factory workshop equipment can be self-repaired, the operating life of the equipment can be effectively extended, the production process can be automatically adjusted, and productivity can be maximized. Welcome to the exciting new world, come and truly experience ADI's self-aware motion control and the arrival of the next industrial revolution.