A review of the effects of AI and ML in motion control, their benefits, real-world applications, and the challenges they present is of great value in priming the understanding of real-world events that are happening in real-time.
An AI and ML equipped world relies on an ocean of data, both historical (ML) and real-time (AI) to enable decision making that is fast, precise, adaptive and effective. Source: touseef/Adobe Stock
What is motion control?
Motion control is the kinetic and position management of the movement of machinery to achieve required real-world interpretations of virtual instructions in precise positioning, velocity, or force in equipment. The functional components required for this include actuators/drives, sensors, control modules, and feedback/modulation systems. In a typical operational setup, a controller interprets feedback from sensors, to modulate drive-induced movement in real-time, enabling equipment to operate with the required precision.
Historically, such systems have been static-rule based, using fixed algorithms to follow predetermined control commands. While this works well for simple and repetitive tasks, industrial processes of growing intricacy and even stability are increasingly requiring systems capable of agile and real-time adaptation to altered conditions (demanding local intelligence), combined with slower progression adaptive-learning from data enriched record of past experience (demanding a system that can learn from experience).
The role of AI and ML in motion control
Adaptive control
AI allows motion control systems to dynamically adjust to condition changes or variations in material properties and systems mechanical performance. In robotics, as the load on a motor changes when a pickup is overweight, or an obstruction impedes motion, AI algorithms can optimize the control parameters on-the-fly, maintaining smooth operation. This adaptive control reduces operational disruptions and productivity and can help in maintaining system components and reducing damaging stresses on drives.
Predictive maintenance
Precision and optimization
ML models operate by analyzing historical performance data (and real events) and the AI component can then interpret these datum points to learn to make fine model-adjustments to improve motion precision. In manufacturing, where micron-level precision is demanded, such adjustments can have a significant impact on product quality. AI-driven optimization typically helps balance multiple operational concerns - speed, power consumption, and accuracy/repeatability, resulting in optimized operations.
Energy efficiency
Motion control systems that rely on AI can be structured for concern about energy usage, adjusting system power and loads based on real-time conditions for particular optimizations that are imposed on it. For instance, machine learning models applied by artificial intelligence analysis can predict when it is possible to reduce motor power without sacrificing performance. This is particularly relevant in applications with very high energy demands, where small adjustments can deliver significant power savings.
Learning from experience
Unlike traditional systems that require human-intervention tuning, AI-powered motion control systems can integrate a higher level intervention in algorithm design that enables the system to learn from historical performance data and adjust automatically and in real-time, adapting to altered tasks or conditions without human intervention. For instance, a robotic loading system can develop optimized motion patterns and loading scenarios that rescue loading/unloading times for shipping containers.
Real-world applications of AI and ML in motion control
The integration of AI and ML into motion control is already taking place across most sectors. Real-world examples where these technologies are having a significant impact are commonplace
Industrial robotics
In heavy-engineering and light-assembly tasks, industrial robots are employed in repetitive actions like assembly, welding and material handling. AI and ML enable robots to learn from their task outcomes and adapt their motions accordingly. To illustrate, AI-enabled robots in automotive bodywork welding can adjust their actions in real time to accommodate variations in weldability that typically occur through long production runs, ensuring consistency of outcome in terms of structural integrity/strength without the need for human QA driven adjustments and potential rework.
Autonomous vehicles
Autonomous vehicles rely utterly on motion control and real-time analysis of wide-scale sensory inputs to manage speed, steering, braking and safety for occupants and surroundings. AI and ML algorithms are used to interpret sensor data, rapidly predict imminent future conditions, and adjust vehicle movements in real-time to adapt to incipient conditions, before the crisis. These systems allow vehicles to navigate fast, dynamic and danger laden environments, avoid obstacle impacts and optimize routing based on real-time conditions.
Drones and UAVs
AI and ML are key to controlling the flight of drones and unmanned aerial vehicles (UAVs), particularly in regulated and/or dynamic environments. These algorithms allow drones to maintain stability, adjusting to wind conditions, obstacles, and the need for complex maneuvers, without human input. ML-based models also help drones improve their performance by learning from past operational data and real-world outcomes, improving reliability under challenging operational conditions.
Precision agriculture
Agriculture is increasingly reliant upon AI-driven motion control for automated machinery such as automated weed controllers, pruning robots and harvesters. These machines can autonomously navigate fields for optimized paths, and adjust their operations based on terrain, growth-generated obstructions and crop conditions. AI helps ensure that the machinery moves efficiently and precisely for minimum plant impact, reduced fuel consumption and optimized yields.
CNC machining
CNC machines are universal in manufacturing in processing and shaping materials/components. AI and ML improve the precision of CNC machines by enabling the learning of optimal tool paths and cutting speeds through understanding of the data records about prior operational outcomes. This reduces material waste and enhances productivity and quality. Predictive maintenance empowered by ML ensures that machines operate predictably, without unplanned downtime.
Medical robotics
In healthcare, AI-powered motion control systems are used in both patient interaction and cobots to manage processes and real-world interactions. These systems must make dynamic and precise adjustments to movements based on real-time feedback from sensors, allowing for minimal disruption of surroundings/patient comfort, precision activities and interactions and optimized patient/environment outcomes. Machine learning algorithms enable machines to improve their interactions and techniques over time, learning from each operation and its real-world outcomes to deliver better patient interactions.
Benefits of AI and ML in motion control
The integration of AI and ML into motion control provides a variety of advantages that are context/application sensitive in their relevance:
Increased precision: AI-driven systems can make dynamic micro-adjustments based on real-time situational understanding, improving accuracy in precision-sensitive applications.
Adaptive response: AI allows motion control systems to be agile in response to changing environments, conditions and task demands, increasing the range of complexity of applications that can be automated.
Reduced downtime: Predictive maintenance modes minimize unplanned maintenance and reduce the impact of operational interruptions.
Enhanced efficiency: AI-optimized motion control systems can reduce energy consumption and improve throughput by optimizing adjustments of task performance.
Scalability: AI systems can scale to automate more complex or dynamically altered tasks, without requiring extensive manual tuning.
Learning capabilities: ML algorithms enable systems to improve detailed performance and planning/routing over time, interpreting the learning from past operations and their outcomes, without human intervention.
Challenges and considerations
While the benefits of AI and ML in motion control are significant, there are also challenges that must be accommodated:
Data dependency
AI and ML models require data oceans to learn/operate/interpret effectively. In motion control systems, gathering sufficient, high-quality data can be challenging, particularly in environments where sensor reliability and scope are limited and real world outcomes must be interpreted beyond simple motion/position.
Integration with existing systems
Many industries rely on legacy motion systems and these can be challenging or impossible to integrate with AI and ML technologies. Retrofitting existing equipment with the necessary sensors and processors can be expensive and time-consuming, and complete replacement is often commercially impractical and expensive when short-term issues dominate.
Complexity
AI and ML models can be complex and difficult to interpret, making it challenging for engineers and operators who are not algorithm designers to understand how the system is making decisions. This can make troubleshooting difficult in the event of errors or malfunctions, where an algorithm weakness is identified in terms of outcome failures without capacity for methodological analysis and revision.
Security risks
AI and ML systems are typically vulnerable to cyber-attacks, particularly as they tend to rely on connected systems for data processing. Robust cybersecurity is essential to prevent malicious tampering with AI algorithms controlling critical machinery but challenging to achieve and maintain.
The future of AI and ML in motion control
As AI and ML technologies continue to develop and become integrated more widely, their role in motion control will continue to expand and their complexity of operation will grow via;
Enhanced autonomy: Motion control systems are becoming fully autonomous, capable of self-redaction and even self-repair, up to a point, reducing the need for human intervention.
AI-driven design: AI could assist in the design of motion control systems, optimizing both hardware and software components for specific applications. This leads to a generational disconnect, as human design interaction diminishes and the capacity for human analysis becomes more distant.
Collaborative robotics: AI-powered cobots that work alongside humans benefit from enhanced motion control capabilities and breadth of sensory/data inputs as their complexity and task intricacy increase. The task is two-fold, improving safety and collaboration in shared workspaces and increasing productivity.
AI-integrated sensors: Advanced sensors powered by AI are already providing real-time data analysis, further improving the precision and efficiency of motion control systems by undertaking more of the processing at the data acquisition stage.
Conclusion
AI and ML are mid-flow in revolutionizing motion control by providing enhanced precision, agility, and efficiency. These technologies are already impacting fields ranging from manufacturing to autonomous vehicles, from healthcare to horticulture. While there are myriad challenges to overcome, such as data dependency, reliability of process and integration costs, the long-term benefits of AI and ML in motion control are substantial. The growth rate in innovative applications is accelerating and this is enabling performance/productivity/cost improvements across all sectors.
The evolving nature and penetration of automation embodied in AI-driven motion control is at the forefront of the dissemination of automation, offering a future where machines learn, adapt, and optimize without constant human intervention and human control becomes more specialized and disconnected from direct function, as AI designed AI control systems proliferate.
