How MCUs improve system performance in robot motor control designs


Robotic systems can automate repetitive tasks, undertake complex and laborious operations, and work in environments that are dangerous or harmful to humans. More integrated and higher-performance microcontrollers (MCUs) enable greater power efficiency, smoother and safer motion, and greater accuracy, resulting in increased productivity and automation. For example, higher accuracy (sometimes within 0.1mm) is important for applications dealing with laser welding, precision coatings or inkjet or 3D printing.

The number of axes of the robotic arm and the type of control architecture required (centralized or distributed) determine the appropriate MCU or motor control integrated circuit (IC) for the system. Modern factories use a combination of robots with different numbers of axes and degrees of freedom of motion (moving and rotating in the x, y or z plane) to meet the needs of different manufacturing stages; therefore, different control architectures are used throughout the factory floor.

When selecting an MCU, choose one with additional performance headroom to enable scalability and support for additional functionality in the future. Planning ahead for scalability and additional functionality during the design process can also save cost, time, and reduce complexity.

This article will explore two motor control architectures, centralized and distributed (or decentralized), and the design considerations for integrated real-time MCUs implementing both architectures.

Centralized architecture

In a centralized system, one MCU is used to control multiple axes. This approach effectively solves thermal problems in higher power motor drives (typically over 2kW to 3kW) that require large heat sinks and cooling fans. In this architecture, position data is typically obtained externally via a resolver board or aggregator connected to the encoder.

Typically, in this architecture, multiple power stages are on the same PCB or in close proximity, so one MCU can control multiple axes. This approach simplifies real-time control and synchronization between multiple axes because long communication lines between multiple motor control MCUs are not required.

A motor control MCU/MPU in a centralized architecture requires a high-performance real-time processing core (such as the R5F core or DSP), a real-time communication interface (such as EtherCAT), sufficient PMW channels, and peripherals for voltage and current sensing. MCUs like the AM243x enable scalable multi-axis systems, providing real-time control peripherals for up to six axes and enabling real-time communications in a single chip.

In the past, FPGA or ASIC devices were primarily used for centralized motor control in automation systems. However, modern Arm Cortex-based MCUs such as the AM243x have become increasingly popular in recent years. These MCUs are highly integrated and cost-effective, helping designers meet the performance requirements of their systems while enabling design scalability and flexibility.

While centralized control architectures can meet the performance and efficiency design requirements of high-power automation systems such as heavy-payload industrial robots, these systems require the use of additional cables, mechanical motors connecting cabinets and joints, and position sensors and aggregators. Not only are these wires expensive, they also tend to wear out and require maintenance.

Figure 1: Block diagram of a decentralized motor control architecture for multi-axis systems

Decentralized or distributed architecture

Recently, decentralized or distributed architectures (Figure 2) have become increasingly popular for systems with lower power requirements and have become a standard approach for collaborative robot manipulators.

The decentralized architecture integrates multiple single-axis motor drives into each joint of the robot, connected and synchronized through real-time communication interfaces such as EtherCAT. Typically each drive controls one axis and handles certain safety functions locally. Therefore, each MCU requires real-time control and communication capabilities, single-axis motor control peripherals, three to six PWM channels, on-chip successive approximation register analog-to-digital converters, or delta-sigma modulator inputs.

In these applications, the position sensor is usually located close to the MCU, so these MCUs require a digital or analog interface to read the position sensor data. Although this architecture requires more MCUs, system-level costs can be significantly reduced because less wiring is required between the power bus and communication interfaces. Modern real-time MCUs such as the F28P65x integrate not only all necessary peripherals, but also safety peripherals, thus providing a single or two-chip solution for the integration axis in a distributed architecture and achieving high performance in a smaller form factor .

Figure 2: Block diagram of a decentralized motor control architecture for a single-axis system


While electric motors may not be the hottest choice in robotics right now (especially compared to AI-enabled systems), they are the muscle that keeps factories running and are a vital part of modern manufacturing, so choosing the right ones A lot of considerations need to be made when controlling the device. As the integration of these devices increases, additional capabilities such as edge computing and wireless connectivity may be incorporated into motor control designs.