Typical Applications of Siemens PLC in Robot Control: From Modeling to Debugging

Self-Introduction Like an Old Friend

Hello everyone, I am an engineer with over ten years of experience in the industrial automation industry, specializing in PLC control and motion control systems. Throughout my work, I have encountered various automation devices, and what interests me the most is robot control systems, especially those based on Siemens PLCs. These projects are not only challenging but also greatly enhance production efficiency, making them a field where one can feel a strong sense of accomplishment.

Today, I would like to discuss the application of Siemens PLC in robot control, from modeling to debugging, combining my experiences and the pitfalls I have encountered to help everyone avoid detours. If you are new to industrial automation or want to gain a deeper understanding of robot control, this article will definitely be useful to you!

Main Content

1. Hardware Configuration and Environmental Requirements

In robot control projects, hardware selection is a key step, and choosing the right equipment can save a lot of trouble in subsequent work. Below is a list of hardware configurations I commonly use:

PLC Model We usually choose the Siemens S7-1500 series PLC, especially high-performance CPUs like CPU 1516-3 PN/DP. It has strong processing capabilities and rich communication interfaces, suitable for complex multi-axis motion control.
Communication Modules If your robot needs to communicate with other devices (such as HMI, upper computers, or sensors), Profinet or Profibus modules are essential.
Motion Control Modules Siemens provides TM Axis modules, specifically for motion control. These modules support encoder feedback, servo control, and other functions, enabling easy implementation of multi-axis linkage.
Servo Drives and Motors I recommend Siemens’ SINAMICS V90 drives paired with SIMOTICS S-1FL6 servo motors, which offer good stability and are relatively easy to debug.
Environmental Requirements

Programming Software: TIA Portal (recommended version V16 and above for richer functionality).
Robot Mechanical Structure: Three-axis or six-axis robotic arms, with specific configurations chosen based on actual needs.
Network: Ensure that the PLC, servo drives, HMI, and other devices are on the same industrial network; a Profinet switch is essential.

Tip: When selecting components, be sure to estimate the system complexity to avoid insufficient hardware performance or resource waste. For example, if the project involves multi-axis linkage and real-time calculations, be sure to choose high-performance PLCs and motion modules.

2. Core Principles and Design Ideas

The core of robot control lies in motion control and trajectory planning. In simple terms, we need to enable the PLC to control the motor through the servo drive to achieve precise positioning and path planning of the robotic arm.

Core Principles

Establishing Coordinate Systems

Typically, the base of the robotic arm is set as the origin, establishing the workpiece coordinate system and the tool coordinate system.
Siemens PLC supports G-code and various coordinate transformations, which is very useful in complex trajectory control.

Motion Modes

Point-to-Point Motion (PTP): The robotic arm moves quickly from one position to another, commonly used for simple tasks like picking and placing.
Interpolation Motion: Includes linear interpolation and circular interpolation, used for continuous motion along complex paths.
Multi-Axis Linkage: Ensure that all axes are synchronized to avoid trajectory deviations due to inconsistent speeds.

Feedback Control

The servo system monitors the motor position in real-time through encoder feedback, achieving closed-loop control to ensure high precision.

Design Ideas

The project design can be divided into the following steps:

Modeling Phase Obtain the motion parameters of the robotic arm (such as joint lengths and rotation angle ranges) during the mechanical design phase, and establish a mathematical model in the PLC program.
Task Decomposition Break down complex tasks into multiple basic movements, such as “grasp -> move -> place”.
Programming Use the motion control function blocks in TIA Portal (such as MC_MoveLinear, MC_MoveCircular) to implement path planning and motion control.

Tip: If the project involves multiple robots working together, remember to consider communication and coordination issues during the design phase.

3. Code Implementation and Technical Details

Below is a simple PLC program example for controlling the robotic arm to perform point-to-point motion:

// Define axis parameters
Axis1 := "X-axis";
Axis2 := "Y-axis";
Axis3 := "Z-axis";

// Initialize motion control
MC_Power(Axis := Axis1, Enable := TRUE);
MC_Power(Axis := Axis2, Enable := TRUE);
MC_Power(Axis := Axis3, Enable := TRUE);

// Point-to-point motion
MC_MoveAbsolute(Axis := Axis1, Position := 100.0, Velocity := 50.0, Acceleration := 10.0, Deceleration := 10.0);
MC_MoveAbsolute(Axis := Axis2, Position := 200.0, Velocity := 50.0, Acceleration := 10.0, Deceleration := 10.0);
MC_MoveAbsolute(Axis := Axis3, Position := 50.0, Velocity := 50.0, Acceleration := 10.0, Deceleration := 10.0);

Code Explanation:

<span>MC_Power</span> is used to initialize the servo axes.
<span>MC_MoveAbsolute</span> command implements point-to-point motion, setting the target position, speed, and acceleration.

4. Function Expansion Description

After implementing the basic functions, consider the following expansions:

Trajectory Optimization: Optimize the motion path through interpolation algorithms to reduce robotic arm jitter.
Visual Guidance: Combine industrial cameras to achieve precise positioning through communication between the PLC and the vision system.
Remote Monitoring: Use Siemens HMI or SCADA systems to monitor the robot’s operating status in real-time.

5. Practical Application Case

Once, we designed a robotic arm system for an automotive parts factory to pick parts from a conveyor belt and place them into processing equipment.

Challenges: The conveyor belt speed was uneven, requiring the robotic arm to adjust the picking position in real-time.
Solution: By communicating between the PLC and sensors, we obtained real-time part position data and dynamically adjusted the robotic arm’s motion trajectory. Ultimately, the system achieved an accuracy of ±0.1mm, and the client was very satisfied.

Tip: In practical applications, always consider environmental factors, such as vibration and temperature, that may affect the system.

6. Debugging Methods and Suggestions

Single-Axis Debugging Debug one axis at a time to confirm its position feedback and speed are normal.
Path Verification Use virtual simulation tools (such as SIMIT) to verify the robot’s motion trajectory to avoid collisions during actual operation.
Debugging Logs Include logging functionality in the program to record the operating status and fault information of the axes for easier troubleshooting later.

7. Common Problems and Solutions

Problem: Robotic Arm Motion is Not Smooth

Cause: Acceleration settings are too high or the servo motor gain has not been adjusted.
Solution: Optimize acceleration parameters and adjust servo gains.

Problem: Large Position Deviation

Cause: Encoder failure or feedback signal interference.
Solution: Check encoder wiring to ensure stable signals.

Conclusion: Summary of Experiences and Insights

Robot control is a complex yet enjoyable field, and the powerful motion control capabilities of Siemens PLC allow us to complete tasks more efficiently. In actual projects, I have learned one thing: Details Determine Success or Failure, and optimizing every parameter can lead to unexpected results.

If you are also interested in robot control, feel free to leave a message for discussion. I hope my sharing can help everyone avoid pitfalls and gain more!