Achieving Coordinated Control of Multi-Degree-of-Freedom Robotic Arms with Industrial PCs

As an engineer with over ten years of experience in the field of industrial robot control, today I will detail how to achieve high-precision coordinated control of multi-degree-of-freedom robotic arms using industrial PCs, helping you master this key technology.

1. Introduction to Motion Control Library

In the control of multi-degree-of-freedom robotic arms, we primarily use the CODESYS SoftMotion library as the core control component. This is a motion control library specifically designed for industrial automation, featuring the following characteristics:

• • Supports synchronous control of up to 32 axes

• • Provides complete kinematic and dynamic calculation functions

• • Built-in various interpolation algorithms (linear, circular, spline, etc.)

• • Excellent real-time performance, with control cycles as low as 1ms

• • Supports mainstream industrial buses such as EtherCAT and PROFINET

The greatest advantage of the SoftMotion library lies in its modular design and open architecture, allowing developers to flexibly implement complex motion control requirements.

2. System Configuration Requirements

To build a stable robotic arm control system, the following hardware requirements must be met:

• • Industrial PC: Intel i5 or higher processor, 8GB or more RAM

• • Real-time operating system: WinPLC or real-time Linux

• • EtherCAT master card

• • Servo driver: supports EtherCAT protocol

• • Encoder: high-precision absolute encoder

Software environment configuration steps:

1. 1. Install CODESYS development environment (version 3.5 or higher)

2. 2. Import SoftMotion library

3. 3. Configure EtherCAT network parameters

4. 4. Set real-time task priority

3. Basic Control Implementation

To implement basic control of the robotic arm, the following core knowledge must be mastered:

// 1. Axis group initialization
VAR
    AxesGroup: MC_AxesGroup;
    Power: ARRAY[1..6] OF MC_Power;
    HomeAsync: ARRAY[1..6] OF MC_HomeAsync;
END_VAR

// 2. Axis enable code
FOR i:= 1 TO 6 DO
    Power[i].Enable := TRUE;
    Power[i].Axis := Axes[i];
END_FOR

// 3. Position control example
MoveAbsolute(
    Axis:= Axes[1],
    Position:= 45.0,
    Velocity:= 10.0,
    Acceleration:= 50.0,
    Deceleration:= 50.0
);

Key control points:

1. 1. Establish the D-H parameter model of the robotic arm

2. 2. Implement forward/inverse kinematics algorithms

3. 3. Configure PID control parameters

4. 4. Implement synchronized motion of the axis group

5. 5. Implement collision detection algorithms

4. Advanced Application Techniques

In actual industrial applications, we need to consider more complex control scenarios:

// Multi-axis collaborative control example
GroupMoveLinear(
    AxesGroup:= AxesGroup,
    Position:= TargetPos,
    Velocity:= 100.0,
    Acceleration:= 500.0,
    CoordinateSystem:= mcACS
);

// Trajectory planning optimization
PathPlanning.EnableLookAhead := TRUE;
PathPlanning.CornerDeviation := 0.1;
PathPlanning.MaxCornerVelocity := 50.0;

Advanced application points:

• • Implement adaptive feedforward control

• • Vibration suppression algorithms

• • Trajectory optimization and smoothing

• • Dynamic compensation

• • Automatic calibration of tool coordinate systems

Best practice recommendations:

1. 1. Use predictive algorithms to improve trajectory accuracy

2. 2. Implement load adaptation

3. 3. Establish a detailed error handling mechanism

4. 4. Optimize acceleration and deceleration curves

5. Conclusion and Outlook

Achieving coordinated control of multi-degree-of-freedom robotic arms using industrial PCs is a complex and challenging task. With the development of artificial intelligence technology, robotic arm control will evolve towards intelligence and adaptability in the future. Mastering these core technologies will open new opportunities for you in the field of industrial automation.