Custom Controllers

This guide covers advanced controller configuration and customization for the RCR Common Robotics Platform.

Overview

Custom controllers allow you to implement advanced control algorithms, optimize performance, and add new functionality to your robot.

Prerequisites

Understanding of control theory
Familiarity with ROS2 control
Knowledge of robot kinematics
Experience with C++ or Python

Controller Types

1. Motion Controllers

Differential Drive Controller:

Controls left and right wheels
Implements velocity commands
Handles odometry feedback

Omni-Drive Controller:

Controls multiple wheels
Implements holonomic motion
Advanced kinematics

2. Sensor Controllers

LiDAR Controller:

Manages LiDAR data
Implements filtering
Handles calibration

Camera Controller:

Manages camera data
Implements image processing
Handles calibration

Controller Development

1. Basic Controller Structure

C++ Controller:

#include <controller_interface/controller_interface.hpp>
#include <hardware_interface/types/hardware_interface_type_values.hpp>

class CustomController : public controller_interface::ControllerInterface
{
public:
  CustomController();
  ~CustomController();

  controller_interface::return_type init(
    const std::string & controller_name) override;

  controller_interface::return_type update() override;

private:
  // Controller implementation
};

Python Controller:

import rclpy
from controller_interface import ControllerInterface

class CustomController(ControllerInterface):
    def __init__(self):
        super().__init__()

    def init(self, controller_name):
        # Initialize controller
        pass

    def update(self):
        # Update controller
        pass

2. Controller Configuration

Controller Parameters:

custom_controller:
  ros__parameters:
    # Controller parameters
    update_rate: 100.0
    control_mode: velocity

    # PID parameters
    p_gain: 1.0
    i_gain: 0.0
    d_gain: 0.0

    # Safety parameters
    max_velocity: 1.0
    max_acceleration: 2.0

3. Controller Integration

Hardware Interface:

hardware:
  ros__parameters:
    joints:
      - left_wheel
      - right_wheel

    interfaces:
      - position
      - velocity
      - effort

Controller Manager:

controller_manager:
  ros__parameters:
    update_rate: 100

    controllers:
      custom_controller:
        type: custom_controller/CustomController

Advanced Control Algorithms

1. PID Control

PID Implementation:

class PIDController
{
public:
  PIDController(double p, double i, double d);
  double update(double error, double dt);

private:
  double p_gain_, i_gain_, d_gain_;
  double integral_, previous_error_;
};

PID Tuning:

Start with P gain only
Add I gain for steady-state error
Add D gain for stability
Tune for performance

2. Model Predictive Control

MPC Implementation:

Predict future behavior
Optimize control inputs
Handle constraints
Real-time optimization

3. Adaptive Control

Adaptive Algorithms:

Self-tuning controllers
Parameter estimation
Robust control
Learning control

Performance Optimization

1. Real-time Performance

Timing Requirements:

Control loop frequency
Latency constraints
Jitter minimization
Priority scheduling

Optimization Techniques:

Efficient algorithms
Memory management
CPU optimization
Real-time scheduling

2. Control Performance

Performance Metrics:

Tracking accuracy
Response time
Stability margins
Robustness

Optimization Methods:

Parameter tuning
Algorithm selection
Hardware optimization
System integration

Testing and Validation

1. Simulation Testing

Gazebo Simulation:

# Launch simulation
ros2 launch common_platform launch_sim.launch.py

# Test controller
ros2 run custom_controller test_controller

Controller Testing:

Unit tests
Integration tests
Performance tests
Safety tests

2. Hardware Testing

Safe Testing:

Start with low gains
Test in safe environment
Monitor system behavior
Have emergency stop ready

Validation Procedures:

Step response tests
Frequency response tests
Stability tests
Performance tests

Troubleshooting

Common Issues

Controller Not Starting:

Check configuration
Verify hardware interface
Check for errors
Review logs

Poor Performance:

Tune parameters
Check sensor data
Verify timing
Review algorithms

Instability:

Reduce gains
Check for delays
Verify system model
Review stability

Diagnostic Tools

Controller Monitoring:

# Monitor controller status
ros2 control list_controllers

# Check controller parameters
ros2 param list /controller_manager

# Monitor performance
ros2 run rqt_plot rqt_plot

Best Practices

Development

Code Quality:

Follow coding standards
Use version control
Document code
Test thoroughly

Safety:

Implement safety checks
Use appropriate limits
Handle failures gracefully
Test extensively

Deployment

Configuration Management:

Version control configurations
Document changes
Test in simulation first
Deploy gradually

Monitoring:

Monitor performance
Check for errors
Log important events
Maintain backups

For firmware updates, see Firmware Updates For troubleshooting, see Troubleshooting