Robotics is an interdisciplinary field that involves the integration of hardware and software to create intelligent machines that can interact with the physical world. Control systems are a critical component of any robotic system, as they provide the means to regulate the behavior of the robot in response to different inputs and environmental conditions. In this post, we will explore advanced control systems for robotics that can be implemented using Arduino, a popular microcontroller platform that is widely used in the maker and hobbyist communities.
PID Controllers
PID (proportionalintegralderivative) controllers are a type of feedback control system that are widely used in robotics and other engineering applications. A PID controller consists of three components: a proportional component, an integral component, and a derivative component. The proportional component provides a direct control signal that is proportional to the error between the desired and actual values of a particular variable, such as the robot's position or velocity. The integral component provides a signal that is proportional to the cumulative error over time, which helps to eliminate steadystate errors in the control signal. The derivative component provides a signal that is proportional to the rate of change of the error, which helps to reduce overshoot and improve the stability of the system.
Implementing a PID controller with Arduino involves writing a program that calculates the control signal based on the current error and the values of the three components. The program can be executed in realtime using the Arduino's builtin timers and interrupts, allowing the robot to respond quickly to changes in the environment or user inputs.
Code example:
// Set PID gains
float Kp = 1.0;
float Ki = 0.1;
float Kd = 0.01;
// Initialize PID variables
float error = 0;
float last_error = 0;
float integral = 0;
float derivative = 0;
float control_signal = 0;
// PID loop
while(true) {
// Read sensor value
float sensor_value = analogRead(A0);
// Calculate error
error = setpoint  sensor_value;
// Calculate integral
integral += error * dt;
// Calculate derivative
derivative = (error  last_error) / dt;
// Calculate control signal
control_signal = Kp * error + Ki * integral + Kd * derivative;
// Apply control signal
motor.setSpeed(control_signal);
// Update last error
last_error = error;
// Delay for sample time
delay(dt);
}
Kalman Filters
Kalman filters are a type of state estimator that can be used to estimate the true state of a system based on noisy sensor measurements. Kalman filters use a probabilistic model to represent the uncertainty in the system state and sensor measurements, and update the estimate of the system state based on new sensor readings. Kalman filters are widely used in robotics and other applications that require accurate state estimation.
Implementing a Kalman filter with Arduino involves writing a program that uses the Kalman filter algorithm to estimate the true state of the system based on the sensor measurements. The program can be executed in realtime using the Arduino's builtin timers and interrupts, allowing the robot to update its estimate of the state as new sensor measurements become available.
Code example:
// Set initial state estimate
float x_hat = 0;
float P = 1;
// Set Kalman filter gains
float Q = 0.1;
float R = 1;
// Kalman filter loop
while(true) {
// Read sensor value
float y = analogRead(A0);
// Predict state estimate
float x_hat_minus = x_hat;
float P_minus = P + Q;
// Calculate Kalman gain
float K = P_minus / (P_minus + R);
// Update state estimate
x_hat = x_hat_minus + K * (y  x_hat_minus);
P = (1  K) * P_minus;
// Apply control signal
motor.setSpeed(x_hat);
// Delay for sample time
delay(dt);
}
Neural Networks
Neural networks are a type of machine learning algorithm that are inspired by the structure and function of the human brain. Neural networks consist of interconnected nodes or "neurons" that perform simple computations on the input data, and produce an output signal based on the weighted sum of the neuron's inputs. Neural networks can be trained using a variety of techniques, such as supervised learning, unsupervised learning, and reinforcement learning, to perform a wide range of tasks, such as classification, regression, and control.
Implementing a neural network with Arduino involves writing a program that uses a pretrained neural network to control the behavior of the robot. The program can be executed in realtime using the Arduino's builtin timers and interrupts, allowing the robot to respond quickly to changes in the environment or user inputs.
Code example:
// Load pretrained neural network
NeuralNetwork nn = load_neural_network("my_network.json");
// Neural network loop
while(true) {
// Read sensor values
float input1 = analogRead(A0);
float input2 = analogRead(A1);
// Run neural network
float output = nn.predict({input1, input2});
// Apply control signal
motor.setSpeed(output);
// Delay for sample time
delay(dt);
}
Examples of Advanced Control Systems for Robotics with Arduino
Here are some examples of advanced control systems for robotics that can be implemented using Arduino:

Autonomous Navigation: A robot that can navigate autonomously using a combination of PID controllers and Kalman filters to control the motion and estimate the position of the robot.

Object Tracking: A robot that can track objects using a neural network to analyze the camera images and control the motion of the robot to keep the object in the center of the frame.

Robotic Arm Control: A robot arm that can be controlled using a combination of PID