How to Build a Self-Balancing Robot: Arduino Nano & MPU6050

Unmanned vehicle navigation requires establishing a Cartesian coordinate system and calculating orientation angles. The Inertial Measurement Unit (IMU) consists of accelerometers and gyroscopes. Accelerometers measure acceleration and detect gravity to calculate roll and pitch angles but cannot measure yaw. They are sensitive to vibrations and rapid movements. Gyroscopes measure angular velocity in all three axes, providing 6 degrees of freedom when combined with accelerometers. However, gyroscopes suffer from drift, where small measurement errors accumulate over time, causing calculated angles to drift away from true values.

The IMU (Inertial Measurement Unit) contains two sensors: the accelerometer measures linear force exerted on the quad (linear acceleration), and the gyroscope measures rotational motion. Together, they measure linear and rotational forces but do not know the absolute position or orientation (whether the quad is upright or upside down).

IMU (Inertial Measurement Unit) is a class of semiconductor electronic devices containing mechanical components, primarily including MEMS accelerometers and gyroscopes. Accelerometers measure linear acceleration using a proof mass suspended on springs, where displacement caused by acceleration changes capacitance between movable and fixed plates, generating a digitized signal. Gyroscopes measure angular velocity by forcing a proof mass into oscillation and detecting Coriolis forces that deflect the mass when the device rotates, also using capacitive sensing. Key parameters for IMU selection include sampling frequency (typically ≥1 kHz), resolution, maximum allowable values, sensitivity, zero offset (bias), stability over time and temperature, and noise characteristics. These sensors form the basis of AHRS (Attitude and Heading Reference Systems) that process raw sensor data to provide orientation angles, with Kalman filtering used to fuse sensor data and maintain accuracy even when GPS signals are lost.

Inertial Measurement Units (IMUs) combine three types of sensors: accelerometers measure linear acceleration and detect orientation by sensing gravitational forces (answering 'which way is down'), gyroscopes (actually angular rate sensors) measure rotational speed around axes, and magnetometers determine direction by detecting Earth's magnetic field; these MEMS-based sensors are digital, three-axis devices prone to temperature variations and environmental interference that require calibration for accurate readings.

An Inertial Measurement Unit (IMU) integrates three fundamental motion sensors—accelerometers, gyroscopes, and magnetometers—to enable comprehensive motion tracking. Accelerometers measure linear acceleration, detecting forces like gravity (1g when stationary) and registering zero g during free fall. Gyroscopes specialize in rotational sensing, maintaining accurate orientation measurements without human-like disorientation. Magnetometers detect magnetic fields, with compasses representing their primary navigational application for determining direction. Together, these sensors form the foundation of modern motion sensing technology, enabling features like screen orientation, heading detection, and gesture recognition through sophisticated mathematical combination.

This section introduces DC motor control using Arduino, explaining that skills learned for controlling LEDs directly apply to motor control. The H-Bridge module (Ponte H) simplifies motor control by handling complex circuitry internally. The module has four central control pins (IN1, IN2, IN3, IN4) where each motor requires two pins: connecting one pin causes rotation in one direction, connecting the opposite pin causes reverse rotation, and connecting both or neither stops the motor. Additionally, enable pins (ENA, ENB) control whether motors are active or disabled.

This section introduces DC motor control using the L293D H-bridge driver chip. The instructor explains that DC motors are common in toys due to their low cost and simple operation. The L293D chip enables control of both motor speed and direction, which is essential for applications like robotics. Key concepts covered include locating the chip's notch for pin identification, understanding the pin layout where pins 1-8 are counted left-to-right downward and pins 9+ right-to-left upward, and recognizing that the chip has identical configurations on both sides for controlling two motors. The instructor emphasizes never connecting motors directly to Arduino pins as this can damage the microcontroller, requiring instead an external power supply connected through the VCC and ground pins.

A simple circuit can be built to control the rotation speed of a DC motor commonly found in toy cars and CD/DVD/Blu-ray players. The circuit allows users to easily adjust how fast the motor spins.

DC motors are simple to control through voltage adjustment for speed and polarity reversal for direction. While permanently reversing wires works for static applications, automatic control requires the H-bridge circuit. The simplest H-bridge uses two relays with two push buttons (CW and CCW), a DC motor, and appropriate power sources. The control circuit can use 5V while the motor uses higher voltage like 12V. This configuration allows reversible motor control for applications like conveyor belts.

This segment introduces DC motor control for robotics, explaining why standard motor drivers like the L298N are insufficient for large motors due to current limitations and inefficiency. The presenter covers H-bridge fundamentals, showing how four switching devices control motor direction by reversing current polarity. The key distinction between bipolar transistors (0.7V drop each, total 1.4V loss) and MOSFETs (0.1V drop each, total 0.2V loss) is explained, demonstrating why MOSFET-based controllers like the Cytron MD10C offer superior efficiency. The MD10C specifications are detailed: 5-30V operation, 13A continuous/30A peak current, no heatsink required, and 20kHz maximum PWM frequency. Practical connections are shown: Arduino ground to motor controller ground, PWM pin 3 to speed input, pin 2 to direction control, and power supply connections. The segment establishes foundational concepts for understanding advanced motor control systems.

A feedback loop continuously measures output using sensors, compares it to a set point, and makes corrections through a repeating cycle of measure, compare, and correct. PID control combines three actions: Proportional reacts to current error distance, Integral addresses accumulated error over time, and Derivative responds to rate of change. Together they reduce deviation from the set point. Proportional alone causes oscillation with overshoot and undershoot. Integral gradually eliminates steady-state error by tracking accumulated offset. Derivative anticipates overshoot by reacting to approaching changes. Modern controllers can auto-tune to optimize these parameters automatically.

PID (Proportional-Integral-Derivative) control is a versatile feedback controller that uses three components to drive system error toward zero: the proportional component uses present error to generate immediate corrective action, the integral component accumulates past error over time to eliminate steady-state error, and the derivative component predicts future error based on the rate of change to prevent overshooting. This combination makes PID the most prevalent form of feedback control for physical systems because it addresses the limitations of using only proportional or integral control alone.

A feedback controller measures the control variable and uses this measurement to adjust the system. Basic components include: process, transmitter/sensor, controller, and final control element. Proper notation uses solid lines for system signals and dashed lines for electrical signals. For flammable processes, IP transducers convert electrical signals to pneumatic signals. PID controllers consist of Proportional (P), Integral (I), and Derivative (D) components. A proportional controller generates output proportional to current error: Output = Steady State + KC × Error. Key characteristics: fast response but cannot eliminate steady-state offset. Higher KC reduces offset but increases oscillation risk. The transfer function is P(s)/E(s) = KC. The controller has physical saturation limits that prevent infinite response.

This section covers the foundational concepts of feedback control systems and introduces PID controllers. Key topics include: (1) The three-stage process of system control: modeling (creating transfer functions), analysis (examining transient and steady-state responses), and control (implementing controllers); (2) The feedback control system structure with error signal generation (e = r - c); (3) Desired system performance characteristics: stability, good transient response (minimal overshoot, short rise and settling times), and zero steady-state error; (4) Introduction to the three basic PID control actions: proportional, integral, and derivative, which form the basis for advanced control system design.

Control theory is the discipline of getting electromechanical systems to respond as intended, using feedback loops and control algorithms. A PID controller combines three components: Proportional (responds to current error), Integral (accumulates past errors over time), and Derivative (responds to rate of change). Unlike simple on/off control, PID enables nuanced responses by considering how far off a system is from its target, how long it has been off, and how quickly it is changing. Real-world applications include household thermostats maintaining temperature through continuous adjustment rather than binary switching.