Self-Balancing Robot with Arduino and MPU-6050: Full Tutorial

This comprehensive section covers the complete foundation of DC motor control using H-bridge circuits and PWM. The H-bridge uses four switches to direct current through the motor in either direction, enabling bidirectional rotation. The L293D motor driver IC implements this circuit with separate power pins for motor (up to 36V) and logic (5V), containing two independent H-bridges. PWM (Pulse Width Modulation) controls motor speed by rapidly switching digital signals on and off, where duty cycle determines average voltage. ATmega microcontrollers generate PWM using built-in timers controlled by the TCCR0 register, with Fast PWM mode preferred for motor control. Key configuration includes clock source selection, waveform generation mode, and output compare behavior. Pull-up resistors prevent false triggering from motor noise.

PWM (Pulse Width Modulation) is primarily used for driving motors. Brush-style motors require only one PWM channel, while brushless DC motors need three phases generated by three PWM signals. The robot uses H-bridge motor drivers that enable bidirectional motor control, with each motor requiring its own H-bridge. The H-bridge can drive motors up to 2 amps maximum on a 24V power source. The direction pin controls motor rotation direction: high voltage produces positive direction, low voltage produces negative direction. PWM can be applied to the direction line for linear motor control, allowing proportional current and direction control rather than simple on/off switching.

An H-bridge is a four-switch circuit configuration (typically using NPN transistors or MOSFETs) that enables bidirectional control of DC motors by reversing voltage polarity; it consists of four switches arranged in an H-shape with the motor at the center, where applying HIGH to opposite pairs of switches causes current to flow in one direction through the motor, while applying HIGH to the other pair reverses the current direction, and Pulse Width Modulation (PWM) can be applied to control motor speed by varying the duty cycle of the switching signals.

The UC1637 is an analog PWM control IC for DC motor drives, featuring a triangular wave generator and error amplifier that compares reference voltage with triangular waves to generate PWM signals. H-bridge motor control operates in three modes based on dead-time requirements: no dead-time mode for direct drive, dead-time with crossing for bidirectional current, and dead-time without crossing for safety. H-bridges can be controlled using two PWM signals with an op-amp to generate complementary signals, preventing shoot-through by ensuring one switch turns off before another turns on. A case uses single PWM for maximum torque/stiffness, while B case uses dual PWM for flexible speed/direction control.

This section covers DC motor speed control using PWM (Pulse Width Modulation) and H-bridge circuits. For DC motors with permanent magnets, PWM switches the full supply voltage on and off rapidly, with the average voltage proportional to the duty cycle. When the transistor is on, full voltage reaches the motor with minimal power loss; when off, no current flows. This switching action eliminates the continuous power loss of linear regulation. H-bridges use four transistors to control both speed and direction by activating different transistor pairs to reverse current flow through the motor. Integrated H-bridge controllers combine PWM control with direction control, available as ready-made modules that can be controlled by analog potentiometers or digital signals from microcontrollers.

A MEMS Inertial Measurement Unit (IMU) measures an object's acceleration and angular velocity in three-dimensional space using micro-electromechanical sensors; accelerometers detect linear acceleration by measuring capacitance changes caused by the deflection of a movable mass within spring-mounted comb structures, while gyroscopes measure angular velocity through the Coriolis effect, where oscillating masses experience lateral displacement when rotation is applied, allowing the system to calculate real-time attitude information including pitch, roll, and yaw for navigation and stability control in aerospace, defense, and robotics applications.

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).

An Inertial Measurement Unit (IMU) is an electronic device that measures a body's specific gravitational force, angular rate, and orientation. IMUs combine accelerometers (measuring linear acceleration) and gyroscopes (measuring angular rotation). Classification includes: 3-axis IMU (only accelerometer), 6-axis IMU (accelerometer + gyroscope), and 9-axis IMU (adds magnetometer). Acceleration is defined as change in velocity normalized by time, typically measured in 'g' units (1g = 9.8 m/s²). MEMS accelerometers use capacitive sensing: a spring-mass system where displacement due to acceleration changes capacitance between fixed and mobile plates, enabling acceleration measurement through electrical signal conversion.

Inertial measurement units (IMUs) combine accelerometers and gyroscopes to track motion and orientation. Six-axis IMUs provide linear acceleration (X, Y, Z) and angular rotation rates (roll, pitch, yaw). Nine-axis IMUs add magnetometers to correct for drift in the gyroscopic measurements. Gyroscopes measure rotational velocity, while accelerometers measure linear acceleration. Magnetometers provide absolute orientation references by detecting Earth's magnetic field. Software algorithms fuse these sensor readings to compute stable orientation estimates, with magnetometers preventing long-term drift that occurs when integrating angular rates over time.

PID (Proportional-Integral-Derivative) control is a fundamental feedback control mechanism used in engineering systems to automatically adjust outputs based on the difference between desired and actual values; it works by combining three correction terms—proportional to current error, integral to accumulated past error, and derivative to predicted future error—each scaled by tunable gains (KP, Ki, KD), enabling systems to meet performance requirements despite disturbances or variations in operating conditions.

This segment introduces PID control as a solution for stabilizing unstable plants. The instructor explains that while proportional control alone failed to stabilize the plant 1/(s-2), adding integral and derivative actions provides more flexibility. The PID controller combines three components: proportional (KP), integral (KI/s), and derivative (KD*s) into a unified transfer function. The mathematical derivation shows how these components combine into (KD*s^2 + KP*s + KI)/s, which can then be incorporated into the closed-loop system analysis.

This comprehensive section establishes the theoretical foundation for understanding control systems and PID controllers. It begins by contrasting open-loop systems (no feedback) with closed-loop systems that use sensors to measure actual output and compare it with desired values. The instructor explains the block diagram representation showing how inputs relate to outputs through the plant, with the error signal driving the controller's corrective action. The core PID theory is presented: proportional action responds immediately to current errors, integral action accumulates past errors to eliminate steady-state offset, and derivative action predicts future errors based on rate of change. The video emphasizes that PID controllers are versatile tools applicable to most systems with relative simplicity, though proper gain tuning is essential for optimal performance. This theoretical groundwork prepares viewers for the practical implementation demonstrated in subsequent sections.

PID control is a fundamental method for maintaining process variables at desired set points through closed-loop feedback. The system consists of a process (like a vessel with liquid inflow/outflow), sensors (transmitters measuring process variables), actuators (control valves adjusting flow), and a PID controller that compares set points to actual values and adjusts outputs accordingly. The controller uses three mathematical functions: Proportional (responds to current error), Integral (eliminates steady-state offset by accumulating past errors), and Derivative (prevents overshoot by predicting future error trends). Proper tuning of these parameters determines control quality, achieved through methods like trial-and-error, Ziegler-Nichols formulas, or automated software. Industrial implementations range from standalone panel-mounted controllers to software-based PLC systems integrated with broader process control networks.

Feedback control systems maintain desired operating conditions by continuously comparing the actual process value (PV) with a setpoint (SV) and adjusting the control output accordingly. The basic feedback loop consists of a sensor measuring PV, a controller processing the error signal (SV-PV), and an actuator (like a valve) implementing corrections. PID control is the fundamental technique for maintaining process variables such as temperature or pressure at desired levels. The controller uses three mathematical operations—proportional, integral, and derivative—to calculate the appropriate actuator position. Understanding these three components is essential for designing effective control strategies in industrial automation and process control applications.