Optimal PID Control of a Brushed DC Motor with an Embedded Low-Cost Magnetic Quadrature Encoder for Improved Step Overshoot and Undershoot Responses in a Mobile Robot Application

Abstract

The development of a proportional–integral–derivative (PID) control system is a simple, practical, highly effective method used to control the angular rotational velocity of electric motors. This paper describes the optimization of the PID control of a brushed DC motor (BDCM) with an embedded low-cost magnetic quadrature encoder. This paper demonstrates empirically that the feedback provided by low-cost magnetic encoders produces some inaccuracies and control artifacts that are not usually considered in simulations, proposing a practical optimization approach in order to improve the step overshoot and undershoot controller response. This optimization approach is responsible for the motion performances of a human-sized omnidirectional mobile robot using three motorized omnidirectional wheels.

Non-Parametric Calibration of the Inverse Kinematic Matrix of a Three-Wheeled Omnidirectional Mobile Robot Based on Genetic Algorithms

“…where ∆t is the sampling time at which the angular velocities of the wheels of the robot are updated and k is the current discrete sample number that describes a time elapsed t(k) (where t(k) = ∆t•k) since the initialization of the robot. In the case of the APR-02, this sampling time coincides with the sampling time used internally by the three proportional, integral, and derivative (PID) controllers (∆t = 10 ms) [29] of the three brushed direct current motors (BDCM) driving its three omnidirectional wheels.…”

“…The inclusion of an onboard high-end computer in the following prototypes allowed the development of autonomous applications, e.g., supporting and guiding older people with mobility impairments [25], and an early gas leak detection system [26]. The main characteristic of this assistive mobile robot [27] is the use of an omnidirectional motion system based on three optimal omnidirectional wheels [28] driven by three brushed direct current motors (BDCM) [29]. The odometry of the robot is calculated from the information provided by the encoders of the motors.…”

SLAMICP Library: Accelerating Obstacle Detection in Mobile Robot Navigation via Outlier Monitoring following ICP Localization

“…This mobile robot has been used to validate practical applications such as early gas leak detection [ 68 ] and enhance the sense of attention [ 69 ]. The robot has also been used as a platform test bench for alternative designs of omnidirectional wheels [ 70 ] and PID wheel control tuning [ 71 ]. The mobile robot embeds different sensors and actuators [ 67 ]: one 2D Hokuyo UTM-30 LIDAR for self-localization, one panoramic RGB camera on top of its head, three RGB-D Creative Senz3D cameras used for human interaction and ground validation, 8 fixed infrared distance detectors are used to provide interaction when the LIDAR is powered off; 16 passive infrared detectors are used to detect human activity and occupancy; 8 digital servomotors are used in the arms for gesticulation; redundant ambient sensors are used; and one touch screen is used to display an animated face and other interaction information.…”

Internet of Robotic Things (IoRT) and Metaheuristic Optimization Techniques Applied for Wheel-Legged Robot

“…The output of the additional controller is the reference speed value. The output is computed based on the position error, defined as the difference between the reference and measured values [42,43]. The speed and position in the analyzed control system was measured using four incremental encoders connected to the Raspberry Pi Pico microcontroller.…”