PID++: A Computationally Lightweight Humanoid Motion Control Algorithm

Abstract

Currently robotic motion control algorithms are tedious at best to implement, are lacking in automatic situational adaptability, and tend to be static in nature. Humanoid (human-like) control is little more than a dream, for all, but the fastest computers. The main idea of the work presented in this paper is to define a radically new, simple, and computationally lightweight approach to humanoid motion control. A new Proportional-Integral-Derivative (PID) controller algorithm called PID++ is proposed in this work that uses minor adjustments with basic arithmetic, based on the real-time encoder position input, to achieve a stable, precise, controlled, dynamic, adaptive control system, for linear motion control, in any direction regardless of load. With no PID coefficients initially specified, the proposed PID++ algorithm dynamically adjusts and updates the PID coefficients Kp, Ki and Kd periodically. No database of values is required to be stored as only the current and previous values of the sensed position with an accurate time base are used in the computations and overwritten in each read interval, eliminating the need of deploying much memory for storing and using vectors or matrices. Complete in its implementation, and truly dynamic and adaptive by design, engineers will be able to use this algorithm in commercial, industrial, biomedical, and space applications alike. With characteristics that are unmistakably human, motion control can be feasibly implemented on even the smallest microcontrollers (MCU) using a single command and without the need of reprogramming or reconfiguration.

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

“…The specific problem of reducing the PID step overshoot response has been addressed by many approaches [ 12 ], such as by analyzing the relation between the location of the transfer function poles and the overshoot [ 23 ], by using sliding perturbation observers [ 24 ], by implementing a Tabu search algorithm [ 25 ], or by explicitly avoiding the overshoots [ 26 ]. The versatility of the basic core algorithm of the PID controller has fostered many enhancements [ 27 ] and its application to control dynamic systems [ 28 ]. For example, Podlubny [ 29 ] proposed a PID implementation involving a fractional-order integrator and a fractional-order differentiator (PI λ D μ ) in order to control systems that are better described by fractional-order mathematical models, a proposal that has been applied successfully to control a quadrotor unmanned aerial vehicle (UAV) [ 30 ] and to control the position of a micrometric linear axis [ 31 ].…”

Intelligent Parameter Identification for Robot Servo Controller Based on Improved Integration Method

“…In order to make the industrial robot capable of more complex work, the robot control technology requires to have the characteristics of high speed, high precision, multi-coordination and has good dynamic performance. In summary, finding a way to control industrial robotsin a way that makes them respond quickly to the predetermined trajectory instructions while maintaining a high dynamic tracking accuracy, aiming to achieve stable operation, has become an important research topic in the field of industrial robot motion control [ 1 , 2 , 3 , 4 , 5 , 6 ].…”

Low-Cost DC Motor Control System Experiments for Engineering Students

“…Each of the PID controller components affects the controller's response characteristics, such as its stability, responsiveness, and steady-state error. Figure 6 shows a basic PID algorithm block diagram, where r(t) is the desired setpoint, e(t) is the error signal, and the feedback signal is the process variable [13]. The general effects of each controller gain (Kp, Kd, Ki) on a closed-loop system are summarized in Table 2 In this laboratory exercise, the "guess and check" method is employed for PID tuning [12].…”