A new simulation scheme for self-tuning adaptive control of robot manipulators

Robotica ◽  
1991 ◽  
Vol 9 (3) ◽  
pp. 335-339 ◽  
Author(s):  
Q. Wang ◽  
D. R. Broome
Keyword(s):  
Adaptive Control ◽  
Controller Design ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Degree Of Freedom ◽  
Dynamic Equations ◽  
Real Time Control ◽  
Highly Nonlinear ◽  
Self Tuning ◽  
Simulation Scheme

SUMMARYIn most dynamic adaptive control simulation of robotic manipulators, the Langrange–Euler (L–E) dynamic equations are first piecewise linearized about the desired reference and then discretized and rewritten in a state space form. This makes things very complicated and it is easy to make errors. What is more is that with a different reference this work must be done again. A new simulation scheme – Backward Recursive Self-Tuning Adaptive (BRSTA) – as it will be called, is suggested in this paper for adaptive controller design of robot manipulators. A two degree of freedom robot manipulator is used to verify the scheme in the condition of highly nonlinear and highly coupled system. A one degree of freedom robot manipulator is used for comparing both the forward and backward methods. The main advantages of this scheme include that it can be used for evaluating the self-tuning adaptive control laws and provide the initial process parameters for real-time control. And it is concluded here that the Newton–Euler (N–E) dynamic equations are equally well qualified as the Langrange–Euler (L-E) equations for the simulation of self-tuning adaptive control of robot manipulators.

A New Adaptive Control Approach for Robot Manipulators

1992 ◽  
Author(s):  
Jingsheng Ye
Keyword(s):  
Adaptive Control ◽  
Feedback Linearization ◽  
Robot Manipulators ◽  
Linearization Method ◽  
Dynamic Equations ◽  
Unknown Parameters ◽  
Control Approach ◽  
Highly Nonlinear ◽  
Hyperstability Theory ◽  
Model Reference Adaptive

Abstract Dynamic equations of robot manipulators are highly nonlinear with time-varying and unknown parameters. Using the model reference adaptive control (MRAC) technique, a control scheme based on hyperstability theory is developed for robot manipulators. A new adaptive algorithm is proposed for compensating the nonlinear term in the dynamic equations and for decoupling the dynamic interaction among the joints. The main feature of the approach is that the unknown parameters are not estimated separately, but the total influences due to the modeling errors and the disturbances can be directly compensated. Simulations show good results even for large variations of parameters. A comparison of this approach with the feedback linearization method (FLM) is also presented.

Globally exponential continuous controller/observer for position tracking in robot manipulators with hysteretic joint friction

Robotica ◽  
2009 ◽  
Vol 28 (5) ◽  
pp. 759-763 ◽  
Author(s):  
Srinivasulu Malagari ◽  
Brian J. Driessen
Keyword(s):  
Robot Manipulator ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Degree Of Freedom ◽  
Position Tracking ◽  
Observer Error ◽  
Joint Friction

SUMMARYIn this work, we present a continuous observer and continuous controller for a multiple degree of freedom robot manipulator with hysteretic joint friction. The fictitious hysteresis state is of course unknown to the controller and must be estimated. The joint velocities are assumed measured here. For this considered plant, we propose and present a continuous observer/controller that estimates or observes the hysteresis state and drives the position tracking error to zero. We prove that the combined tracking error and observer error converges to zero globally exponentially.

scholarly journals Self-Tuning Predictive Control of Nonlinear Servo-Motor

2010 ◽  
Vol 61 (6) ◽  
pp. 365-372 ◽  
Author(s):  
Vladimír Bobál ◽  
Petr Chalupa ◽  
Marek Kubalčík ◽  
Petr Dostál
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Laboratory Model ◽  
Servo Motor ◽  
Real Time Control ◽  
Highly Nonlinear ◽  
Model Control ◽  
Self Tuning

Self-Tuning Predictive Control of Nonlinear Servo-MotorThe paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model — Amira DR300.

Experimental Verification of Adaptive Dominant Type Hybrid Adaptive and Learning Controller for Trajectory Tracking of Robot Manipulators

2013 ◽  
Vol 25 (4) ◽  
pp. 737-747 ◽  
Author(s):  
Munadi ◽  
◽  
Tomohide Naniwa ◽  
Keyword(s):  
Adaptive Control ◽  
Motion Control ◽  
Trajectory Tracking ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Control Input ◽  
Dominant Type ◽  
Dynamical Parameters ◽  
Small Robot

This paper presents an experimental study to verify an adaptive dominant type hybrid adaptive and learning controller for acquiring an accurate trajectory tracking of periodic desired trajectory of robot manipulators. The proposed controller is developed based on combining the model-based adaptive control (MBAC), repetitive learning control (RLC) and proportionalderivative (PD) control in which the MBAC input becomes dominant than other inputs. Dominance of adaptive control input gives the advantage that the proposed controller could adjust the feed-forward motion control input immediately after changing the desired motion or load of the manipulator. In motion control law, the proposed controller uses only one vector to estimate the unknown dynamical parameters. It makes the proposed controller as a simpler hybrid adaptive and learning controller which does not need much computational power and also is easily be implemented for real applications of robot manipulators. The proposed controller is verified through experiments on a four-link small robot manipulator as representation of a scale robot manipulator to ensure this controller can be applied in the real applications of robot manipulators. The experimental results show the effectiveness of the proposed controller by indicating the position tracking error approaches to zero.

A New Lagrangian Formulation of Dynamics for Robot Manipulators

1989 ◽  
Vol 111 (4) ◽  
pp. 559-566 ◽  
Author(s):  
Chang-Jin Li
Keyword(s):  
Control System ◽  
Dynamic Simulation ◽  
Degrees Of Freedom ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Equations Of Motion ◽  
Lagrangian Formulation ◽  
Lagrangian Dynamics ◽  
Highly Nonlinear ◽  
Mathematical Operations

In this paper, a new Lagrangian formulation of dynamics for robot manipulators is developed. The formulation results in well structured form equations of motion for robot manipulators. The equations are an explicit set of closed form second order highly nonlinear and coupling differential equations, which can be used for both the design of the control system (or dynamic simulation) and the computation of the joint generalized forces/torques. The mathematical operations of the formulation are so few that it is possible to realize the computation of the Lagrangian dynamics for robot manipulators in real-time on a micro/mini-computer. For a robot manipulator with n degrees-of-freedom, the number of operations of the formulation is at most (6n2 + 107n − 81) multiplications and (4n2 + 102n − 86) additions; for n = 6, about 780 multiplications and 670 additions.

Design of a Robotic System that Plays with a Yoyo

1999 ◽  
Vol 11 (5) ◽  
pp. 387-392 ◽  
Author(s):  
Koichi Hashimoto ◽  
◽  
Toshiro Noritsugu
Keyword(s):  
Energy Balance ◽  
Controller Design ◽  
Robotic System ◽  
Energy Balance Model ◽  
Dynamic Equations ◽  
Balance Model ◽  
Dynamic Parameters ◽  
Parameter Change ◽  
Highly Nonlinear ◽  
Event Driven

Yoyo dynamics are discontinuous and highly nonlinear, and dynamic parameters are difficult to estimate precisely. Some parameters even change their sign at the loop bottom of yoyo's trajectory. Humans predict parameter change timing visually and sense yoyo dynamics through the fingers. Designing a robot that plays with a yoyo is difficult due to a lack of sensors and dynamic equations for sensing yoyo movement. We propose eventbased formulation for this based on an energy balance model and event-driven control. Simulations and experiments verified the validity of formulation and controller design.

Robust Controller Design for Robot Manipulators

1988 ◽  
Vol 110 (1) ◽  
pp. 94-96 ◽  
Author(s):  
T. Sugie ◽  
T. Yoshikawa ◽  
T. Ono
Keyword(s):  
Control Method ◽  
Controller Design ◽  
Robot Manipulators ◽  
Degree Of Freedom ◽  
Robust Controller ◽  
Two Degree Of Freedom ◽  
Robust Controller Design

In this paper we give a control method for robot manipulators which takes account of both the command response and the robustness in a systematic way by utilizing two-degree-of-freedom controller configuration. A simulation result is given to show the validity of our method.

scholarly journals Intelligent Controller Design for Quad-Rotor Stabilization in Presence of Parameter Variations

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Oualid Doukhi ◽  
Abdur Razzaq Fayjie ◽  
Deok Jin Lee
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Controller Design ◽  
Flight Period ◽  
Test Bed ◽  
Trajectory Tracking Control ◽  
Highly Nonlinear ◽  
Self Tuning ◽  
Aerial Vehicle ◽  
The Mathematical Model

The paper presents the mathematical model of a quadrotor unmanned aerial vehicle (UAV) and the design of robust Self-Tuning PID controller based on fuzzy logic, which offers several advantages over certain types of conventional control methods, specifically in dealing with highly nonlinear systems and parameter uncertainty. The proposed controller is applied to the inner and outer loop for heading and position trajectory tracking control to handle the external disturbances caused by the variation in the payload weight during the flight period. The results of the numerical simulation using gazebo physics engine simulator and real-time experiment using AR drone 2.0 test bed demonstrate the effectiveness of this intelligent control strategy which can improve the robustness of the whole system and achieve accurate trajectory tracking control, comparing it with the conventional proportional integral derivative (PID).

SELF-TUNING PID CONTROLLER DESIGN USING FUZZY LOGIC FOR A SINGLE-LINK FLEXIBLE JOINT ROBOT MANIPULATOR

2016 ◽  
Vol 78 (6-13) ◽  
Author(s):  
Ali Dehghani ◽  
Hamed Khodadadi
Keyword(s):  
Fuzzy Logic ◽  
Pid Controller ◽  
Controller Design ◽  
Robot Manipulator ◽  
Flexible Joint ◽  
Flexible Joint Robots ◽  
Pid Controller Design ◽  
Self Tuning ◽  
Intelligent Methods ◽  
Parameter Values

Although flexible joint robots are widely used in the industry, they are not without problems. It is especially so in their joints, links and complex dynamic where the interaction between loops, non-linearity, and flexibility in the joints can be difficult. The purpose of the present paper is to improve the tracking performance of flexible joint robots. Therefore the physical relations of the system dynamics need to be used to determine a non-linear model for the flexible joint robot. This paper attempts to achieve the desired performance flexible joint robot based on Fuzzy Logic Self-Tuning PID controller. Generally, the classic PID controller is different from the newly introduced form of PID. In classic PID, the parameter values are calculated based on various methods such as Ziegler-Nichols, while in fuzzy logic self-tuning PID, they are obtained by intelligent methods such as fuzzy logic. After deriving the system model, this logic self-tuning PID controller is designed in two cases: using error and its derivative and employing error and its integral for the inputs. The simulation results indicate that the proposed controllers can improve the overall efficiency of the system.

Design and Development of H∞ and μ-Synthesis Controllers for Nanorobotic Manipulation and Grasping Applications

2007 ◽  
Author(s):  
Reza Saeidpourazar ◽  
Beshah Ayalew ◽  
Nader Jalili
Keyword(s):  
Degrees Of Freedom ◽  
Controller Design ◽  
Equations Of Motion ◽  
Dynamic Equations ◽  
Robust Controller ◽  
Accuracy And Precision ◽  
Highly Nonlinear ◽  
Linearized Model ◽  
High Level ◽  
Robust Controller Design

This paper presents the development of H∞ and μ-synthesis robust controllers for nanorobotic manipulation and grasping applications. Here a 3 DOF (Degrees Of Freedom) nanomanipulator with RRP (Revolute Revolute Prismatic) actuator arrangement is considered for nanomanipulation purposes. Due to the sophisticated complexity, and expected high level of accuracy and precision (of the order of 10−7 rad in revolute actuators and 0.25 nm in the prismatic actuator) of the nanomanipulator, there is a need to design a suitable controller to guarantee an accurate manipulation process. However, structure of the nanomanipulator employed here, namely MM3A, is such that the dynamic equations of motion of the nanomanipulator are highly nonlinear and complicated. Linearizing these dynamic equations of the nanomanipulator simplifies the controller design process significantly. However, linearization could suppress some critical information about the system dynamics. In order to achieve the precise motion of the nanomanipulator utilizing the simple linearized model, H∞ and μ-synthesis robust controller design approaches are proposed. Following the development of the controllers, numerical simulations of the proposed controllers on the nanomanipulator are used to verify the positioning performance.

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