Trajectory Control Of Robot Manipulators Research Articles

Sectorial Fuzzy Controller Plus Feedforward for the Trajectory Tracking of Robotic Arms in Joint Space

In this paper, we propose a Sectorial Fuzzy Controller (SFC) with a feedforward compensation of the robot dynamics in joint space, evaluated at the desired angular positions, velocities, and accelerations, applied to the trajectory tracking of all revolute joints robotic arms. Global uniform asymptotic stability proof applying the direct Lyapunov theorem, is introduced for this new control scheme by using a strict Lyapunov function. This strict Lyapunov function is the first one within the field of fuzzy control that is applied to the trajectory control of robotic manipulators. With this strict Lyapunov function, a sensitivity analysis was also computed for this novel control scheme. Additionally, physical and simulation experimental results are given in comparison to the original control scheme, in which this new controller is inspired: the Proportional-Derivative (PD) controller plus feedforward compensation. The experimental results yielded better performance for the new fuzzy control scheme when compared to the classical structure, in both the joint position errors for similar or smaller values of applied torques, showing the expected tolerance to parametric deviations and uncertainties that all fuzzy controllers possess.

Adaptive-Sliding Mode Trajectory Control of Robot Manipulators with Uncertainties

In this paper, we propose and demonstrate an adaptive-sliding mode control for trajectory tracking control of robot manipulators subjected to uncertain dynamics, vibration disturbance, and payload variation disturbance. Throughout this work we seek a controller that is, robust to the uncertainty and disturbance, accurate, and implementable. To perform these requirements, we use a nonlinear Lyapunov-based approach for designing the controller and guaranteeing its stability. MATLAB-SIMULINK software is used to validate the approach and demonstrate the performance of the controller. Simulation results show that the derived controller is stable, robust to the disturbance and uncertainties, accurate, and implementable.

Fuzzy Sliding Mode Controller (FSMC) with Global Stabilization and Saturation Function for Tracking Control of a Robotic Manipulator

This paper proposes development of an intelligent algorithm naming Fuzzy Sliding Mode Controller (FSMC) with global stabilization and saturation function for the trajectory control of robotic manipulators. A new exponential dynamic sliding function with PID has been developed. Fuzzy tuned gain of SMC is taken in this paper, which has chattering in it. This chattering effect of FSMC has been removed by using saturation function in place of signum function in FSMC. This FSMC with saturation function has reduced error and chattering. Various conventional techniques like Proportional Derivative (PD), Computed Torque Controller (CTC) and Sliding Mode Control (SMC) have been reviewed and compared with the proposed control scheme. From the results, it can be said that the proposed controller scheme can assure robustness against a continuous disturbance with chattering elimination.

A Study on the Class of Model Feedback Controllers Formultiple-Input/Multiple-Output Non-Minimum Phase System

In the present paper, we examine model feedback control systems (MFCSs). BecauseMFCS is simple, the MFCS has been applied in many applications such as the trajectorycontrol of robot manipulators, serially connected water tanks, etc. The control structure of theMFCS is limited, but Yamada and Moki reported about whether or not MFCS can representall stabilizing controllers of a minimum phase plant. However, no research has been reportedwhether or not MFCS can represent all stabilizing controllers of a non-minimum phase plant.The purpose of the present paper is to give a solution to the question as to whether or not allstabilizing controllers for a plant are expressible in the MFCS structure. The relation betweenMFCS and the parameterization of all stabilizing controllers for a class of non-minimum phasemultiple-input/multiple-output plants is shown. A simple design method to specify controlcharacteristics is also presented.

Study on the Model Feedback Control System for a Class of Non-Minimum Phase Systems

In the present paper, we examine model feedback control systems (MFCSs). Because MFCSis simple, the MFCS has been applied in many applications such as the trajectory control of robotmanipulators, serially connected water tanks, etc. The control structure of the MFCS is limited, butYamada and Moki reported about whether or not MFCS can represent all of the stabilizing controllersof a minimum phase plant. However, no research has been reported whether or not MFCS can representall of the stabilizing controllers of a non-minimum phase plant. The purpose of the present paper isto give a solution to the question as to whether or not all of the stabilizing controllers for a plantare expressible in the MFCS structure. The relation between MFCS and the parameterization of allstabilizing controllers for a class of non-minimum phase plants is shown. A simple design method tospecify control characteristics is also presented.

Robust trajectories following control of a 2-link robot manipulator via coordinate transformation for manufacturing applications

A common idea concerning trajectory control of robot manipulators is to tackle the motion of the end-effector. According to traditional trajectory designs, a prescribed profile in a work space is first decomposed into independent joint positions such that the success in a contouring task lies with good tracking capability of individual joints. To advance trajectory control precision without relying on high tracking performance, a contour control strategy for a robot manipulator is presented in this paper. Different from the traditional concept of trajectory control, a contour following control strategy is developed via a coordinate transformation scheme. The main advantage of the proposed control architecture is that the final contouring accuracy will not be degraded in case the tracking performance of the robot manipulator is not good enough. Moreover, using a concept of variable structure control theory, a smooth robust control algorithm is realized in the form of proportional control plus an integration term. The robustness of the control algorithm is also demonstrated. A number of experiments are conducted to demonstrate the advantage of the trajectories following control framework and validate the feasibility of the proposed controller.

Robust Trajectory Control of Robot Manipulators Using Time Delay Control with Adaptive Compensator

An enhanced controller to improve the robustness of Time Delay Control (TDC) for a robot manipulator in the presence of hard nonlinearities is proposed. The problem of TDC is first analyzed with TDC as a trajectory control for a robot manipulator in the presence of hard nonlinearity. The method of steepest descent, which is a type of adaptive control scheme, is used to solve this problem in order to develop an enhanced controller. The proposed controller is called TDC with Adaptive Compensator (TDCAC). The adaptive compensator of TDCAC serves as a type of high pass filter for the effect of hard nonlinearities and is very intuitional for choosing the adaptive gain of the adaptive compensator. The robustness of TDCAC is verified by experiment.

A Study on the Class of Controllers in Model Feedback Control

In the present paper, we examine the Model Feedback Control System. Since the Model Feedback Control System is simple, the ModeI Feedback ControI System has been applied to many applications such as the trajectory control of robot manipulator, level control of serially connected tanks and so on. Since the control structure of the ModeI Feedback Control System has constrained, it is likely that the Model Feedback Control System can not represent all of stabilizing controllers for the plant. Nothing has been reported whether or not, the ModeI Feedback Control System can represent all of stabilizing controllers. The purpose of the present paper is to give a solution to the question whether or not, all of stabilizing controllers for the plant are written as the Model Feedback Control structure. Since Model Feedback Control System is applied to many minimum phase systems, in the present paper, we especially clarify the relationship between the ModeI Feedback Control System and the parametrization of all stabilizing controllers for the minimum phase system. First, the relationship between the Model Feedback Control system and the parametrization by Glaria and Goodwin is clarified. Next, for the strictly proper system, the relationship between the Model Feedback Control System and the parametrization by Yamada and Moki is presented. Simple design method of the Model Feedback ControI System to specify control characteristics is shown.

RELATION BETWEEN MODEL FEEDBACK CONTROL SYSTEMS AND PARAMETERIZATION OF ALL STABILIZING CONTROLLERS

In the present paper, we examine model feedback control systems. Because model feedback control is simple, the model feedback control system has been applied in many applications such as the trajectory control of robot manipulators, serially connected tanks, etc. The control structure of the model feedback control system is limited, and nothing has been reported about whether or not model feedback control can represent all of the stabilizing controllers of a plant. The purpose of the present paper is to give a solution to the question as to whether or not all stabilizing controllers for a plant are expressible in the model feedback control structure. For strictly proper systems, the relation between model feedback control and the parameterization of all stabilizing controllers for minimum phase systems is shown. A simple design method to specify control characteristics is also presented.

Using soft computing techniques for improving foot trajectories in walking machines

AbstractWalking machines have been investigated during the last 40 years and some basic techniques of this field are already well known. However, some aspects still need to be optimized. For instance, speed seems to be one of the major shortcomings of legged robots; thus, improving leg speed has been chosen as the main aim of this work. Although some algorithms for optimizing trajectory control of robot manipulators already exist, we propose a more computationally efficient method that employs fuzzy set theory to involve real dynamic effects over leg motion instead of an inaccurate mathematical model. In this article, we improve leg speed by automatically tuning the acceleration of legs. For this purpose, we define fuzzy rules based on experiments and we find the optimal acceleration for every given trajectory. A simple fuzzy inference system is used to compute the required acceleration. It is based on five rules using three linguistic variables. Final results show that foot acceleration tuning for straight trajectory generation is a suitable method for achieving accurate, smooth and fast foot movements. Also it is shown that under some conditions average leg speed can be increased up to 100% using the control methods herein proposed. © 2001 John Wiley & Sons, Inc.

Two Adaptive Robust Sliding Mode Controllers for Robot Manipulators

Sliding mode controllers are known to be capable of achieving robustness against modeling errors, plant disturbances and plant parameter variations. They, however, require upper-bounds of uncertainties in plant parameters to specify feedback gains that realize robustness. These upper-bounds are often chosen conservatively and, as a result, high feedback gains and oscillations, refered to as chattering, occur. Recently, on-line identification of the upper-bounds of uncertainties have been successfully incorporated into sliding mode regulators to alleviate the chattering. Here, we propose to extend this on-line identification technique to two types of controllers, called adaptive robust sliding mode controllers, for trajectory control of robot manipulators. The stability properties of the proposed controllers are demonstrated using Lyapunov functions and are implemented using a PUMA-type manipulator. The performance of these controllers is compared with that of regular sliding mode controllers. The result of comparison reveals excellent robustness and chattering suppression of the proposed controllers.

Trajectory control of robotic manipulators by using a feedback-error-learning neural network

SummaryThis paper presents a neural network based control strategy for the trajectory control of robot manipulators. The neural network learns the inverse dynamics of a robot manipulator without any a priori knowledge of the manipulator inertial parameters nor any a priori knowledge of the equation of dynamics. A two step feedback-error-learning process is proposed. Strategies for selection of the training trajectories and difficulties with on-line training are discussed.

Comments on "A sliding mode controller with bound estimation for robot manipulators"

In the above paper by Su-Leung (IEEE Trans. Robot Automat., vol.9, no.2, p.208-14 (1993)), an adaptive sliding mode control scheme for the trajectory control of robot manipulators is developed. The proof of Theorem 1 in the paper is accomplished by resorting to the Lasalle theorem. It is well known that the Lasalle theorem can be applied to either autonomous systems or periodic nonautonomous systems, but it is not applicable to general nonautonomous systems. The Lasalle theorem is not applicable to the manipulator tracking control system given in the paper because the tracking control system considered in the paper is neither an autonomous system nor a periodic nonautonomous system but it can be classified into general nonautonomous systems. Therefore, the proof of Theorem 1 in the paper is not correct. >

Discrete-time learning control for robotic manipulators

A discrete-time learning control for robotic manipulators is studied using its pulse transfer function. Firstly, the discrete-time learning stability condition which is applicable to single-input two-outputs systems is derived. Secondly, the stability of the learning algorithm with a position signal is studied. In this case, when the sampling period is small, the algorithm is not stable because of an unstable zero of the system. Thirdly, the stability of the algorithm with position and velocity signals is studied. In this case, we can stabilize the learning control system which is unstable in the case of learning with only the position signal. Finally, simulation results on the trajectory control of robotic manipulators using the discrete-time learning control are shown. The simulation results agree well with the analytical results.

Acceleration based learning control of robotic manipulators using a multi-layered neural network

This paper presents a nonlinear compensation method based on neural networks for trajectory control of robotic manipulators. A multi-layered perceptron neural network (MLP) is used to predict the required actuator torques of a robot to follow a desired trajectory, and these predicted torques are applied to the robot as feedforward compensations in parallel to a linear feedback controller. An acceleration based learning scheme is proposed to adjust the connection weights in the neural network to form an approximated dynamic model of the robot. Simulation results show that the proposed learning scheme improves the speed of error convergence of the system and reduces the convergent error with the efficient adaptation to the changing system dynamics. The validity of the proposed learning scheme is verified through experiments. >

A sliding mode controller with bound estimation for robot manipulators

A sliding-mode control algorithm combined with an adaptive scheme, which is used to estimate the unknown parameter bounds, is developed for the trajectory control of robot manipulators. The major contribution of this methodology lies in the use of a special matrix, called the regressor, which makes it possible to isolate the unknown parameters from the robotic dynamics. Based on the upper bounds of those unknown parameters, which are estimated by a simple adaptive law, the proposed VSS (variable-structure-system) controller guarantees the stability of the closed-loop system. The robustness analysis shows that in the presence of the uncertainties, which are assumed to be unbounded and rapidly varying, the closed-loop system can still be stabilized. Chattering is reduced by using the boundary layer technique. Simulation results show the validity of the proposed algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Trajectory Control of Manipulators with Discrete-Time Learning Control. Experimental Studies.

A discrete-time learning control for robotic manipulators is studied from a viewpoint of zeros of a control system. The signal used for learning is a position signal of each joint detected by a pulse encoder. Firstly, we show that when sampling period is small, the learning is unstable because of an unstable zero of the system. Secondly, we show that we can stabilize the learning control system with small sampling period by using a low pass filter, and also show that a tracking error converges not to zero but to a certain value in that case. Finally, experimental results on the trajectory control of robotic manipulators using the discrete-time learning control are shown. These experimental results agree well with the analytical ones.

Real-time implementation of regressor-based sliding mode control algorithm for robotic manipulators

A regressor-based variable-structure control scheme has been developed for the trajectory control of robot manipulators in the presence of disturbances, parameter variations, and unmodeled dynamics. The method is based on the regressor structure given by J.J.E. Slotine and W. Li, without parameter adaptation. This avoids the requirement of persistency of excitation, and the convergence of the overall transient exponential. The method is robust against a class of state-dependent uncertainties, which may result, for example, from unmodeled dynamics. The problem of chattering is solved by the smoothing control law. It is with respect to a set around the origin, which can be made arbitrarily small. To illustrate the feasibility of this controller, it was implemented using a Motorola M68000 microprocessor on a two-link revolute joint manipulator subjected to a variable payload. Experimental results confirm the validity of accurate tracking capability and the robust performance. >

Linearized dynamic models for trajectory control of robot manipulators

To make an appropriate choice of linear model for the development of real-time control of robot manipulators, four direct linearization schemes have been studied here on the basis of computation and accuracy. Accuracy is assured in the state linearization method, whereas the rate linearization method leads to a satisfactory trade-off between computational effort and accuracy. In the case of high-velocity motions, a combination of state and rate linearization is proposed.

A neural network compensator for uncertainties of robotics manipulators

A neural network controller for trajectory control of robotic manipulators that is used not to internalize the inverse dynamic model of the controlled object but to compensate only the uncertainties of the robotic manipulator is presented. Its performance is compared with that of the conventional adaptive scheme. The results show the ability of the neural network controller to adapt to unstructured effects. A learning method for the neural network compensator with true teaching signals is shown. The tracking error of the robotic manipulator was greatly reduced when this controller was used. >