Decentralized LPV Gain-Scheduled PD Control of a Robotic Manipulator

In this paper, low-complexity linear parameter-varying (LPV) modeling and control of a two-degrees-of-freedom robotic manipulator is considered. A quasi-LPV model is derived and simplified in order to facilitate LPV controller synthesis. An LPV gain-scheduled, decentralized PD controller in linear fractional transformation form is designed, using mixed sensitivity loop shaping to take — in addition to high tracking performance — noise and disturbance rejection into account, which are not considered in model-based inverse dynamics or computed torque control schemes. The controller design is based on the existence of a parameter-dependent Lyapunov function — employing the concept of quadratic separators — thus reducing the conservatism of design. The resulting bilinear matrix inequality (BMI) problem is solved using a hybrid gradient-LMI technique. Experimental results illustrate that the LPV controller clearly outperforms a decentralized LTI-PD controller and achieves almost the same accuracy as a model-based inverse dynamics and a full-order LPV controllers in terms of tracking performance while being of significantly lower complexity.

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A New Control Approach to Nonlinear Systems Undergoing Changes in a System Parameter

10.1115/imece2000-2335 ◽

2000 ◽

Author(s):

Hyuk C. Nho ◽

Peter Meckl

Keyword(s):

Neural Network ◽

Fuzzy Logic ◽

Sudden Change ◽

Torque Control ◽

Tracking Performance ◽

Control Architecture ◽

Model Based ◽

Payload Uncertainty ◽

Payload Mass ◽

Computed Torque

Abstract Conventional model-based computed torque control fails to produce good trajectory tracking performance in the presence of payload uncertainty and modeling error. The problem is how to provide accurate dynamics information to the controller. A new control architecture that incorporates a neural network, fuzzy logic and a simple proportional-derivative (PD) controller is proposed to control an articulated robot carrying a variable payload. A feedforward (multilayer) neural network is trained off-line to capture the nonlinear inverse dynamics of the system. The network is placed in the feedforward path to minimize tracking error. The network receives the same input signals as conventional computed torque as well as the payload mass estimate, which comes from a fuzzy logic mass estimator. The fuzzy logic, trained off-line to optimize the membership function, is developed to estimate the changing payload mass. The fuzzy logic estimator is based on joint acceleration error to improve the speed of detection and estimation of payload mass change. The effectiveness of the proposed architecture is demonstrated by experiment on a two-link planar manipulator with changing payload mass. Experiment results show that this control architecture achieves excellent tracking performance in the presence of payload uncertainty. The results of the control architecture are also compared with those of a model-based control architecture. This approach can be employed in any nonlinear mechanical system with a sudden change in a parameter.

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An Emergency Tracking Controller Design for a Manipulator After its Actuator Failure

Volume 8: 27th Conference on Mechanical Vibration and Noise ◽

10.1115/detc2015-47304 ◽

2015 ◽

Cited By ~ 1

Author(s):

Elżbieta Jarzębowska ◽

Adam Szewczyk

Keyword(s):

Degrees Of Freedom ◽

Controller Design ◽

Actuator Failure ◽

End Effector ◽

Model Based ◽

Rest Position ◽

Control Goal ◽

Tracking Controllers ◽

Tracking Controller ◽

Simulation Results

This paper presents a development of two model-based emergency tracking controllers which can be turned on when one of actuators of a system fails during motion. The system is represented by a manipulator possessing 3 degrees of freedom, which may work in horizontal or vertical planes. The control goal is to enable an end effector of a broken manipulator completing tracking a predefined task as good as possible and then get back to its rest position. Simulation results confirm good performance of the designed emergency tracking controllers.

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A Neural Network Based Sliding Mode Control for Tracking Performance with Parameters Variation of a 3-DOF Manipulator

Applied Sciences ◽

10.3390/app9102023 ◽

2019 ◽

Vol 9 (10) ◽

pp. 2023 ◽

Cited By ~ 4

Author(s):

Hoai-Vu-Anh Truong ◽

Duc-Thien Tran ◽

Kyoung Kwan Ahn

Keyword(s):

Neural Network ◽

Sliding Mode Control ◽

Degrees Of Freedom ◽

Sliding Mode ◽

External Disturbance ◽

Robotic Manipulator ◽

Tracking Performance ◽

Mode Control ◽

Tracking Motion ◽

Three Degrees Of Freedom

The manipulator, in most cases, works in unstructured and changeable conditions. With large external variations, the demand for stability and robustness must be ensured. This paper proposes a neural network sliding mode control (NNSMC) to cope with uncertainties and improve the behavior of the robotic manipulator in the presence of an external disturbance. The proposed method is applied to the three degrees of freedom (DOF) manipulator. Some comparisons between the proposed and the conventional algorithms are given in both simulation and experiments to prove that the designed control can achieve higher accuracy in tracking motion.

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Adaptive and Singularity-Free Inverse Dynamics Models for Control of Parallel Manipulators With Actuation Redundancy

Volume 6: 35th Mechanisms and Robotics Conference, Parts A and B ◽

10.1115/detc2011-47943 ◽

2011 ◽

Author(s):

Andreas Mu¨ller ◽

Timo Hufnagel

Keyword(s):

Range Of Motion ◽

Inverse Dynamics ◽

Null Space ◽

Parallel Manipulators ◽

Point Of View ◽

Torque Control ◽

Alternative Formulation ◽

Dynamics Model ◽

Model Based ◽

Control Forces

Redundant actuation of parallel kinematics machines (PKM) is a way to eliminate input-singularities and so to enlarge the usable workspace. From a kinematic point of view the number m of actuator coordinates exceeds the DOF δ of a redundantly actuated PKM (RA-PKM). The dynamics model, being the basis for model-based control, is usually expressed in terms of δ independent actuator coordinates. This implies that the model exhibits the same singularities as the non-redundant PKM, even though the RA-PKM is not singular. Consequently the admissible range of motion of the RA-PKM model is limited to that of the non-redundant PKM. In this paper an alternative formulation of the dynamics model in terms of the full set of m actuator coordinates is presented. It leads to a redundant system of m motion equations that is valid in the entire range of motion. This formulation gives rise to an inverse dynamics formulation tailored for real-time implementation. In contrast to the standard formulation in independent coordinates, the proposed inverse dynamics formulation does not involve control forces in the null space of the control matrix, i.e. it does not allow for the generation of internal prestresses, however. This is not problematic as the latter is usually not exploited. The proposed method is compared to the recently proposed adaptive coordinate switching method. Experimental results are reported if the inverse dynamics solution is introduced in model-based computed torque control scheme of a planar 2DOF RA-PKM.

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Acceleration decoupling control of 6 degrees of freedom electro-hydraulic shaking table

Journal of Vibration and Control ◽

10.1177/1077546319870620 ◽

2019 ◽

Vol 25 (21-22) ◽

pp. 2758-2768 ◽

Cited By ~ 2

Author(s):

Guang-feng Guan ◽

AR Plummer

Keyword(s):

Degrees Of Freedom ◽

Inverse Dynamics ◽

Joint Space ◽

Decoupling Control ◽

Shaking Table ◽

Tracking Performance ◽

Actuator Dynamics ◽

Stability Margins ◽

Seismic Testing ◽

Acceleration Tracking

Electro-hydraulic shaking tables are widely used for vibration testing where high force and displacement amplitudes are required. In particular, they are a vital tool in seismic testing, enabling the development of buildings and other structures which are earthquake resistant. Three-variable-control (TVC) is commonly used for the control of multi-degrees of freedom (DOFs) electro-hydraulic shaking tables. However, the coupling between the DOFs is often significant and is not compensated by TVC. In this paper, an acceleration decoupling control (ADC) method is presented for a 6 DOFs electro-hydraulic shaking table system to improve the acceleration tracking performance and decouple the motion in task space. The gravitational, Coriolis, and centripetal forces are compensated for in joint space based on a dynamic model of the shaking table. Modal control is used to transform the coupled dynamics into six independent systems. Inverse dynamics models are used to cancel the differences in actuator dynamics. The proportional gains in modal space are tuned heuristically to give sufficient stability margins to provide robustness in the presence of modeling errors. The input filter and feedforward controller in TVC are added to improve the acceleration tracking performance of each independent system. Experimental acceleration frequency responses are used to demonstrate the effectiveness of ADC, and in particular these show a consistent reduction in cross-axis coupling compared to TVC. Moreover, only four parameters need to be tuned, as opposed to 36 for TVC, and the method provides a viable route to improving the accuracy of seismic testing in the future.

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Design of Robust Fopi-Fopd Controller for Maglev System Using Particle Swarm Optimization

Engineering and Technology Journal ◽

10.30684/etj.v39i4a.1956 ◽

2021 ◽

Vol 39 (4A) ◽

pp. 663-667

Author(s):

Salwan Y. Yousif ◽

Mohamed J. Mohamed

Keyword(s):

Particle Swarm Optimization ◽

Degrees Of Freedom ◽

Controller Design ◽

Magnetic Levitation ◽

Particle Swarm ◽

Pso Algorithm ◽

Pd Controller ◽

Swarm Optimization ◽

Feedback Control Systems ◽

The Time Domain

Magnetic Levitation System (MLS) is one of the benchmark laboratories models for designing and testing feedback control systems in the presence of the parametric uncertainties and disturbances effect. Therefore, the MLS can be regarded as a tool to study and verify a certain robust controller design. In this paper, two types of powerful control schemes are presented to control the MLS. The first controller is a robust PI-PD controller, while the other is a robust fractional order FOPI-FOPD controller which provides two extra degrees of freedom to the system. In both controller design procedures, the Particle Swarm Optimization (PSO) algorithm is used to find the best values of controller parameters subject to the time-domain objective function and H∞ constraints. All modeling processes including parameterization, optimization, and validation of the controllers are performed using MATLAB. The simulation results show that the MLS with robust FOPI-FOPD is faster and more stable than the MLS with robust classical PI-PD. Also, the proposed FOPI-FOPD controller gives far superior results than the PI-PD controller for disturbance rejection.

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