Tip Trajectory Tracking for Multilink Flexible Manipulators Using Stable Inversion

Zhao, Hongchao; Chen, Degang

doi:10.2514/2.4237

Cited by 24 publications

(16 citation statements)

References 20 publications

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“…Dynamic inversion used in the controller design is defined as the inverse model of the DNF system shown in (9), with a state specified by the desired dynamics, which is similar to the stable inversion proposed by Zhao and Chen [30]. Stable inversion is the inverse model of the multivariable system with a desired state trajectory as its model input, which guarantees the inverse model to be bounded.…”

Section: Nf Dynamic Inversionmentioning

confidence: 99%

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Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case

Sun

et al. 2007

IEEE Trans. Ind. Electron.

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In this paper, we present a stable discrete-time adaptive tracking controller using a neuro-fuzzy (NF) dynamicinversion for a robotic manipulator with its dynamics approximated by a dynamic T-S fuzzy model. The NF dynamic-inversion constructed by a dynamic NF (DNF) system is used to compensate for the robot inverse dynamics for a better tracking performance. By assigning the dynamics of the DNF system, the dynamic performance of a robot control system can be guaranteed at the initial control stage, which is very important for enhancing system stability and adaptive learning. The discrete-time adaptive control composed of the NF dynamic-inversion and NF variable structure control (NF-VSC) is developed to stabilize the closed-loop system and ensure the high-quality tracking. The NF-VSC enhances the stability of the controlled system and improves the system dynamic performance during the NF learning. The system stability and the convergence of tracking errors are guaranteed by the Lyapunov stability theory, and the learning algorithm for the DNF system is obtained thereby. An example is given to show the viability and effectiveness of the proposed control approach.

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Section: Nf Dynamic Inversionmentioning

confidence: 99%

“…. , 5) determined by (30), the following dynamic T-S fuzzy model can be obtained by substituting (30) into (29) as…”

Section: A Derivation Of the Robot T-s Fuzzy Modelmentioning

confidence: 99%

Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case

Sun

et al. 2007

IEEE Trans. Ind. Electron.

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“…In literature [11,16] such a linearly combined output y is proposed for serial flexible manipulators. Then, for this linearly combined output the coordinate transformation for the flexible system is given by…”

Section: Flexible System Exact Inversionmentioning

confidence: 99%

“…An analysis of the zero-dynamics of the initial design of the flexible manipulator with this output (12)(13)(14)(15)(16) shows, that the system is non-minimum phase.…”

Section: Flexible System Exact Inversionmentioning

confidence: 99%

Integrated Design Approaches for Controlled Flexible Multibody Systems

Seifried

Held

2011

Volume 4: 8th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts a and B

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In industrial manipulators and machine tools modern lightweight designs result in low energy consumption and often allow high working speeds. However, undesired vibrations occur due to the flexibility of the bodies and the control of flexible multibody systems is often a challenging task. In order to achieve good end-effector tracking performance the mechanical design and the control design must be considered concurrently in the sense of an integrated design process. In this paper three different optimization based integrated design approaches for controlled flexible multibody systems are presented.1

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“…Kwon and Book [10], De Luca and Siciliano [11], Feliu and Rattan [12], Ge et al [13], Qian and Ma [14], Yeung and Chen [15], and Morgul [16] proposed tip point motion control strategies for flexible one-link manipulators. In the trajectory tracking control studies for multi-link manipulators by Carusone and D'Eleuterio [17], Ider et al [18], Pfeiffer [19], Yim [20], and Zhao and Chen [21], various control strategies were developed making use of the linearized dynamic equations.…”

Section: Introductionmentioning

confidence: 99%

Motion control of flexible-link manipulators

Kiliçaslan

İder

Özgören

2008

Proceedings of the Institution of Mechanical Engineers, Part C:

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A new method is proposed for the end-effector trajectory tracking control of flexible robot manipulators. The equations of motion are separated into two parts that represent the pseudostatic equilibrium and the deviations from it. The part of the control input for the pseudostatic equilibrium is determined algebraically, and the other part of the control input for the stabilization of the deviations is obtained by a state variable feedback law, by using strain, joint variable, and end-effector position measurements. The feedback gain matrix is determined online by continuously updated pole placement. The pseudostatic equilibrium is defined here as a hypothetical state, in which the velocity and acceleration of the end-effector have their desired values whereas the elastic deformations are instantaneously constant. In order to demonstrate the method, a planar two-link robot with a flexible forearm is taken into consideration. The elasticity of the forearm is approximately described by the first two modes, and a controller is designed by using this two-mode model. Furthermore, in order to investigate the effects of modelling discrepancies, a 'submodel controller' is designed by using a model with only the first mode and it is applied to the same system with the two-mode model. The performances of these two controllers are compared by means of simulations. The behaviour of the flexible robot is also simulated by using the computed torque method as if the robot is rigid in order to illustrate the importance of including the flexibility effects in the formation of an appropriate control law. The spillover effect that causes the dominant poles to approach towards the imaginary axis is inspected by monitoring the real parts of the dominant poles of the closed-loop system under the effect of the 'submodel' and 'computed torque' controllers.

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Tip Trajectory Tracking for Multilink Flexible Manipulators Using Stable Inversion

Cited by 24 publications

References 20 publications

Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case

Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case

Integrated Design Approaches for Controlled Flexible Multibody Systems

Motion control of flexible-link manipulators

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Tip Trajectory Tracking for Multilink Flexible Manipulators Using Stable Inversion

Cited by 24 publications

References 20 publications

Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators&mdash;Discrete Time Case

Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators&mdash;Discrete Time Case

Integrated Design Approaches for Controlled Flexible Multibody Systems

Motion control of flexible-link manipulators

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Product

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Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case

Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case