Force feedback controller based on fuzzy-rules emulated networks and Hertzian contact with ultrasound

Armendariz, J.; Treesatayapun, Chidentree; Baltazar, Arturo

doi:10.1016/j.ymssp.2011.08.009

Cited by 21 publications

(14 citation statements)

References 32 publications

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“…Motivated our previous works on neuro-fuzzy controller [11] and the regressor-free second order sliding mode con troller [7], we further explore the synthesis of a self-tuning mechanism in the context of passivity to guarantee tracking without any function approximation compensator of inverse dynamics. The key observation is to workout the passivity inequality of the error dynamics parameterized by PID like error manifolds, [7], and identify the parameter of the dissipation rate.…”

Section: Background On Self-tuning Pid-l1ke Controlmentioning

confidence: 99%

“…The key observation is to workout the passivity inequality of the error dynamics parameterized by PID like error manifolds, [7], and identify the parameter of the dissipation rate. Then, propose a neurofuzzy mechanism, [11], to ensure dissipativity when the storage function is the Lyapunov function.…”

Section: Background On Self-tuning Pid-l1ke Controlmentioning

confidence: 99%

“…This particular configuration has a simple structure to transfer the knowledge about S(k) based on fuzzy rules injected into the network, [15], [11].…”

Section: A Neurojuzzy Networkmentioning

confidence: 99%

“…This gain plays the role of enlarging or reducing the dissipa tion rate gain to induce an isolated equilibrium by monitoring the error manifold. Our proposal extends the involved neuro fuzzy algorithm of [11], [15] to develop a simple yet intuitive tuning rules without depending on structural information of the system nor complex approximation techniques nor high frequency or involved stabilizers. Simulation illustrates the performance rather under ideal conditions, and real time experiments are shown and discussed to establish the feasibility and viability of the proposed scheme.…”

Section: Introductionmentioning

confidence: 99%

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Regressor-free tracking of robots with self-tuning PD-like control

Armendariz

Parra-Vega

García-Rodríguez

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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The success in real applications of PID regulator has thought us that it is desirable a simple controller with clear and intuitive policy for tuning of feedback gains, as well as minimum or null dependency on the dynamics of the system. In this paper, theoretical and experimental results of PD-like structures for tracking of robots subj ect to friction and disturbances is presented. The salient feature of our proposal is a neuro-fuzzy schemes used to a single gain that multiplies the regressor-free PID structure. This bounded time varying gain plays the role of enforcing a dissipative mapping to obtain exponential stability. Stability is obtained in the sense of Lyapunov and experimental study is presented and discussed, which highlights the viability and feasibility for real applications.

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Section: Background On Self-tuning Pid-l1ke Controlmentioning

confidence: 99%

Section: Background On Self-tuning Pid-l1ke Controlmentioning

confidence: 99%

“…This particular configuration has a simple structure to transfer the knowledge about S(k) based on fuzzy rules injected into the network, [15], [11].…”

Section: A Neurojuzzy Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Regressor-free tracking of robots with self-tuning PD-like control

Armendariz

Parra-Vega

García-Rodríguez

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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“…To the case of tactile sensing based on ultrasound, an important benefit of using FREN is the fact that it does not require knowledge of the mathematical model that describes the interaction between the contact force and the ultrasonic energy which is quite difficult to obtain [5,6]. Thus by following IF-THEN rules similar to human sense, the force and the velocity could be controlled.…”

Section: Introductionmentioning

confidence: 99%

Multi-Input Fuzzy Rules Emulated Networks with a Hertzian Contact Force Sensor

Armendariz

Treesatayapun

Baltazar

2011

2011 10th Mexican International Conference on Artificial Intelligence

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In this paper, the implementation of an adaptive neuro-fuzzy network called Multi-Input Fuzzy Rules Emulated Network (MIFREN) is presented. This network has a simple structure to describe the human knowledge about the robotic manipulator and the sensor in the form of fuzzy IF-THEN rules. MIFREN is implemented as a force feedback controller which used a force sensor based on Hertzian contact and ultrasound. The contact probe exhibits high sensitivity to initial instantaneous contact and provides an estimation of the reaction force. Based on the force signal provided by this probe, the MIFREN's structure can be constructed and integrated directly in the controller without any mathematical model of the system. The experimental results with synthetic elastomer and tomato samples demonstrate the superior performance of MIFREN compared to a well tuned PI controller.

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