Neural-network-based nonlinear model predictive tracking control of a pneumatic muscle actuator-driven exoskeleton

This paper presents an integral-type adaptive sliding mode controller integrated into a neural network for position-tracking control of a pneumatic muscle actuator testing system. Stability of the closed-loop system is covered by the sliding mode algorithm while both control error and control energy are minimized by the neural network. With only four weight factors in the hidden layer and two weight factors in the output layer, the network provides a very high calculation speed. Then, the approach is successfully verified on a real-time system under different working conditions. By comparing it with a proportional-integraldifferential controller on the same system and under the same working conditions, the effectiveness of the designed controller is confirmed.

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Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network

Abstract and Applied Analysis ◽

10.1155/2015/906126 ◽

2015 ◽

Vol 2015 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Alexander Hošovský ◽

Ján Piteľ ◽

Kamil Židek

Keyword(s):

Neural Network ◽

Analytical Model ◽

Physical Interpretation ◽

Dynamic Properties ◽

Model Performance ◽

Open Loop ◽

Elman Neural Network ◽

Pneumatic Muscle ◽

Rotational Joint ◽

Pneumatic Muscle Actuator

To make effective use of model-based control system design techniques, one needs a good model which captures system’s dynamic properties in the range of interest. Here an analytical model of pneumatic muscle actuator with two pneumatic artificial muscles driving a rotational joint is developed. Use of analytical model makes it possible to retain the physical interpretation of the model and the model is validated using open-loop responses. Since it was considered important to design a robust controller based on this model, the effect of changed moment of inertia (as a representation of uncertain parameter) was taken into account and compared with nominal case. To improve the accuracy of the model, these effects are treated as a disturbance modeled using the recurrent (Elman) neural network. Recurrent neural network was preferred over feedforward type due to its better long-term prediction capabilities well suited for simulation use of the model. The results confirm that this method improves the model performance (tested for five of the measured variables: joint angle, muscle pressures, and muscle forces) while retaining its physical interpretation.

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Position Control of a Pneumatic Muscle Actuator Using RBF Neural Network Tuned PID Controller

Mathematical Problems in Engineering ◽

10.1155/2015/810231 ◽

2015 ◽

Vol 2015 ◽

pp. 1-16 ◽

Cited By ~ 8

Author(s):

Jie Zhao ◽

Jun Zhong ◽

Jizhuang Fan

Keyword(s):

Neural Network ◽

Experimental Data ◽

Phenomenological Model ◽

Pid Controller ◽

Position Control ◽

Rbf Neural Network ◽

Pneumatic Muscle ◽

Highly Nonlinear ◽

Experimental Apparatus ◽

Pneumatic Muscle Actuator

Pneumatic Muscle Actuator (PMA) has a broad application prospect in soft robotics. However, PMA has highly nonlinear and hysteretic properties among force, displacement, and pressure, which lead to difficulty in accurate position control. A phenomenological model is developed to portray the hysteretic behavior of PMA. This phenomenological model consists of linear component and hysteretic component force. The latter component is described by Duhem model. An experimental apparatus is built up and sets of experimental data are acquired. Based on the experimental data, parameters of the model are identified. Validation of the model is performed. Then a novel cascade position PID controller is devised for a 1-DOF manipulator actuated by PMA. The outer loop of the controller is to cope with position control whilst the inner loop deals with pressure dynamics within PMA. To enhance the adaptability of the PID algorithm to the high nonlinearities of the manipulator, PID parameters are tuned online using RBF Neural Network. Experiments are performed and comparison between position response of RBF Neural Network based PID controller and that of classic PID controller demonstrates the effectiveness of the novel adaptive controller on the manipulator.

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