Fuzzy PD+I learning control for a pneumatic muscle

Chan, Siew; Lilly, J. H.; Repperger, D.W.; Berlin, J.

doi:10.1109/fuzz.2003.1209375

Cited by 80 publications

(51 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to their highly nonlinear nature and time-varying parameters, PAM robot arms present a challenging nonlinear model problem. Approaches to PAM control have included PID control, adaptive control (Lilly, 2003), nonlinear optimal predictive control (Reynolds et al, 2003), variable structure control (Repperger et al, 1998;Medrano-Cerda et al,1995), gain scheduling (Repperger et al,1999), and various soft computing approaches including neural network Kohonen training algorithm control (Hesselroth et al,1994), neural network + nonlinear PID controller (Ahn and Thanh, 2005), and neuro-fuzzy/genetic control (Chan et al, 2003;Lilly et al, 2003). Balasubramanian et al, (2003a) applied the fuzzy model to identify the dynamic characteristics of PAM and later applied the nonlinear fuzzy model to model and to control of the PAM system.…”

Section: Introductionmentioning

confidence: 99%

“…Repperger et al (1999) applied a gain scheduling model-based controller to a single vertically hanging PAM. Chan et al, (2003) and Lilly et al, (2003) introduced a fuzzy P+ID controller and an evolutionary fuzzy controller, respectively, for the PAM system. The novel feature is a new method of identifying fuzzy models from experimental data using evolutionary techniques.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

Ánh¹,

Nam²

2011

PID Control, Implementation and Tuning

View full text Add to dashboard Cite

This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has highly simple and dynamic self-organizing structure, fast online-tuning speed, good generalization and flexibility in online-updating. The proposed adaptive algorithm focuses on fast and efficiently optimizing Gain Scheduling and PID weighting parameters of Neural MLPNN model used in DNN-PID controller. This approach is employed to implement the DNN-PID controller with a view of controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time through Real-Time Windows Target run in MATLAB SIMULINK ® environment. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID controller. These results can be applied to control other highly nonlinear SISO and MIMO systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

Ánh¹,

Nam²

2011

PID Control, Implementation and Tuning

View full text Add to dashboard Cite

show abstract

“…Therefore, it is not easy to realize the performance of transient response with high speed and with respect to various external inertia loads in order to realize a HFTR. The limitations of PAM manipulator have promoted research into a number of control strategies, such as an adaptive controller [2], sliding mode control [2], fuzzy PD+I learning control [3], robust control [4], feedback linearization control [5], and so on. Though these systems were successful in addressing smooth actuator motion in response to step inputs, the manipulator must be controlled slowly in order to get stable, accurate position control and the external inertia loads were also assumed to be constant or slowly varying.…”

Section: Introductionmentioning

confidence: 99%

Application of Intelligent Switching Control of Pneumatic Artificial Muscle Manipulators With Magneto-Rheological Brake

Ahn²

2005

Proceedings of the JFPS International Symposium on Fluid Power

View full text Add to dashboard Cite

A pneumatic artificial muscle actuator (PAM actuator), has been regarded during the recent decades as an interesting alternative to hydraulic and electric actuators because of these advantages such as high power to weight ratio, low cost, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks. However, problems with the air compressibility and the lack of damping ability have made it difficult to realize motion with high accuracy, high speed and with respect to various external inertia loads in order to realize a human-friendly therapy robot (HFTR). An intelligent phase plane switching controller, which harmonizes a phase plane switching control (PPSC) algorithm, conventional PID controller and the adaptabilities of neural network, is newly proposed in this study. In order to realize satisfactory control performance, Magneto-Rheological Brake (MRB) is equipped to the joint of the manipulator. The experiments were carried out in practical PAM manipulator and the effectiveness of the newly proposed control algorithm was demonstrated through experiments, which proved that the stability of the manipulator could be improved greatly in a high gain control without regard to the change of external inertia loads.

show abstract

“…The advantages and disadvantages of these types of actuators are listed in Table 1. As Figure 2 shows [48], a PM is composed of a flexible reinforced thin inner rubber tube covered by a double helix cordage braid which transforms a radial force into an axial contraction force. The muscle has two ends; one is used for supplying air pressure inside the rubber tube while the second end is used for transferring the muscle force to an external object.…”

Section: Pneumatic Actuatorsmentioning

confidence: 99%

“…The properties of a pneumatic muscle system were studied in the Human vertically actuating a mass [41,48].…”

Section: Dynamical Behavior Of Pneumatic Musclementioning

confidence: 99%

Sliding mode control of robotics systems actuated by pneumatic muscles.

Liang¹

View full text Add to dashboard Cite

show abstract

Fuzzy PD+I learning control for a pneumatic muscle

Cited by 80 publications

References 8 publications

A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

Application of Intelligent Switching Control of Pneumatic Artificial Muscle Manipulators With Magneto-Rheological Brake

Sliding mode control of robotics systems actuated by pneumatic muscles.

Contact Info

Product

Resources

About