Application of a flexible structure artificial neural network on a servo-hydraulic rotary actuator (original) (raw)
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ISA transactions, 2011
This paper presents a new intelligent approach for adaptive control of a nonlinear dynamic system. A modified version of the brain emotional learning based intelligent controller (BELBIC), a bio-inspired algorithm based upon a computational model of emotional learning which occurs in the amygdala, is utilized for position controlling a real laboratorial rotary electro-hydraulic servo (EHS) system. EHS systems are known to be nonlinear and non-smooth due to many factors such as leakage, friction, hysteresis, null shift, saturation, dead zone, and especially fluid flow expression through the servo valve. The large value of these factors can easily influence the control performance in the presence of a poor design. In this paper, a mathematical model of the EHS system is derived, and then the parameters of the model are identified using the recursive least squares method. In the next step, a BELBIC is designed based on this dynamic model and utilized to control the real laboratorial EHS system. To prove the effectiveness of the modified BELBIC's online learning ability in reducing the overall tracking error, results have been compared to those obtained from an optimal PID controller, an auto-tuned fuzzy PI controller (ATFPIC), and a neural network predictive controller (NNPC) under similar circumstances. The results demonstrate not only excellent improvement in control action, but also less energy consumption.
Neural Network Modeling of a Flexible Manipulator Robot
Springer eBooks, 2012
This paper presents an artificial neural networks application for a flexible process modeling. A flexible planar single-link manipulator robot is considered. The dynamic behavior of this process is described using Lagrange equations and finite elements method. The artificial neural networks are all variations on the parallel distributed processing (PDP) idea. The architecture of each network is based on very similar building blocks which perform the processing. Therefore, two feed-forward and recurrent neural networks are developed and trained using back-propagation algorithm to identify the dynamics of the flexible process. Simulation results of the system responses are given and discussed in terms of level of error reduction. Finally, a conclusion encloses the paper.
The motion control of an experimental hydraulically actuated robot with structured uncertainty (parameter uncertainty, unknown loads, inaccuracies in the torque constants of the actuators, and others) and unstructured uncertainty (high-frequency modes, neglected time-delays, unknown friction forces, stick-slip oscillations, and unknown oil viscosity, etc…) is considered. As a solution we propose two control techniques based on ANFIS: Adaptive Neuro Fuzzy Inference System based computed torque controller (type PD), and Adaptive Neuro Fuzzy Inference System based PD plus I controller. Comparative evaluations with respect to conventional PD controller are presented to validate the controllers design. The simulated and experimental results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controllers. Keywords ANFIS based computed torque controller (type PD), ANFIS based computed torque controller (type PD) plus I, ANFIS based PD plus I controller, hydraulically actuated robot arm.
Control of a hydrolyzer using neural-network based controller
2009
Hydrolyzer is a commonly found unit operation in oleochemical industry. Control of hydrolyzer has to be done carefully since efficiency in the control of this unit will affect the yield of the process. At present conventional controllers such as PI and PID have been used to achieve the setpoint especially under presence of disturbances. In this study, neural network have been applied as an alternative to cope with the dynamics behavior of the hydrolyzer. Two types of control strategies namely, direct inverse controller (DIC) and internal model controller (IMC) were implemented in the control system. Two sets of data were used to develop the DIC and IMC. The controllers were evaluated on the ability to track set-points, load disturbance and noise disturbance test and the IMC was found to be the most versatile controller.
ADAPTIVE TRACKING CONTROL OF HYDRAULIC ROBOT MANIPULATOR USING HYBRID INTELLIGENT SYSTEM (ANFIS
The motion control of an experimental hydraulically actuated robot with structured uncertainty (parameter uncertainty, unknown loads, inaccuracies in the torque constants of the actuators, and others) and unstructured uncertainty (high-frequency modes, neglected time-delays, unknown friction forces, stick-slip oscillations, and unknown oil viscosity, etc…) is considered. As a solution we propose two control techniques based on ANFIS: Adaptive Neuro Fuzzy Inference System based computed torque controller (type PD), and Adaptive Neuro Fuzzy Inference System based PD plus I controller. Comparative evaluations with respect to conventional PD controller are presented to validate the controllers design. The simulated and experimental results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controllers. Keywords ANFIS based computed torque controller (type PD), ANFIS based computed torque controller (type PD) plus I, ANFIS based PD plus I controller, hydraulically actuated robot arm.
Being difficult to attain the precise mathematical models, traditional control methods such as proportional integral (PI) and proportional integral differentiation (PID) cannot meet the demands for real time and robustness when applied in some nonlinear systems. The neural network controller is a good replacement to overcome these shortcomings. However, the performance of neural network controller is directly determined by neural network model. In this paper, a new neural network model is constructed with a structure topology between the regular and random connection modes based on complex network, which simulates the brain neural network as far as possible, to design a better neural network controller. Then, a new controller is designed under smallworld neural network model and is investigated in both linear and nonlinear systems control. The simulation results show that the new controller basing on small-world network model can improve the control precision by 30% in the case of system with random disturbance. Besides the good performance of the new controller in tracking square wave signals, which is demonstrated by the experiment results of direct drive electro-hydraulic actuation position control system, it works well on anti-interference performance.
Adaptive and Intelligent Controller Using Neural Network
Intelligent control techniques have emerged to overcome some deficiencies in conventional control method in dealing with complex real-world systems. These problems include knowledge adaptation, learning and expert knowledge incorporation. In this paper, a newly proposed intelligent controller which includes both neural network controller as compensator and an intelligent switching control algorithm based on learning vector quantization neural network (LVQNN) is used to control of complex dynamic systems. A superb mixture of conventional PID controller and the neural network's powerful capability of learning, adaptive and tackle nonlinearity bring us a good-tracking controller for such a kind of plants with high nonlinearity and hysteresis. In addition, with the greatly changing external environments, a learning vector quantization neural network (LVQNN) is applied as a supervisor of the conventional PID controller, which estimates the external environments and switches to the optimal gain of the PID controller. Results of simulating on the complex dynamic systems such as pneumatic artificial muscle (PAM) manipulator show that the newly proposed intelligent controller presented in this study can making online control with better dynamic property, strong robustness and suitable for the control of various plants, including linear and nonlinear process and without regard to the severe change of external environments.
Self Learning Intelligent Controller of Electro hydraulic Actuator
International Journal of Engineering and Manufacturing, 2013
The use of electro-hydraulic cylinders in industry has been becoming increasingly popular. A Fuzzy Logic Controller (FLC), which consists of the linguistic defined fuzzy sets and the control rules, is designed to control of the electro-hydraulic cylinder. Using the triangle shaped membership function; the position of the servo cylinder was successfully controlled. When the system working condition is altered, the control algorithm is shown to be more robust compared to the PID controller.
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2021
In this work, we use a neural network as a substitute for the traditional analytic functions employed as an inversion set in feedback linearization control algorithms applied to hydraulic actuators. Although very effective and with strong stability guarantees, feedback linearization control depends on parameters that are difficult to determine, requiring large amounts of experimental effort to be identified accurately. On the other hands, neural networks require little effort regarding parameter identification, but pose significant hindrances to the development of solid stability analyses and/or to the processing capabilities of the control hardware. Here, we combine these techniques to control the positioning of a hydraulic actuator, without requiring extensive identification procedures nor losing stability guarantees for the closed-loop system, at reasonable computing demands. The effectiveness of the proposed method is verified both theoretically and by means of experimental results.
IEEE Transactions on Industrial Electronics, 2001
This thesis presents an intelligent strategy for controlling the tip position of a flexible-link manipulator. Motivated by the well-known inverse dynamics control approach for rigid-link manipulators, two multi-layer feedforward neural networks are developed to learn the nonlinearities of the system dynamics. The re-defined output scheme is used by feeding back this output to guarantee the minimum phase behavior of the resulting closed-loop system. No a prion' knowledge about the nonlinearities of the system is needed where the payload mass is also assumed to be unknoa-n. The weights of the networks are adjusted using a modified on-line error backpropagation algorithm that is based on the propagation of the redefined output error, derivative of this error and the tip deflection of the manipulator. Numencal simulations as well as real-time controller hplernentation on an experimental setup are carried out. The results acliieved by the proposed neural network-based controller are compared in simulations and experimentally with conventional PD-type and inverse dynamics controls to substantiate and demonstrate the advantages and the promising potentials of this scheme. work, to Sanjeev for proof-reading of a part of this thesis, and to my best friends, Quan and Qingyuan, who have always given me a spintual support. But last, and always, 1 am indebted t o my mother, the rest of my family in China, and my canadian friend, Dorothy, for their continuing backup and encouragement during my study. Their ambitions on me have always been rny source of strengt h and motivation.