CDC2000 Novel Neural Network Control Techniques for Industrial Motion Control Systems

Novel Neural Network Control Techniques for Industrial Motion Control Systems
Home Full List of Titles 1: Proceedings of CDC2000 Discrete Event Systems Control in Communication Systems Optimal Control and Applications I Optimisation Approaches and Methods Model Predictive Control Advances in Linear Estimation Stochastic and Uncertain Systems Nonlinear Control and Applications Nonlinear Estimation and Filtering Formation Control and its Applications New Approaches to Fuzzy Control Manufacturing Systems Automotive Applications Stability Issues in Hybrid Control Recent Advances in Stochastic Networks Optimal Control and Applications II Robust Controller Design - mu, L1 and H2 Constrained and Receding Horizon Control Identification and Control around the World Markov Decision Processes Nonlinear Optimisation Observers for Nonlinear Systems Motion Planning Neural / Fuzzy Stability and Control Motor Control Control of Quantum Phenomena I Hybrid Systems Methods Control in Communication Networks Robustness and Optimisation Bumpless Transfer, Antiwindup and Saturation Adaptive Control: Linear Systems Estimation and Closed Loop Identification Control of Markov Processes Nonlinear Filtering and Control Modelling, Identification and Validation of Nonlinear Systems Differential Geometric Control Theory for Mechanical Systems Nonlinear Output Feedback Control Pneumatics and Compression Systems Control of Quantum Phenomena II Stability of Hybrid Systems Performance Analysis in Communication Networks Adaptive Control of Nonlinear Systems LMI Methods in Design Robust Control of Time Delay Systems Subspace Identification Methods Nonlinear Stochastic Filtering and Estimation Bifurcations, Chaos and Control I New Progress in Synthesis of Nonlinear Systems I Implementation Issues of Sliding Mode Control Theory Control of Mixing in Shear Flows Novel Neural Network Control Techniques for Industrial Motion Control Systems Physiological Control Systems Optimal Control of Hybrid Systems Stochastic Models for Communication Networks Control and Stabilisation of Nonlinear Systems New Directions in Robust Control Linear Systems Theory Advanced Topics in Systems Theory Estimation in Action Bifurcations, Chaos and Control II New Progress in Synthesis of Nonlinear Systems II Numerical Design and Analysis Techniques for Nonlinear Systems Analysis and Control of Underactuated Systems Sliding Mode Control I Challenges in the Application of Control to Computer Systems Estimation and Diagnosis of Discrete Event Systems Communications and Games Optimal Control Stochastic Systems Model Reduction Methodologies Identification and Subspace Methods Applications of Nonlinear Adaptive Control Advances in Nonlinear Output Feedback Design The Behavioural Approach to Systems and Control Vision Based Estimation and Control: Recent Advances and Open Problems Agile Control of Military Operations Sliding Mode Control II Model-based Fault Diagnosis of Industrial Processes Discrete Event Systems / Petri Nets System Identification and Confidence Estimation New Approaches to H-Infinity Control I Probabilistic Approaches to Robust Control Time Delay System Stabilisation Identification Methods Controlled Stochastic Processes Output Feedback of Nonlinear Systems Topics in Nonlinear Stabilisation Mobile Robots: Tracking Control Robust Control of Nonlinear Systems Power Systems Stabilisation and Control Disk Drive Control Hybrid Control Applications Discrete Time Systems New Approaches to H-Infinity Control II Linear Systems with Saturating Actuators New Theories in Distributed Parameter Systems Applications of Estimation and Identification Stochastic Control and Tuning Methodologies Control of Nonlinear Systems Iterative Learning and Control Coordinating Robot Systems Nonlinear Time Varying Systems Novel Applications of Neural Networks Aerospace Applications Switched Systems Implicit and Descriptor Systems LQG Periodic Systems and Disturbances New Horizons for Distributed Parameter Systems State Estimation Learning and Neuro-Control Nonlinear Control and Stabilisation I Tracking Vision Servoing Controllability of Nonlinear Systems Control of Flexible Systems Electro-Mechanical Systems Robust Control Methods and Applications Fault Detection and Diagnosis Optimisation and Applications Robust Stability Analysis Numerical Methods in Control Filtering in Continuous Time Stochastic Systems Interplay between Control and Signal Processing Fault Detection and Analysis Nonlinear Dynamical Systems Nonlinear Time Delay Systems Computational Issues in Nonlinear Control Disturbance Rejection Process Control Industry Applications Linear Parameter Varying Systems Linear Control Systems Dynamic and Nonlinear Programming Model Reduction Applications New Techniques for Control and Systems: Numerical Linear Algebra Estimation and Identification using Hidden Markov Models Applications of Stochastic Control Topics in Linear Design Nonlinear Control and Stabilisation II Ambulatory Robot Systems Chaotic and Oscillatory Systems Biomedical System Control Integrated Control and CPU Scheduling Linear Design Techniques Adaptive Disturbance / Noise Compensation Nonlinear Model Predictive Control Sensitivity Design, Analysis and Limitations Analysis of Linear Systems Linear Matrix Inequalities in Design Lyapunov's 2nd Method Robotics: Tracking Control Lagrangian and Hamiltonian Theory Variable Structure Control Machine Vision Signal Processing Methods in Control Applied Nonlinear Control Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	Neural Net Backlash Compensation With Hebbian Tuning By Dynamic Inversion Authors: Rastko R. Selmic, Frank L. Lewis, Volume: 1, Page 1742 Paper number 3101 Abstract: Neural network compensation scheme is presented for the class of nonlinear systems with backlash nonlinearity. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. Instead of a derivative, which cannot be implemented, a filtered derivative is used. Full rigorous stability proofs are given using filtered derivative. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed. The technique provides a general procedure for using NN to determine the dynamic preinverse of an invertible dynamical system. A modified Hebbian algorithm is presented for NN tuning which yields a stable closed-loop system. Using this method yields a relatively simple adaptation structure and offers computational advantages over gradient descent based algorithms. CD003101.PDF (From Author) TOP A Neural Network Based Learning Controller for Robot Manipulators Authors: Chiang-Ju Chien, Li-Chen Fu, Volume: 1, Page 1748 Paper number 3102 Abstract: In this paper, an iterative learning control using neural network design is presented for robot manipulators with input disturbance and re-initialization uncertainty. A sampled-data feedforward learning algorithm is designed under a feedback configuration and a rigorous proof via a discrete approach is given to study the learning performance. It is shown that under a sufficient condition on the learning gain, convergence and robustness of tracking error in the iteration domain can be guaranteed at each sampling instant if sampling period is small enough. Since the implementation of learning gain depends on the information of input-output coupling matrix of robot manipulator, a neural network is proposed to solve the implementation problem. A training procedure is applied to estimate the robot manipulator by using only input-output data. The neurons, equivalent to the premise and consequent parameters of a fuzzy system, are tuned by gradient descent and least squares estimate. This will give an initial setting of the neural-network based iterative learning controller. During the control iterations, the neural network can still be tuned for each iteration in order to improve the approximation accuracy and increase the tracking speed. CD003102.PDF (From Author) TOP Helicopter Flight Control Design Using A Learning Control Approach Authors: Russell Enns, Jennie Si, Volume: 1, Page 1754 Paper number 3103 Abstract: In this paper we introduce a new neural learning control mechanism for helicopter flight control design. The significance of our contribution is twofold. First neural dynamic programming (NDP) is in its early development stage and successful applications to date have been limited to simple systems, typically those possessing only a single control and a handful of states. With our industrial scale helicopter model, we consider a very realistic class of complex design problem. To accommodate such complex systems we introduce the concept of a trim network which is seamlessly integrated into our NDP control structure and is trained using our NDP control structure. Second, we introduce a new class of design methodologies to the helicopter control system design community. This approach is expected to be effective in dealing with real-time learning applications such as reconfigurable control. The paper consists of a comprehensive treatise of NDP and extensive simulation studies of NDP designs for controlling an Apache helicopter. All of our designs are tested using FLYRT, a sophisticated industry-scale non-linear validated model of the Apache helicopter. Though illustrated for helicopters, our NDP control system framework should be applicable to general control systems. CD003103.PDF (From Author) TOP Adaptive NN Control of Dynamic Systems with Unknown Dynamic Friction Authors: Shuzhi Sam Ge, Tong-Heng Lee, Jing Wang, Volume: 1, Page 1760 Paper number 3104 Abstract: In this paper, based on the dynamic LuGre friction model, adaptive NN controllers are presented by using neural networks to parameterize the unknown characteristic function or the unknown dynamic friction bounding function respectively. Using Lyapunov synthesis, the adaptive control algorithms are designed to achieve globally asymptotic tracking of the desired trajectory and guarantee the boundedness of all the signals in the closed-loop. Intensive simulations are carried out to verify the effectiveness of the proposed methods. CD003104.PDF (From Author) TOP Position Control of a PM Stepper Motor Using Neural Networks Authors: Gang Feng, Volume: 1, Page 1766 Paper number 3105 Abstract: This paper considers position control of a PM stepper motor. A new control scheme is proposed based on a kind of exact linearization controller and a neural network based compensating controller. This scheme takes advantages of simplicity of the model based control approach and uses the neural network controller to compensate for the motor modeling uncertainties. The neural network is trained on line based on Lyapunov theory and thus its convergence is guaranteed. CD003105.PDF (From Author) TOP Neural Network Enhanced Output Regulation in Uncertain Nonlinear Systems Authors: Jin Wang, Jie Huang, Volume: 1, Page 1770 Paper number 3106 Abstract: The problem of designing a control law to achieve asymptotic tracking and disturbance rejection in a nonlinear plant where both the reference and disturbance signals are generated by an exosystem is called nonlinear output regulation problem. It is known that solvability of this problem relies on the existence of a feedforward function defined by a set of mixed nonlinear partial and algebraic equations called regulator equations. Previous approaches to solving the output regulation problem call for the solution of the regulator equations. However, solving the regulator equations is difficult due to the nonlinearity and complexity. This paper proposes a novel approximation approach to solving the output regulation problem by directly approximating the feedforward function using a class of artificial neural networks. Further, a control configuration is developed that allows the reduction of the tracking error by the on-line adjustment of the parameters of the neural networks. The major advantages of our proposed approach over the previous approaches include 1) the precise knowledge of the plant is not needed, and 2) computation complexity is significantly reduced. The effectiveness of our approach are demonstrated by two well known nonlinear benchmark control problems CD003106.PDF (From Author) TOP