bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robust Adaptive Control of Robots Using Neural Network: Global Stability
Authors: Kwan C. , Dawson D.M. , Lewis F.L. 
Published: September 2010

ABSTRACTA desired compensation adaptive law‐based neural network (DCAL‐NN) controller is proposed for the robust position control of rigid‐link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee theglobalasymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on‐line, withno offline learning phase required.When compared with standard adaptive robot controllers, we do not require linearity in the parameters, or lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded asa universal reusable controllerbecause the same controller can be applied to any type of rigid robots without any modifications. A comparative simulation study with different robust and adaptive controllers is included.
Adaptive Neural Network Control For Smart Materials Robots Using Singular Perturbation Technique
Authors: Ge S.S. , Lee T.H. , Wang Z.P. 
Published: September 2010

ABSTRACTIn this paper, an adaptive neural network controller is presented for smart materials robots using Singular Perturbation techniques by modeling the flexible modes and their derivatives as the fast variables and link variables as slow variables. The neural network (NN) controller is to control the slow dynamics in order to eliminate the need tor the tedious dynamic modeling and the error prone process in obtaining the regressor matrix. In addition, inverse dynamic model evaluation is not required and the time‐consuming training process is avoided except for initializing the NNs based on the approximate function values at the initial posture at timet=0. The smart materials bonded along the links are used to active suppress the residue vibration. Simulation results have shown that the controller can control the system successfully and effectively.
Nonlinear Fly‐By‐Throttle H∞ Control Using Neural Networks
Authors: Yu Gwo‐Ruey 
Published: September 2010

ABSTRACTA neural network approach to gain scheduling H∞ controllers for propulsion controlled aircraft (PCA) systems is introduced. The PCA system is applied to backup control of aircraft experiencing control surface failure. The H∞ technology is applied to the problem of matching the crippled aircraft and the nominal model. Various H∞ controllers at various flight conditions are used to train radial basis function networks (RBFN), which can then be used as the nonlinear controller. Simulation on an L‐1011 under fly‐by‐throttle control demonstrates that the RBFN controller can stabilize the crippled airplane to obtain the desired model and possesses robustness against the engine delay.
An Lmi Approach To Non‐Fragile Guaranteed Cost Control Of Uncertain Discrete Time‐Delay Systems
Authors: Yee Jiong‐Sang , Wang Jian Liang , Yang Guang‐Hong 
Published: September 2010

ABSTRACTThis paper studies the non‐fragile Guaranteed Cost Control (GCC) problem via memoryless state‐feedback controllers for a class of uncertain discrete time‐delay linear systems. The systems are assumed to have norm‐bounded, time‐varying parameter uncertainties in the state, delay‐state, input, delay‐input and state‐feedback gain matrices. Existence of the guaranteed cost controllers are related to solutions of some linear matrix inequalities (LMIs). The non‐fragile GCC state‐feedback controllers are designed based on a convex optimization problem with LMI constraints to minimize the guaranteed cost of the resultant closed‐loop systems. Numerical examples are given to illustrate the design methods.
Using the Sliding‐Mode PWM Method in an Anti‐Lock Braking System
Authors: Wu Ming‐Chin , Shih Ming‐Chang 
Published: September 2010

ABSTRACTThis study attempted to integrate the Pulse Width Modulation (PWM) method and sliding mode control theory to develop quasi‐continuous control for an automobile anti‐lock braking system. Two controllers are designed in this study. One applies directly by applying quasi‐continuous control to achieve ABS slip control. In addition, the quasi‐continuous control method was applied to develop pressure tracking control, and then this pressure tracking controller and the acceleration signal of the tire were implemented together to construct an anti‐lock braking controller. Both controllers were investigated on a dynamic test stand. Wet road braking was simulated by spraying water on the contact surface between the tire and the flywheel. Excellent braking results not only verify the performance of the sliding PWM method but also provide an alternative to an ABS controller without slip feedback.
Input Disturbance Estimation For Linear Systems in The Bounded Noise Context
Authors: Grobov Igor D. 
Published: September 2010

ABSTRACTA method is presented for constructing input disturbance estimates for a linear system using noisy observations. The input disturbance of the dynamic system and the observation noise are assumed to be unknown but bounded. In addition, the structural characteristics of the input disturbance are given in the form of the maximum possible change of its magnitude per sampling time. The input disturbance represents a wide category of system uncertainties. A recursive procedure for obtaining disturbance set estimates is derived. The procedure is easy to implement and is a competitive technique comparative to the classical estimation schemes. Simulation results demonstrate the performance of the given techniques.
A Robust Speed Controller With VSS Tuning Of Disturbance Cancellation For An Induction Motor Drive
Authors: Liaw C.M. , Chao K.H. , Guo R.S. 
Published: September 2010

ABSTRACTA robust speed controller for an induction motor drive that achieves quantitative control and considers the effects of nonlinearities is presented in this paper. First, a two‐degree‐of‐freedom controller (2DOFC) is designed according to the estimated model for the nominal case and given speed specifications. Then, the control characteristics and limitations of the conventional robust controller are investigated. Accordingly, a modified robust speed controller based on disturbance cancellation is developed, wherein tuning of disturbance cancellation is performed via a variable structure system (VSS) controller. The estimated model mismatched error and its derivative are used as the driven inputs of the VSS controller. Greater robustness and flexibility in achieving a compromise between control effort and control performance can be achieved using the proposed controller. The effectiveness of the proposed controller is demonstrated by simulation and measured results.
Disturbance Attenuation for Beam Equations Using Stiffness Control
Authors: Guo Bao‐Zhu , Luo Zheng‐Hua 
Published: September 2010

ABSTRACTThe paper presents a control method for disturbance attenuation of a rotating flexible beam using strain feedback. It is shown that the strain feedback, if appropriately used, can increase the stiffness of the flexible beam and, thus, attenuate external disturbances. The disturbance responses of the feedback controlled closed‐loop equation are also derived, and simulation results are provided to demonstrate the effect of the proposed control law.
A Novel Robust Pid Controller Design By Fuzzy Neural Network
Authors: Lee Ching‐Hung , Lee Yi‐Hsiung , Teng Ching‐Cheng 
Published: September 2010

ABSTRACTIn this paper, we propose a robust PID controller tuning method for parametric uncertainty systems (or interval plant family) using fuzzy neural networks (FNNs). This robust controller is based on robust gain and phase margin (GM/PM) specifications that satisfy user requirements. Here, the FNN system is used to identify the relation between the PID controller parameters and robust GM/PM. We can use the trained FNN system to determine the parameters of the PID controllers in order to satisfy robust GM/PM specifications that guarantee robustness and performance. Simulation results are shown to illustrate the effectiveness of the robust controller scheme.
Robust Controller Design And Pid Tuning For Multivariable Processes
Authors: Tan Wen , Chen Tongwen , Marquez Horacio J. 
Published: September 2010

ABSTRACTIn this paper, a robust controller design method is first formulated to deal with both performance and robust stability specifications for multivariable processes. The optimum problem is then dealt with using a loop‐shaping H∞ approach, which gives a sub‐optimal solution. Then a PID approximation method is proposed to reduce a high‐order controller. The whole procedure involves selecting several parameters and the computation is simple, so it serves as a PID tuning method for multivariable processes. Examples show that the method is easy to use and the resulting PID settings have good time‐domain performance and robustness.
The Mimo Predictive Pid Controller Design
Authors: Moradi M.H. , Katebi M.R. , Johnson M.A. 
Published: September 2010

ABSTRACTThis paper is concerned with the design of Multi‐Inputs and Multi‐Outputs (MIMO) predictive PID controllers, which have similar performance to that obtainable from model‐based predictive controllers. A new PID control structure is defined which incorporates the prediction of future outputs and uses future set point. A method is proposed to calculate the optimal values of the PID gains from generalised predictive control results. A decentralized version of the predictive PID controllers is presented and the stability of the closed loop system is studied. Simulation studies demonstrate the superior performance of the proposed controller compared with a conventional PID controller. The results are also compared with generalised predictive control solutions.
A Design Of Multiloop Predictive Self‐Tuning Pid Controllers
Authors: Katayama Masaru , Yamamoto Toru , Mada Yasuhiro 
Published: September 2010

ABSTRACTIn this paper, a new design scheme of multiloop predictive self‐tuning PID controllers is proposed for multivariable systems. The proposed scheme firstly uses a static pre‐compensator as an approximately decoupling device, in order to roughly reduced the interaction terms of the controlled object. The static matrix pre‐compensator is adjusted by an on‐line estimator. Furthermore, by regarding the approximately decoupled system as a series of single‐input single‐output subsystems, a single‐input single‐output PID controller is designed for each subsystem. The PID parameters are calculated on‐line based on the relationship between the PID control and the generalized predictive control laws. The proposed scheme is numerically evaluated on a simulation example.
Pd Control For Active Vibration Damping In An Underactuated Nonlinear System
Authors: Aguilar‐lbÅAñez Carlos , Sira‐Ramirez Hebertt 
Published: September 2010

ABSTRACTA Lyapunov based control approach is proposed for the specification of the constant gains of an asymptotically stabilizing Proportional‐Derivative (PD) controller, devised to damp the oscillations of an underactuated nonlinear mechanical system. The system consists of a rigid beam, sliding on the interior of a spherical surface, and an actuated cart, located over the beam, which acts as an active vibration damping element.
Learning Motion of Dexterous Manipulation For A Pair of Multi‐DOF Fingers With Soft‐TIPS
Authors: Nguyen Pham Thuc Anh , Arimoto Suguru 
Published: September 2010

ABSTRACTThis paper shows that a pair of dual multi‐DOF fingers with soft‐tips can learn iteratively a desired periodic motion of manipulation of an object if sensory feedback signals are designed adequately. It is shown that dynamics of the overall fingers and object system satisfy passivity but residual error dynamics for a given periodic posture of the object and a fixed value of contact force satisfy output‐dissipativity only in an approximate sense. Numerical simulation results are presented which show that the pair of fingers manipulating an object is capable of learning iteratively a variety of dexterous motions with a good performance.
Recursive Direct Learning of Control Efforts for Trajectories with Different Magnitude Scales
Authors: Xu Jian‐Xin , Xu Jing , Viswanathan Badrinath 
Published: September 2010

ABSTRACTDirect learning control (DLC) schemes have been developed recently to address non‐repeatable trajectory tracking problems. Unlike conventional iterative learning schemes, DLC schemes learn a set of unknown basis function vectors which can be used to generate the desired control profile of a new trajectory. DLC schemes use all available trajectory tracking information to obtain the unknown basis function vectors in a Least Squares and pointwise manner. A drawback of DLC is that the inverse matrix calculation is inevitable, which is time consuming and may result in singularities due to the batch processing nature.A Recursive Direct Learning Controlmethod is proposed which learns the basis function vectors meanwhile overcomes the implementation difficulties in DLC schemes. The focus of this paper is on learning the control profile of trajectories with same operation period but different magnitude scales. The recursive learning method makes use of one trajectory information at a time, thus avoids the batch processing. The scheme is first developed for a class of nonlinear time varying systems and then extended to cover more general classes of nonlinear systems including robotic manipulator dynamics. Extensive simulation results on a two‐link robotic model are provided to confirm the features of the proposed algorithm.
Iterative Learning Control Using Adjoint Systems and Stable Inversion
Authors: Kinosita Koji , Sogo Takuya , Adachi Norihiko 
Published: September 2010

ABSTRACTIn this paper, we investigate iterative learning control (ILC) for non‐minimum phase systems from a novel viewpoint. For non‐minimum phase systems, the magnitude of a desiredinput obtained by ILC using forward‐time updating and Silverman's inversion are too large because of the influence of the unstable zeros. On the other hand, stable inversion constructs a bounded desired input by using non‐causal inverse for non‐minimum phase systems.In this paper, we first clarify that ILC using an adjoint system achieves the desired input defined by stable inversion. Hence, ILC using an adjoint system is an effective method for the control of non‐minimum phase systems with uncertainty. However, a useful convergence condition of ILC using an adjoint system was not achieved. Next, we develop a simple convergence condition in the frequency domain.
Model‐Based Learning Controls And Their Comparisons Using Lyapunov Direct Method
Authors: Qu Zhihua , Xu Jian‐Xin 
Published: September 2010

ABSTRACTModel‐based learning control of nonlinear systems is studied. Two types of learning algorithms, described by differential equations and/or difference equations to learn unknown time functions, are designed and compared using the Lyapunov's direct method. The time functions to be learned are classified into several classes according to their properties such as continuity, periodicity, and value at the origin of the state space. Conditions are found for iterative learning controls to achieve asymptotic stability and asymptotic learning convergence. For a comparative study, learning capability of a control is defined and, using the criterion, other model‐based controls with learning capability such as adaptive controls and robust controls are investigated. Through the study, iterative learning control is shown to be the one best suited for learning unknown time functions ofknownperiod. Finally, it is shown for the first time that an iterative learning control is directly applicable to systems described by nonlinear partial differential equation.
Robust Analysis of Discrete‐Time Lur'e Systems with Slope Restrictions Using Convex Optimization
Authors: Banjerdpongchai David , Kimura Hidenori 
Published: September 2010

ABSTRACTThis paper considers robust stability and robust performance analysis for discrete‐time linear systems subject to nonlinear uncertainty. The uncertainty set is described by memoryless, time‐invariant, sector bounded, and slope restricted nonlinearities. We first give an overview of the absolute stability criterion based on the Lur'e‐Postkinov Lyapunov function, along with a frequency domain condition. Subsequently, we derive sufficient conditions to compute the upper bounds of the worst case H2 and worst case H∞ performance. For both robust stability testing and robust performance computation, we show that these sufficient conditions can be readily and efficiently determined by performing convex optimization over linear matrix inequalities.
A Dual‐Type Method Based Algorithm for Nonlinear Large Network Optimization Problems
Authors: Lin Shin‐Yeu , Lin Shieh‐Shing , Lin Ch'i‐Hsin 
Published: September 2010

ABSTRACTIn previous research, we have proposed a Dual Projected Pseudo Quasi Newton (DPPQN) method which differs from the conventional Lagrange relaxation method by treating the inequality constraints as the domain of the primal variables in the dual function and using Projection Theory to handle the inequality constraints. We have combined this dual‐type method with a Projected Jacobi (PJ) method to solve nonlinear large network optimization problems with decomposable inequality constraints, and have achieved several attractive features.To retain the attractive features and to remedy the flaw of the previous method, in the current paper, we propose an active set strategy based DPPQN method to solve the projection problem formed by coupling functional inequality constraints. This method associated with the DPPQN method and the PJ method can be used to solve general nonlinear large network optimization problems. We present this algorithm, demonstrate its computational efficiency through numerical simulations and compare it with the previous method.
Adaptive Precision Control of Permanent Magnet Linear Motors
Authors: Huang S.N. , Tan K.K. , Lee T.H. 
Published: September 2010

ABSTRACTThis paper describes an adaptive control scheme developed for precision control of DC permanent magnet linear motors (PMLM). A sliding surface is defined on which a self‐tuning version of robust control capable of achieving tight set‐point regulation is developed, where the control gains are tuned using an adaptive algorithm. Parameter and states convergence properties of the adaptive controller are derived based on the control law formulated. Finally, simulation study and a real‐time experiment are provided to evaluate the performance of the proposed control system.
An Output Tracking VSS Control in The Presence of A Class of Mismatched Uncertainties
Authors: Lee Bore‐Kuen , Mau Li‐Wei 
Published: September 2010

ABSTRACTA variable structure control system for output tracking in the presence of a class of mismatched uncertainty is studied in this paper. For the mismatched uncertainty, we use an unknown input observer to estimate the states and then apply the VSS control methodology to attain asymptotic output tracking for an arbitrary desired trajectory. The robustness of the VSS control system in the presence of mismatched uncertainty is analyzed using the quadratic stability approach.
Simple Recurrent Neural Network‐Based Adaptive Predictive Control for Nonlinear Systems
Authors: Li Xiang , Chen Zengqiang , Yuan Zhuzhi 
Published: September 2010

ABSTRACTMaking use of the neural network universal approximation ability, a nonlinear predictive control scheme is studied in this paper. On the basis of a uniform structure of simple recurrent neural networks, a one‐step neural predictive controller (OSNPC) is designed. The whole closed‐loop system's asymptotic stability and passivity are discussed, and stable conditions for the learning rate are determined based on the Lyapunov stability theory for the whole neural system. The effectiveness of OSNPC is verified via exhaustive simulations.
Adaptive Dynamic Output Feedback Stabilisation of Nonlinear Systems
Authors: Ilchmann A. , Isidori A. 
Published: September 2010

ABSTRACTAn adaptive dynamical output feedback controller is introduced for a class of nonlinear non‐minimum phase systems. This adaptive controller achieves practical stabilisation, that means the output will asymptotically tend to a pre‐specified neighbourhood of the origin. In case of linear systems, we can even prove adaptive stabilisation.
Controller Design Using Walsh‐Basis‐Function Neural Networks
Authors: Chen Shing‐Chia , Chen Wen‐Liang 
Published: September 2010

ABSTRACTThis paper investigates the function approximation problem by using Walsh functions to establish a Walsh‐basis‐function neural network (WBFNN). The proposed novel system avoids the possible heavy computation problem of a controller usually encountered in adaptive neural controller design. With the developed adaptation scheme combined with the sliding mode control strategy for a class of nonlinear systems, the proposed WBFNN‐based controller can guarantee global stability of the closed‐loop system in the Lyapunov sense. The output tracking error then converges to zero asymptotically, and boundedness of all the signals in the whole system is ensured. Simulation validation for a nonlinear unstable system was performed to verify the effectiveness of the proposed controller design.
A Fuzzy Logic Based Discrete Mode Power System Stabilizer
Authors: Chetty M. 
Published: September 2010

ABSTRACTA Fuzzy Power System Stabiliser (FPSS) design using discrete inputs is presented. The FPSS basically utilises only one measurable plant signal as input, namely, the generator speed. The speed signal is discretised resulting in three inputs to the FPSS. A simplified power system represented by a single generator transmitting power to the infinite bus is considered for investigations. The system is simulated in simulink while the FPSS is implemented using Fuzzy Logic Toolbox. There are six rules for the fuzzification and two rules for defuzzification. To provide satisfactory behaviour in the entire region of operation of the generator, additional signals namely, generator's active and reactive power are used as inputs to FPSS. Simulation studies show the superior performance of the proposed FPSS compared to an optimally designed Conventional Power System Stabiliser.
Systematic Gain‐Scheduling Control Design: A Missile Autopilot Example
Authors: Wu Fen , Packard Andy , Balas Gary 
Published: September 2010

ABSTRACTThe missile autopilot was designed using linear parameter‐varying (LPV) control techniques. The controller provides exponential stability guarantee and performance bound in terms of induced L2 norm for the missile plant. The systematic gain‐scheduling approach is motivated by the recent development in LPV control theory and provides a well founded and systematic procedure for high performance missile autopilot design problem.
Adaptive Control For Robotic Manipulators Executing Multilateral Constrained Task
Authors: Kawasaki Haruhisa , Taniuchi Ryo 
Published: September 2010

ABSTRACTThis paper presents a model‐based adaptive control in task coordinates for robotic manipulators executing multilateral constrained tasks The controller works based on the concept of orthogonality between force and motion in the subspaces derived from the constraints. The control gains are independently adjustable in each subspace. The friction force, depending on the contact force, is compensated adaptively. Asymptotic convergence for both force and motion tracking errors is guaranteed by the Lyapunov‐Like Lemma. Experimental results obtained using a 3 D.O.F. robot are given.