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Learning Control (learning + control)
Kinds of Learning Control Selected AbstractsIdentification based adaptive iterative learning controllerASIAN JOURNAL OF CONTROL, Issue 5 2010Suhail Ashraf Abstract In recent years, more research in the control field has been in the area of self-learning and adaptable systems, such as a robot that can teach itself to improve its performance. One of the more promising algorithms for self-learning control systems is Iterative Learning Control (ILC), which is an algorithm capable of tracking a desired trajectory within a specified error limit. Conventional ILC algorithms have the problem of relatively slow convergence rate and adaptability. This paper suggests a novel approach by combining system identification techniques with the proposed ILC approach to overcome the aforementioned problems. The ensuing design procedure is explained and results are accrued from a number of simulation examples. A key point in the proposed scheme is the computation of gain matrices using the steepest descent approach. It has been found that the learning rule can be guaranteed to converge if certain conditions are satisfied. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Tracking control of a piezo-actuated stage based on a frictional model,ASIAN JOURNAL OF CONTROL, Issue 3 2009Yi-Cheng Huang Abstract The tracking control accuracy of a piezoelectric actuator (PEA) is limited due to the actuator's inherent hysteretic nonlinearity. Direct drive of PEA on a positioning stage with friction force will cause control problems. An approximated dynamic model of PEA with consideration of friction force is novel synthesized for control. This model is based on a second-order transfer function with two parameterization terms. The first time delay term consists of the hysteresis of piezo effect combined with frictional force lag with varying velocity. The second term is comprised of both presliding and sliding regimes. The H-infinite tracking controller is designed to compensate for the structural uncertainty associated with time delay and the unstructured frictional force in the PEA stage. Iterative Learning Control is implemented to reduce the unmodeled repetitive error by a factor of 20. Numerical simulations and experimental tests consolidate the root mean square (RMS), positioning error close to the hardware reproducibility and accuracy level. Experimental results show the controlled stage can be potentially used for precise positioning. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Iterative learning control and repetitive control in hard disk drive industry,A tutorialINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2008YangQuan Chen Abstract This paper presents a tutorial on iterative learning control (ILC) and repetitive control (RC) techniques in hard disk drive (HDD) industry for compensation of repeatable runouts (RRO). After each tutorial, an application example is given. For ILC, a simple filtering-free implementation for written-in RRO compensation is presented. For the RC part, a new application of RC in dual-stage HDD servo is presented. Copyright © 2007 John Wiley & Sons, Ltd. [source] Repetitive control of synchronized operations for process applicationsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2007James D. Ratcliffe Abstract Repetitive control (RC) algorithms for a plant, which contain pairs of complex conjugate poles at low frequency, resulting in a resonant system, is the subject area of this paper where the experimental results given are for a gantry robot and conveyor system in which the gantry is required to transfer payloads to a constant velocity conveyor by performing a repeating ,pick and place' operation. Initially, the gantry robot is controlled by means of a PID feedback controller in parallel with a proportional (P-type) repetitive feed-forward loop, while the conveyor operates under proportional feedback control. It is found that the RC system is unable to achieve long-term performance. The performance degrades within a relatively small number of repetitions due to the build up of resonant frequencies in the learning loop. To prevent this, a batch aliasing technique, originally developed for iterative learning control, is modified to work in the RC framework, and is implemented in real-time. The superior performance potential of the aliasing system is demonstrated experimentally. In the second part of this paper, multi-machine systems, are considered where the critical new factor is the relative error between the conveyor and the robot. Here a second supervisory learning loop is proposed for use to shift the reference trajectory of one machine so that the relative placement error is also reduced. Again, supporting experimental results are given. Copyright © 2006 John Wiley & Sons, Ltd. [source] Model reference adaptive iterative learning control for linear systemsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 9 2006A. Tayebi Abstract In this paper, we propose a model reference adaptive control (MRAC) strategy for continuous-time single-input single-output (SISO) linear time-invariant (LTI) systems with unknown parameters, performing repetitive tasks. This is achieved through the introduction of a discrete-type parametric adaptation law in the ,iteration domain', which is directly obtained from the continuous-time parametric adaptation law used in standard MRAC schemes. In fact, at the first iteration, we apply a standard MRAC to the system under consideration, while for the subsequent iterations, the parameters are appropriately updated along the iteration-axis, in order to enhance the tracking performance from iteration to iteration. This approach is referred to as the model reference adaptive iterative learning control (MRAILC). In the case of systems with relative degree one, we obtain a pointwise convergence of the tracking error to zero, over the whole finite time interval, when the number of iterations tends to infinity. In the general case, i.e. systems with arbitrary relative degree, we show that the tracking error converges to a prescribed small domain around zero, over the whole finite time interval, when the number of iterations tends to infinity. It is worth noting that this approach allows: (1) to extend existing MRAC schemes, in a straightforward manner, to repetitive systems; (2) to avoid the use of the output time derivatives, which are generally required in traditional iterative learning control (ILC) strategies dealing with systems with high relative degree; (3) to handle systems with multiple tracking objectives (i.e. the desired trajectory can be iteration-varying). Finally, simulation results are carried out to support the theoretical development. Copyright © 2006 John Wiley & Sons, Ltd. [source] Convergence theory for multi-input discrete-time iterative learning control with Coulomb friction, continuous outputs, and input boundsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2004Brian J. Driessen Abstract In this paper we consider the problem of discrete-time iterative learning control (ILC) for position trajectory tracking of multiple-input, multiple-output systems with Coulomb friction, bounds on the inputs, and equal static and sliding coefficients of friction. We present an ILC controller and a proof of convergence to zero tracking error, provided the associated learning gain matrices are scalar-scaled with a sufficiently small positive scalar. We also show that non-diagonal learning gain matrices satisfying the same prescribed conditions do not lead to the same convergence property. To the best of our knowledge, for problems with Coulomb friction, this paper represents a first convergence theory for the discrete-time ILC problem with multiple-bounded-inputs and multiple-outputs; previous work presented theory only for the single-input, single-output problem. Copyright © 2004 John Wiley & Sons, Ltd. [source] Closed-loop iterative learning control for non-linear systems with initial shiftsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 7 2002Mingxuan Sun Abstract This paper is concerned with the problem of the iterative learning control with current cycle feedback for a class of non-linear systems with well-defined relative degree. The tracking error caused by a non-zero initial shift is detected as extended D-type learning algorithm is applied. The defect is overcome by adding terms including the output error, its derivatives as well as integrals. Asymptotic tracking of the final output to the desired trajectory is guaranteed. As an alternative approach, an initial rectifying action is introduced in the extended D-type learning algorithm and shown effective to achieve the desired trajectory jointed smoothly with a transitional trajectory from the starting position. Also these algorithms with adjustable tracking interval ensure better robustness performance in the presence of initial shifts. Numerical simulation is conducted to demonstrate the theoretical results. Copyright © 2002 John Wiley & Sons, Ltd. [source] Robust monotone gradient-based discrete-time iterative learning controlINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 6 2009D. H. Owens Abstract This paper considers the use of matrix models and the robustness of a gradient-based iterative learning control (ILC) algorithm using both fixed learning gains and nonlinear data-dependent gains derived from parameter optimization. The philosophy of the paper is to ensure monotonic convergence with respect to the mean-square value of the error time series. The paper provides a complete and rigorous analysis for the systematic use of the well-known matrix models in ILC. Matrix models provide necessary and sufficient conditions for robust monotonic convergence. They also permit the construction of accurate sufficient frequency domain conditions for robust monotonic convergence on finite time intervals for both causal and non-causal controller dynamics. The results are compared with recently published results for robust inverse-model-based ILC algorithms and it is seen that the algorithm has the potential to improve the robustness to high-frequency modelling errors, provided that resonances within the plant bandwidth have been suppressed by feedback or series compensation. Copyright © 2008 John Wiley & Sons, Ltd. [source] Robust stability of iterative learning control schemesINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 10 2008Mark French Abstract A notion of robust stability is developed for iterative learning control in the context of disturbance attenuation. The size of the unmodelled dynamics is captured via a gap distance, which in turn is related to the standard ,2 gap metric, and the resulting robustness certificate is qualitatively equivalent to that obtained in classical robust ,, theory. A bound on the robust stability margin for a specific adaptive ILC design is established. Copyright © 2007 John Wiley & Sons, Ltd. [source] Predictor-based repetitive learning control for a class of remote control nonlinear systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 16 2007Ya-Jun Pan Abstract In this paper, a repetitive learning control (RLC) approach is proposed for a class of remote control nonlinear systems satisfying the global Lipschitz condition. The proposed approach is to deal with the remote tracking control problem when the environment is periodic or repeatable over infinite time domain. Since there exist time delays in the two transmission channels: from the controller to the actuator and from the sensor to the controller, tracking a desired trajectory through a remote controller is not an easy task. In order to solve the problem caused by time delays, a predictor is designed on the controller side to predict the future state of the nonlinear system based on the delayed measurements from the sensor. The convergence of the estimation error of the predictor is ensured. The gain design of the predictor applies linear matrix inequality (LMI) techniques developed by Lyapunov Kravoskii method for time delay systems. The RLC law is constructed based on the feedback error from the predicted state. The overall tracking error tends to zero asymptotically over iterations. The proof of the stability is based on a constructed Lyapunov function related to the Lyapunov Kravoskii functional used for the proof of the predictor's convergence. By well incorporating the predictor and the RLC controller, the system state tracks the desired trajectory independent of the influence of time delays. A numerical simulation example is shown to verify the effectiveness of the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd. [source] Sampled-data iterative learning control with well-defined relative degreeINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2004Mingxuan Sun Abstract This paper addresses the problem of iterative learning control with well-defined relative degree. The solution is a family of sampled-data learning algorithms using lower-order differentiations of the tracking error with the order less than the relative degree. A unified convergence condition for the family of learning algorithms is derived and is proved to be independent of the highest order of the differentiations. In the presence of initial condition errors, the system output is ensured to converge to the desired trajectory with a specified error bound at each sampling instant. The bound will reduce to zero whenever the bound on initial condition errors tends to zero. Numerical examples are provided to illustrate the tracking performance of the proposed learning algorithms. Copyright © 2004 John Wiley & Sons, Ltd. [source] Multi-objective learning control for robotic manipulatorJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 10 2004Khin Kyu Kyu Win Several types of learning controllers have been proposed in the literature to improve the tracking performance of robot manipulators. In most cases, the learning algorithms emphasize mainly on a single objective of learning a desired motion of the end-effector. In some applications, more than one objective may be specified at the same time. For example, a robot may be required to follow a desired trajectory (primary objective) and at the same time avoid an obstacle (secondary objective). Thus, multi-objective learning control can be more effective to realize the collision-free tasks. In this paper, a multi-objective learning control problem is formulated and solved. In the proposed learning control system, the primary objective is to track a desired end-effector's motion and several secondary objectives can be specified for the desired orientation and for obstacles avoidance. To avoid obstacles in the workspace, a new learning concept called "region learning control" is also proposed in this paper. The proposed learning controllers do not require the exact knowledge of robot kinematics and dynamics. Sufficient condition is presented to guarantee the convergence of the learning system. The proposed learning controllers are applied to a four-link planar redundant manipulator and simulation results are presented to illustrate the performance. © 2004 Wiley Periodicals, Inc. [source] Robustness of time-scale learning of robot motions to uncertainty in acquired knowledgeJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 10 2001C.C. Cheah A disadvantage of present iterative learning control algorithms is that they are generally applicable only in cases where a certain task is performed over and over again. Consequently, if knowledge or control inputs acquired from learning a task can be used on similar tasks, learning will be more efficient. Recently, several methods for constructing the control input of a new motion based on the control inputs acquired from previous learning of similar tasks have been proposed. However, these methods assumed that the perfect control inputs could be obtained from the previous learning. In practice, the control inputs could never be obtained exactly from learning in the presence of certain uncertainties such as disturbance and measurement noises. In addition, it is also not known for sure how the basic motion patterns should be chosen for learning. In this article, the robustness problem of the time-scale learning control to uncertainty in the acquired learning control inputs is formulated and solved. From the analysis, certain new insights such as its implication to choices of basic motion patterns for time-scale learning will be discussed. Simulation results of a 3-link robot are presented to illustrate the analysis. © 2001 John Wiley & Sons, Inc. [source] Model predictive control with learning-type set-point: Application to artificial pancreatic ,-cellAICHE JOURNAL, Issue 6 2010Youqing Wang Abstract A novel combination of model predictive control (MPC) and iterative learning control (ILC), referred to learning-type MPC (L-MPC), is proposed for closed-loop control in an artificial pancreatic ,-cell. The main motivation for L-MPC is the repetitive nature of glucose-meal-insulin dynamics over a 24-h period. L-MPC learns from an individual's lifestyle, inducing the control performance to improve from day to day. The proposed method is first tested on the Adult Average subject presented in the UVa/Padova diabetes simulator. After 20 days, the blood glucose concentrations can be kept within 68,145 mg/dl when the meals are repetitive. L-MPC can produce superior control performance compared with that achieved under MPC. In addition, L-MPC is robust to random variations in meal sizes within ±75% of the nominal value or meal timings within ±60 min. Furthermore, the robustness of L-MPC to subject variability is validated on Adults 1,10 in the UVa/Padova simulator. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source] Robust iterative learning control design for batch processes with uncertain perturbations and initializationAICHE JOURNAL, Issue 6 2006Jia Shi Abstract A robust iterative learning control (ILC) scheme for batch processes with uncertain perturbations and initial conditions is developed. The proposed ILC design is transformed into a robust control design of a 2-D Fornasini,Marchsini model with uncertain parameter perturbations. The concepts of robust stabilities and convergences along batch and time axes are introduced. The proposed design leads to nature integration of an output feedback control and a feedforward ILC to guarantee the robust convergence along both the time and the cycle directions. This design framework also allows easy enhancement of the feedback and/or feedforward controls of the system by extending the learning information along the time and/or the cycle directions. The proposed analysis and design are formulated as matrix inequality conditions that can be solved by an algorithm based on linear matrix inequality. Application to control injection packing pressure shows the proposed ILC scheme and its design are effective. © 2006 American Institute of Chemical Engineers AIChE J, 2006 [source] Simple LMI based learning control design,ASIAN JOURNAL OF CONTROL, Issue 1 2009Yongqiang Ye Abstract In this note, a simple linear matrix inequality (LMI) design method is proposed for iterative learning control (ILC). The design can ensure a monotonic error decay in 2-norm. Experimental results on a SCARA robot shows that the design can achieve nearly perfect tracking. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] | ||||||||||