Safanah M. Raafat

Improved intelligent identification of uncertainty bounds: design, model validation and stability analysis

Identification of uncertainty bounds in robust control design is known to be a critical issue that attracts the attention of research in robust control field recently. Nevertheless, the practical implementation involves a trial and error procedure, which depends on the designer prior knowledge and the available information about the system under study. Artificial intelligent techniques provide a suitable solution to such a problem. In this paper a new intelligent identification method of uncertainty bound utilises an adaptive neuro-fuzzy inference system (ANFIS) in an enhanced feedback scheme is proposed. The proposed ANFIS structure enables accurate determination of the uncertainty bounds and guarantees robust stability and performance. In our proposed technique, the validation of the intelligent identified uncertainty weighting function is based on the measurement of both the v-gap metric and the stability margin that result from the corresponding robust controller design. Additionally, these two indices are used to improve the accuracy of the intelligent estimation of uncertainty bound in conjunction with the robust control design requirements. The enhanced intelligent identification of uncertainty bound is demonstrated on a servo positioning system. Simulation and experimental results proves the validity of the applied approach; more reliable and highly efficient estimation of the uncertainty weighting function for robust controller design.

Survey on robust control of precision positioning systems

A very high accuracy and resolution, stability, and fast response are the requirements of high precision positioning systems (HPPS). Accurate position sensing and feedback control of the motion are key to the successful precision positioning. In this paper, the robust control of HPPS literature published within last two decades is categorized and discussed. The paper also provides detailed technical descriptions on many such novel methods. According to the type of the control methodology applied in the reviewed literature, the paper categorizes them into seven main categories. The purpose is to emphasize the key role of advanced control techniques in improving precision, accuracy, and the speed of operation of these systems. While doing so, the paper also reviews literature on various applications of robust control for HPPS including some recent patents. However, the list of patents reviewed is not the least assumed to be complete or general. Nevertheless, the evolution and trends in the applications of robust control methods are shown. Based on the analysis of the reviewed patents, the expected future developments in this domain are highlighted.

Improving trajectory tracking of a three axis SCARA robot using neural networks

In this paper, a neural-network based robust adaptive controller is proposed to control an industrial robot considering non- linearities, uncertainties and external perturbations. Three-axis SCARA robots is used to test the performance of this controller. The nonlinear system is treated as a partially known system. The known dynamic is used to design a nominal feedback controller based on the well-known feedback linearization method and PD controller. A Variable Structure Controller is added to the PD loop to provide robustness to uncertainties in the model of the system in order to improve accuracy of the trajectory tracking. A Neural Network (NN) based robust adaptive tracking controller is applied to further improves the control action. The outputs of the NNs are used to compensate the effects of the system uncertainties and to improve the tracking performance. Using this scheme, strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, the output tracking error between the plant output and the desired reference output can asymptotically converge to zero as well. This controller exhibited superior performance characteristics where the maximum absolute error for the three-axis SCARA robot is considerably reduced.

Intelligent disturbance rejection for robust tracking performance of X-Y positioning system

In this paper, design and implementation of an intelligent disturbance weighting function for H∞-based precision positioning control system is presented for an AC motor drive X-Y table positioning mechanism. The system works with a robust feedback controlled, enhanced by an outer loop integral control. The objective is to achieve high tracking and contouring performances even in the presence of uncertainties and disturbances. The effect of crosstalk between the two axes is considered as disturbance that is treated by intelligent estimated disturbance weighting functions using adaptive neuro fuzzy inference system (ANFIS). The developed controller is implemented experimentally, and a tracking performance of less than 1µm is achieved. Contouring error of less than 1µm is achieved as well.

Enhanced servo performance of a single axis positioning system in an intelligent robust framework

This paper proposes an optimized performance of an intelligent H∞ robust controller of a single axis positioning system. The objective is to achieve wider bandwidth, better resolution, and robustness to modeling uncertainties. The main contribution is the combination of intelligent uncertainty weighting function and optimized weighting function in an H∞ robust controller design. The main distinguishing features of this approach are: the accurate, fast identification of the uncertainty bounds using an adaptive neuro fuzzy inference system and the automatic tuning of the performance weighting function in accordance to performance requirements. v-gap measure is utilized to validate the intelligent identified uncertainty bounds for wider stability region. Then the methodology is demonstrated through both simulation and experiments on the practical system. Experimental results also demonstrate the robustness against load variations.

Robust identification of a single axis high precision positioning system

Robust control has been studied in recent years as an efficient methodology for the design of highly performing controllers of positioning systems. However, it is necessary to provide accurate nominal model and associated uncertainty bounds to design this type of controllers. The aim of this paper is to robustly identify a single axis high precision positioning system with uncertainties. For a given data set a nominal model is identified then model error modeling techniques are used to handle uncertainties in modeling. To examine the quality of the identified nominal and modeling error models H∞ is designed based on the identified uncertainties. Computer simulation and experimenting with the high precision positioning system verify that the obtained nominal model and modeling error model are reliable and the implemented approach can be used to develop an integrated identification and robust controller.

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFB-ICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance.

Bounded constrained optimization of performance weighting function for precise robust positioning control system

In this paper, we present a robust control design and analysis for a single axis servo positioning system. A new method that is based on inequalities and bounded constrained optimization technique is developed for tuning the performance weighting function. The effect of parameter changes in the system model are treated as a set of parametric uncertainties. It is shown that the proposed method can simplify the design procedure of Hoo robust controller design. The developed controllers are implemented experimentally; high robustness, precision, and high bandwidth are achieved.

Intelligent identification of uncertainty bounds for robust servo controlled system

In this paper a new intelligent identification method of uncertainty bound utilizes an adaptive neuro-fuzzy inference system (ANFIS) in a feedback scheme is proposed. The proposed ANFIS feedback structure performs better in determining the uncertainty bounds with minimum number of iterations and error. In our proposed technique, the intelligent identified uncertainty weighting function is validated utilizing v-gap to ensure the stability of the designed H∞ controlled system. Our proposed intelligent identification of uncertainty bound is demonstrated on a servo motion system. Simulation and experimental results show that the new ANFIS identifier is more reliable and highly efficient in estimating the best uncertainty weighting function for robust controller design.

Comprehensive modeling for simulation and control of a single axis high precision positioning system

In this paper, a high precision positioning system identification is studied and analyzed to obtain a suitable model for simulation and control. The selected model must capture most of system dynamics. Second order system model with one pole at the origin proves to be a good selection. Systems friction nonlinearity is considered as well; Both LuGre and Stick-Slip friction models were simulated and tested experimentally. The later proves to be more accurate. Computer simulation and experimenting with the high precision positioning system verify that the obtained model is reliable and can be used in control application.