A multiple-model adaptive tracking scheme for high-speed train motion control

Multiple model adaptive control schemes offer the potential of improved performance over conventional single model schemes in a number of cases of practical interest. This paper describes modifications to enhance the performance of one class of such schemes when applied to linear and (potentially) non-linear processes.

Hydraulic turbines are known to have complex and highly nonlinear dynamics with some varying parameters, especially in the presence of large or abrupt disturbances. In this study, the weighted multiple model adaptive control scheme was used as a controller for a hydraulic turbine model, with the aim of providing desirable characteristics such as robustness and stability. The system of comprised four candidate plant models with four corresponding candidate PID controllers which were tuned according to the phase shifting method. The plant operation, subjected to both abruptly changing and smoothly changing load conditions, was simulated. From the results, it was found that the proposed controller showed very good performance for both types of disturbance changes.

This paper develops a multiple fixed model blending based adaptive parameter identification scheme for multi-input multi-output (MIMO) systems with polytopic parameter uncertainty. The developed identification scheme is proven to be asymptotically stable for uncertain linear time-invariant MIMO systems, and is shown to provide fast adaptation for even uncertain linear time-varying (LTV) systems. Furthermore, utilizing the proposed parameter identification scheme, a linear-quadratic (LQ) optimal multiple model adaptive control (MMAC) scheme is developed for linear MIMO systems with polytopic uncertainties. The proposed MMAC scheme is applied to tracking control of uncertain lateral vehicle dynamics. A set of simulation test results are presented to verify the stability and effectiveness of the proposed MMAC scheme.

Because of increased requirements on the reliability, maneuverability and survivability of modern highspeed trains, more and more advanced control theory is applied against actuator failures. The control reallocation and adaptive control have long been attracting research interest in the field of fault-tolerant control, which is reflected in the design and development of powerful and complex control systems against actuator failures. In this paper we investigate the velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers in the case of actuator failures. First, the multiple point-mass model is built and the common actuator failures of the train operation control system are divided into two types: stuck in a fixed value and loss of effectiveness. When plant inputs are stuck in a fixed value, a novel approach based on the control reallocation theory is implemented to ensure the system stability and performance. Meanwhile when the loss of the actuator effectiveness occurs, a robust adaptive fault-tolerant control method is applied. Then, the effectiveness of the proposed approach is also confirmed through numerical simulations based on a train similar to China Railways High-speed 5 (CRH5).

Multiple Radial Basis Function (RBF) neural network models are used to approximate a kind of discrete time nonlinear system described by a combination of nonlinear and linear part. Based on these models, Multiple model adaptive control strategy is given to improve the transient response of closed-loop system. Simulations according to different uncertain changing parameters in the model of the system are studied to show the efficiency of the proposed method.

Recently, a class of adaptive control schemes called L1 Adaptive Control (L1-AC) has been proposed and widely advertised in aerospace control for achieving fast and robust adaptation and better performance than the existing Model Reference Adaptive Control (MRAC) Schemes. The L1-AC scheme is designed mainly for plants with full state measurement even though the name L1-AC has been used as an umbrella name for more general classes of plants. In this paper, we show that the L1-AC for plants with measured states is simply a standard MRAC with a low pass filter inserted in front of the control input. The analysis of the scheme is almost identical to that of MRAC as the same Lyapunov function is used to establish stability. The motivation for using the filter is the fact that for this class of adaptive schemes i.e. MRAC for plants with full state measurement the tracking error can be made arbitrarily small during transient by increasing the adaptive gain. A high adaptive gain however makes the differential equation of the adaptive law or estimator very stiff and leads to numerical problems that cause high oscillations in the estimated parameters leading to loss of adaptivity and deviations from what the theoretical properties dictate. The L1-AC approach mistook these numerical oscillations as properties of the adaptive scheme and inserted an input low pass filter in order to filter them out. While the filter helps reduce the frequency of these oscillations in the control law the price paid is high. First the numerical instability does not go away and the estimated parameters continue to oscillate without converging to the true parameters even in the presence of sufficiently rich signals. Second, due to the filter the tracking error is no longer guaranteed to converge to zero and the transient bounds for the tracking error also depend on the filter. As a result, the tracking properties of the L1-AC scheme are worse than what a simple MRAC scheme can generate with adaptive gains that could be high but away from the region of numerical instability. In addition, the presence of the filter reduces the robust stability margins in the presence of unmodeled dynamics and provides literally no advantage as simple robust MRAC techniques can solve the same problem achieving much better properties. The authors of L1-AC often compare the properties of a numerically unstable MRAC due to extremely high adaptive gains something that is prohibited by robust adaptive control with those of the filtered MRAC aka L1-AC to show that L1-AC performs better. Such comparisons are not only misleading but do not reveal what causes what giving the reader a false impression of a new theory that results to new performance and robustness improvements. The use of filters in adaptive control is not new and they are used to improve the performance and robustness of certain adaptive control schemes without destroying their ideal tracking properties. In this paper we present such a scheme that guarantees good transient performance and robustness and reveals the trade off between transient performance and robust stability.

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

An adaptive control scheme of digital loop filter for automatic frequency control (AFC) is proposed to improve LTE downlink throughput in high-speed train (HST) channel. A mobile device in HST channel suffers from performance degradation due to the abrupt change in Doppler shift. In this case, a higher loop gain is desirable to quickly track the Doppler shift. However, in general, there is a fundamental trade-off between locking time and phase jitter performance in AFC. Therefore, it is crucial to identify the channel condition for adjusting AFC loop gain. In this paper, a wireless channel classification problem is formulated and the optimal classifier is derived by using support vector machine (SVM) algorithm. In particular, HST and non-HST channel environments are classified by exploiting the statistical distribution of the phase estimates. Experimental results show that the SVM-based adaptive loop gain control improves the throughput in HST channel.

Blending based multiple-model adaptive control of multivariable systems with application to lateral vehicle motion control

Output-feedback binary adaptive control of MIMO systems using monitoring function

Traditional adaptive control assumes full control authority and lacks an adequate theoretical treatment for control in the presence of actuator saturation limits. The adaptive dynamic inversion control methodology, which uses dynamic inversion to calculate the control and adaptation to compensate for errors in the inversion due to model uncertainties, also lacks an adequate theoretical treatment for saturation. This paper investigates the problems introduced in adaptive dynamic inversion control schemes due to bounds on the control, and develops a three component control scheme to overcome them. The main contribution of the paper is determination of the maximum possible domain of attraction with respect to the control position limit, and development of a control switching strategy to contain the plant within the maximum possible domain of attraction. This strategy ensures boundedness of the state by restricting it within the Domain of Control Authority. A direction consistent control constraint mechanism was also developed, to maintain the resultant direction of the rate of change of state to be the same as that of the desired, even in the presence of control saturation. Finally, a modified adaptation mechanism was implemented to prevent incorrect adaptation arising from trajectory errors due to control saturation. Mathematical development of the control laws and the adaptation mechanisms is presented, along with proofs for convergence of the tracking error and stability of the overall control scheme. To demonstrate the control scheme, two different numerical simulations for rigid spacecraft attitude tracking with uncertain inertias and saturated controls are presented. Results show that the control scheme successfully handles adaptive dynamic inversion control of systems with dynamics that are nonlinear in terms of the state, with uncertain parameters that appear linearly, in the presence of initial condition errors and control position bounds, and nonlinear saturation constraints on the components of the control.

In this paper, adaptive control approach with uncertain parameters or not for 2D discrete-time Ushiki system is discussed both theoretically and numerically. Based on the Lyapunov stability theorem and the chaotic controlling methods, adaptive control scheme with uncertain parameters or not is given and illustrated with Ushiki system as example. Numerical simulations are presented to demonstrate the effectiveness of the proposed synchronization scheme.

Physics-based models, particularly urban canopy models (UCM), have been widely used to investigate urban microclimate conditions of various urban areas. UCMs have been developed to simulate the interaction between buildings, roads, and vegetation, including the surface energy balance, to predict urban climate. Despite its developments, deployment of UCM for city-scale urban simulations presents major challenges as follows: (1) UCM requires many uncertain input parameters, referred to as parameter uncertainty, due to the lack of data availability; (2) UCM is based on the concept of urban canyon that assumes homogeneous urban settings with one representative building geometry, and this assumption pertains to model-form uncertainty. Few studies attempted to address the parameter uncertainty via calibration of uncertain input parameters. Other some studies have compared the prediction accuracy of multiple UCM types or integration of UCMs with different models. Yet, the effect of homogeneous urban canyon assumption on the model discrepancy have not been sufficiently explored and there is no knowledge base to identify key urban factors, unrepresented in UCMs, relating to model discrepancy.To address the research gap, this work aims to improve the prediction accuracy of an existing UCM model for wind speed prediction by handling both parameter uncertainty and model-form uncertainty under various urban contexts. First, an existing UCM, specifically the vertical city weather generator (VCWG) model, was used to predict wind speed at 248 weather stations in Seoul, Republic of Korea. Our previous study demonstrated that the root-mean-square error (RMSE) of VCWG wind speed model at 248 weather stations ranged between 0.14 and 20.14 m/s [1]. Second, we calibrated three important uncertain parameters selected on the basis of sensitivity analysis results, namely turbulence coefficient during unstable conditions, pressure gradient coefficient, and aerodynamic roughness length. Third, to address model form uncertainty, we identified urban factors that are not represented in the VCWG model, such as urban heterogeneity, topography, and blue infrastructure. Fourth, we develop statistical model(s) associated with unrepresented urban factors to quantify the model-form uncertainty and, ultimately, reduce the gap between the model and reality. It is noteworthy that the 248 weather stations used were deployed in different vertical layers: urban canopy-level, roughness sublayer-level, and inertial sublayer-level, which allows to improve UCM prediction in different vertical heights. This study will deliver a calibrated VCWG model that is able to accurately predict actual wind speed and provide a knowledge base regarding the possible causes of model discrepancies.

Fractional adaptive SMC fault tolerant control against actuator failures for wing rock supervision

An adaptive control approach is proposed to deal with the multiple manipulators consensus problem based on the multi-agent theory. In the current multi-agent literature, agents were assumed to have determined models. However, the practical manipulator's kinematics contains uncertain parameters. By using the projection method, the adaptive updating law for uncertain kinematic parameters is derived. Then, the estimated manipulator Jacobian matrix can be obtained to design the decentralized controller. By the proposed controller, all the manipulators' end-effectors move towards the same configuration to achieve certain coordination tasks. In addition, performance of the control system is analyzed by the Lyapunov method, and the consensus error is proved to approach zero. Finally, the effectiveness of the proposed scheme is illustrated by simulations on a multiple PUMA 560 robots system.