Time-varying System Parameters Research Articles

Application of Adaptive Sliding Mode Control for Regenerative Braking Torque Control

In air hybrid vehicles, there are two independent braking systems: frictional and regenerative. Since the regenerative braking torque is proportional to the parameters such as tank pressure and engine speed, a controller is needed for the control of the regenerative braking torque generated by internal combustion engine, based on the driver preference. In this work, a nonlinear control approach based on adaptive sliding-mode control (ASMC) is employed to tackle the problem of engine torque control during regenerative mode. To this end, a novel mean value model for a recently proposed cam-based air hybrid engine is derived for the regenerative mode and employed for designing the controller. The adaptive sliding-mode controller incorporates the approximately known inverse dynamic model output of the engine as a model-base component of the controller, and an estimated uncertainty term to compensate for the unmodeled dynamics, external disturbances (e.g., gear shifting), and time-varying system parameters such as tank pressure. The robustness and performance of the controller for this particular application is investigated and compared with that of a high-gain PID controller and a smooth sliding-mode controller numerically and experimentally. The results show that the controller performs remarkably well in terms of the robustness, tracking error convergence, and disturbance attenuation. Chattering effect is also removed by utilizing the ASMC scheme.

Research on Intelligent Synchronization Control of Erecting System Driven by Two Hydraulic Cylinders

The synchronization control problem of a large equipment erecting system driven by two oil cylinders side-by-side is analyzed. A closed loop control scheme of hydraulically driven erecting system based on electro-hydraulic proportion control valve is given. Considering the existence of time-varying parameters of hydraulic system, intelligent PID control algorithm is implemented by adding neurotic adaptive element control approach to improve the adaptive control capacity of the controller. Simulation results show that the intelligent PID control algorithm is more effective than conventional method for the erecting system synchronization control.

A Study of Vehicle Dynamics Stability Based on Fuzzy Control

Electronic Stability Program (ESP) has become the focus of the study in the field of automotive active safety and chassis control in recent years, which was developed from ABS and TCS. ESP mainly works through adjusting the size and the distribution of the longitudinal tire force. ESP can make vehicle produce effective yaw moment to restrain oversteering or understeering, However, ESP is a typical nonlinear, time-delay, time-varying parameter system and its mathematical model is very complex. It is difficult to design the control model by traditional control theory. Fuzzy control does not depend on a precise mathematical model. It is employed to handle complicated questions of nonlinear dynamics. First, in this paper, the7-DOF of vehicle dynamics model based on the H. B. Pacejka tyre model (magic formula) and vehicle reference model were established by using the MATLAB/SIMULINK. Then by using fuzzy control principle to direct at the nonlinear, time varying characteristics of the ESP system, a controller of yaw rate based on fuzzy control was designed. An analysis of the simulation results of J-turn and lane change on slippery road surface shows that the present stability control system based on the yaw rate is effective in maintaining the yaw rate and the sideslip angle within the optimal range, thus improving the vehicle stability.

A data-driven PID control system using particle swarm optimisation

Most of process systems such as chemical plants are considered as non-linear systems. The global linear approximation for the systems with the strong non-linearities might cause the large modelling error. It is then difficult to obtain the good control performance, even if the controllers are suitably designed based on the models. In this paper, a design method of the PID control system with the data-driven modelling function has been proposed. In the proposed method, local linear models are designed with the multiple datasets selected from the database when needed. Also, the time-variant system parameters are automatically adjusted by using the particle swarm optimisation. Finally, the effectiveness of the proposed method is numerically evaluated through applications to the non-linear systems and the time-variant systems.

Frequency Domain Total Least Squares Identification of Linear, Periodically Time-Varying Systems from noisy input-output data

AbstractThis paper presents an extension of the well known linear time invariant identification theory to Linear, Periodically Time-Varying (LPTV) systems. The considered class of systems is described by ordinary differential equations with coefficients that vary periodically over time, making use of multisines both for excitations as well as for the time-varying system parameters. To solve the model equation, an efficient frequency domain simulator is built and is compared with the classically time integration solvers. Further, a frequency domain identification algorithm is proposed within an errors-in-variables stochastic framework. This approach determines a parametric model for the LPTV-system from noisy input-output data. The developed estimation theory is also verified on a simulation example.

Transient analysis of queues for peer-based multimedia content delivery

Consider a firm that sells online multimedia content. In order to manage costs and quality of service, this firm maintains a peer network that allows new users to download files from their peers who have previously downloaded the required files. The scenario can be modeled as a queueing system where the number of servers varies over time. Analytical models are developed that are based on fluid and diffusion approximations and allow analysis of transient system performance. The same approximations are used to analyze the steady-state behavior of this network. It is shown that the existing fluid and diffusion approximations are inaccurate for transient analysis. To address this shortcoming, a novel Gaussian-based adjustment is proposed and it significantly improves the accuracy of the approximations. Furthermore, the models used in this research can be extended seamlessly to the case of time-varying system parameters (e.g., arrival rates and service rates). Several numerical examples are provided that show how the proposed adjusted models work for the analysis of transient phenomena.

Prediction of Modal Parameters of Linear Time-Varying Systems

Many engineering structures, such as cranes, traffic-excited bridges, flexible mechanisms and robotic devices exhibit characteristics that vary with time and are referred to as time-varying or nonstationary. In particular, linear time-varying (LTV) systems have been often dealt with on a case-by-case basis. Many concepts and analytic methods of linear time-invariant (LTI) systems cannot be applied to LTV systems, as for example the conventional definition of modal parameters. In fact, LTV systems violate one of the assumptions of the conventional modal analysis, which is stationarity.

Prediction of Modal Parameters of Linear Time-Varying Systems

Many engineering structures, such as cranes, traffic-excited bridges, flexible mechanisms and robotic devices exhibit characteristics that vary with time and are referred to as time-varying or nonstationary. In particular, linear time-varying (LTV) systems have been often dealt with on a case-by-case basis. Many concepts and analytic methods of linear time-invariant (LTI) systems cannot be applied to LTV systems, as for example the conventional definition of modal parameters. In fact, LTV systems violate one of the assumptions of the conventional modal analysis, which is stationarity.Subspace-based identification methods, proposed in the 1970s, have been attracting much attention due to their affinity to the modern control theory, which is based on the state space model. These methods are now successfully applied to many industrial cases and may be considered reference methods for identifying LTI systems.In this paper the use of a subspace-based method for identifying LTV systems is discussed and applied to both numerical and experimental systems. More precisely a modified version of the SSI method, referred to here as ST-SSI (Short Time Stochastic Subspace Identification) is introduced as well as a method for predicting time-varying stochastic systems using the angle variation between the subspaces; the latter is able to predict the system parameter in the “near” future.

Quaternion Observer-Based Model-Independent Attitude Tracking Control of Spacecraft

Based on the separation property for a rigid-body tracking control problem established by Seo and Akella, this work explores a model-independent and observer-based attitude tracking control method in which not only the unavailability of direct/accurate measurement of quaternion attitude is considered, but the inevitable factors of external disturbances and uncertain (or even time-varying) system parameters as well as actuation saturation are also explicitly addressed. The proposed control scheme is essentially model-independent in that the system dynamic model (valid for any rigid-body spacecraft) is used for stability analysis only, but not actually needed for setting up and implementing the proposed control strategy. As such, there is no need for extensively redesigning or reprogramming the control algorithms, even if unexpected external disturbances occur or system parameters/ dynamics change during system operation. Those features, highly desirable in practice, are conformed and verified via theoretical analysis and numerical simulations.

Adaptive Quadratic Sum-Squares Error for Structural Damage Identification

The ability to detect damages online, based on vibration data measured from sensors, will ensure the reliability and safety of structures. Innovative data analysis techniques for the damage detection of structures have received considerable attention recently, although the problem is quite challenging. In this paper, we proposed a new data analysis method, referred to as the quadratic sum-squares error (QSSE) approach, for the online or almost online identification of structural parameters. Analytical recursive solution for the proposed QSSE method, which is not available in the previous literature, is derived and presented. Further, an adaptive tracking technique recently proposed is implemented in the proposed QSSE approach to identify the time-varying system parameters of the structure, referred to as the adaptive quadratic sum-squares error. The accuracy and effectiveness of the proposed approach are demonstrated using both linear and nonlinear structures. Simulation results using the finite-element models demonstrate that the proposed approach is capable of tracking the changes of structural parameters leading to the identification of structural damages.

Online Algorithms for the Construction of Guaranteed Confidence Sets for the Parameters of Time-varying Systems

The “Leave-out Sign-dominant Correlation Regions” (LSCR) algorithm is extended to deliver a guaranteed confidence set for the parameters of a time-varying system at any given time. The algorithm is derived by assuming that an upper bound on the parameter variation is available, and the delivered confidence set is valid without any prior knowledge of the noise. Simulation examples are provided to illustrate the performance of the algorithm.

Design of a Data-Driven PID Controller

Since most processes have nonlinearities, controller design schemes to deal with such systems are required. On the other hand, proportional-integral-derivative (PID) controllers have been widely used for process systems. Therefore, in this paper, a new design scheme of PID controllers based on a data-driven (DD) technique is proposed for nonlinear systems. According to the DD technique, a suitable set of PID parameters is automatically generated based on input/output data pairs of the controlled object stored in the database. This scheme can adjust the PID parameters in an online manner even if the system has nonlinear properties and/or time-variant system parameters. Finally, the effectiveness of the newly proposed control scheme is evaluated on some simulation examples, and a pilot-scale temperature control system.

Fault Diagnosis of Time-Varying Parameter Systems With Application in MEMS LCRs

Multiple-model adaptive estimation (MMAE) is a well-known technique used for model matching of deterministic parameter systems. This technique can be used in fault diagnosis by allocating a model to each type of fault. In each contingency, the model that represents the behavior of the actual system can indicate the type of fault occurrence. Kalman filters are generally used in modeling and residual-signal generation of time-invariant systems. Slowly time-varying parameter systems, however, require a system identification unit in addition to the model-matching core. This paper utilizes the least square forgetting-factor technique in parameter identification of slowly time-varying systems and combines it with MMAE for fault-diagnosis applications in microelectromechanical-systems (MEMS) lateral comb resonators (LCRs). Prescheduled faults were designed for simulations and experimentally examined in real-time implementations of estimation-based diagnosis technique for two fabricated MEMS LCRs. It is shown that the application of a system identification unit significantly increases the performance of the fault diagnosis in MEMS devices.

603 時間領域部分空間法による時変系のモード特性同定

In is very important to understand the dynamic characteristics to control vibrations of machinery. Generally, the conventional identification methods for experimental modal analysis are employed to time-invariant systems. However, there are a lot of requirements to extract modal parameters of time-varying systems. It is hard to implement the vibration testing for time varying systems, because of excitation difficulties, boundary conditions, etc. In this paper, the subspace algorithm are proposed to obtain time-varying modal parameters. Then, the pseudo-modal parameters are only extracted from output responses. The effectiveness of the proposed time-varying identification algorithm is verified by numerical results. Finally, the procedure is applied to an experimental manipulator model.

BAYESIAN APPROACH FOR ROBUST PARAMETER TRACKING

In this paper we study the problem of tracking of time-varying parameters of a dynamical system. The problems also facing at the finite number of expected parameter changes and finite number of possible measurement model. We consider a stochastic model of parameter development with some form of obsolete information forgetting. It will be shown that it is possible to track rapidly time-varying plant parameters using extension of Bayesian viewpoint (continuous and discrete parameters) with requiring the prior information that can improve tracking for abrupt changes.

Search and selection in the money market fund industry

This paper develops a dynamic, stochastic growth system for money market fund families and tests how search behavior within this system affects fund family exit. The outcome of search behavior is measured as the time-varying parameters of the growth system, estimated by the Kalman filter. The results provide no evidence that the continuously updated coefficients influence the risk of failure. However, the cumulative amount of search generally affects exit positively, consistent with Hannan and Freeman's (1984) structural inertia theory. Founding conditions and money market fund performance are also important predictors of money market fund failure. These findings are discussed in the light of Bowman's (1963) theory of managerial coefficients and its applicability to simple industries like money market funds. The implications for future empirical studies on evolutionary growth systems are also discussed.

DESIGN AND EXPERIMENTAL EVALUATION OF A DATA-DRIVEN PID CONTROLLER

Since most processes have nonlinearities, controller design schemes to deal with such systems are required. On the other hand, PID controllers have been widely used for process systems. Therefore, in this paper, a new design scheme of PID controllers based on a data-driven(DD) technique is proposed for nonlinear systems. According to the DD technique, a suitable set of PID parameters is automatically generated based on input/output data pairs of the controlled object stored in the data-base. This scheme can adjust the PID parameters in an on-line manner even if the system has nonlinear properties and/or time-variant system parameters. Finally, the effectiveness of the newly proposed control scheme is evaluated on a pilot-scale temperature control system.

PROJECTIVE MODELING AND SYSTEM CHANGE: RESERVOIR MANAGEMENT EXAMPLES

In this paper a projective modeling approach for ecological/environmental systems is introduced. The basic idea behind projective modeling is to define (possible) future output behavior and to use identifiable time-varying system parameters, representing underlying sub-processes, as an (additional) agent for control in order to investigate potential off-normal system changes. Recursive estimation techniques are used to calculate the parameter trajectories, which subsequently, for full understanding of the problem, are subject to physical interpretation. This idea of projective modeling is motivated by a real-world eutrophication example and further explained in some more detail on a class-room example, describing the outflow from a reservoir.

Adaptive optimization of least-squares tracking algorithms: with applications to adaptive antenna arrays for randomly time-varying mobile communications systems

Adaptive antenna arrays are used for reducing the effects of interference and increasing capacity in mobile communications systems. Typical algorithms recursively compute the antenna weights that minimize the weighted error function (at discrete times kh, k=1,2,..., for a sampling interval h) /spl sigma//sub l=1//sup k//spl alpha//sup k-l/[e/sub l/(W)]/sup 2/, where e/sub l/(W) is a measure of the reception error at time lh with antenna weight vector W, and /spl alpha/<1. The forgetting factor /spl alpha/<1 allows tracking as conditions change and the minimization is used only to get the weights. The average detection error rate depends heavily on the chosen value of /spl alpha/, whose optimal value can change rapidly in time, perhaps significantly in seconds. We add another adaptive loop that tracks the optimal value of /spl alpha/ and greatly improves the operation when the environment is randomly time-varying. The additional adaptive loop is based on an approximation to a natural "gradient descent" method. The algorithm is practical and can improve the performance considerably. In terms of average detection error rates and for all of the scenarios tested, the new system tracks the optimal value of /spl alpha/ well, and always performs better (sometimes much better) than the original algorithm that uses any fixed value of /spl alpha/. Although the initial motivation arises in adaptive antennas, the method can be used to improve algorithms for tracking parameters of time-varying nonlinear systems, where similar issues are involved.

Sequential non-linear least-square estimation for damage identification of structures

An early detection of structural damage is an important goal of any structural health monitoring system. In particular, the ability to detect damages on-line, based on vibration data measured from sensors, will ensure the reliability and safety of the structures. In this connection, innovative data analysis techniques for the on-line damage detection of structures have received considerable attentions recently, although the problem is quite challenging. In this paper, we proposed a new data analysis method, referred to as the sequential non-linear least-square (SNLSE) approach, for the on-line identification of structural parameters. This new approach has significant advantages over the extended Kalman filter (EKF) approach in terms of the stability and convergence of the solution as well as the computational efforts involved. Further, an adaptive tracking technique recently proposed has been implemented in the proposed SNLSE to identify the time-varying system parameters of the structure. The accuracy and effectiveness of the proposed approach have been demonstrated using the Phase I ASCE structural health monitoring benchmark building, a non-linear elastic structure and non-linear hysteretic structures. Simulation results indicate that the proposed approach is capable of tracking on-line the changes of structural parameters leading to the identification of structural damages.