Dynamic Variable Structure Research Articles

Research on High-Precision Position Control of Valve-Controlled Cylinders Based on Variable Structure Control

During the operation of valve-controlled cylinder position systems, there are some problems, such as uncertainty and time-varying of the system parameters, imprecise modeling, and external interference. These problems will affect the position loading accuracy and safety reliability of the electro-hydraulic position servo system. This paper used the position servo system of a valve-controlled cylinder as the research object, and a mathematical model was established. Then, a sliding mode variable structure controller and dynamic soft variable structure controller were designed, and the Grey Wolf algorithm was used to adjust the controller parameters. The control effect of the two controllers in the valve-controlled cylinder system was verified with a simulation and experiment. The results showed that compared with the sliding mode variable structure controller, the dynamic soft variable structure controller had higher control precision and better stability and avoided the chattering phenomenon.

Bayesian network for dynamic variable structure learning and transfer modeling of probabilistic soft sensor

Bayesian network is a frequently-used uncertainty reasoning method, which systematically describes relations between random variables. Dynamic Bayesian network is an extension of Bayesian network, which contains the relations between variables at different times. Soft sensor is an important industrial application, in which feature variables are selected to predict the value of the target variables. For industrial soft sensor applications, dynamics is still a tough problem, particularly together with the uncertain feature of process data. In this article, DBN is employed for dynamic variable structure learning and transfer modeling to some strong regression models to build a soft sensor for dynamic industrial processes. At the beginning, a series of dynamic Bayesian networks are constructed on the training set with a sliding window. From these network structures we can find variables related to the quality variables. Then, in each time period, a sequence of data is compared with training data to select the most similar sequence by Dynamic Time Warping. Therefore, the structure of variables is built, i.e., the related feature variables of the quality variables can be determined by the network structure. In the regression step, the dynamic variable structure is transferred to some strong regressors, like Support Vector Regression and Adaboost for further regression. In case study, we use the debutanizer process and a low temperature transformer case to confirm the quality of the soft sensor method. The result reveals that, the prediction accuracy of the new method is much higher than the original commonly-used regression methods.

Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network

Soft sensor has been extensively utilized in industrial processes for prediction of key quality variables. To build an accurate virtual sensor model, it is very significant to model the dynamic and nonlinear behaviors of process sequential data properly. Recently, a long short-term memory (LSTM) network has shown great modeling ability on various time series, in which basic LSTM units can handle data nonlinearities and dynamics with a dynamic latent variable structure. However, the hidden variables in the basic LSTM unit mainly focus on describing the dynamics of input variables, which lack representation for the quality data. In this paper, a supervised LSTM (SLSTM) network is proposed to learn quality-relevant hidden dynamics for soft sensor application, which is composed of basic SLSTM unit at each sampling instant. In the basic SLSTM unit, the quality and input variables are simultaneously utilized to learn the dynamic hidden states, which are more relevant and useful for quality prediction. The effectiveness of the proposed SLSTM network is demonstrated on a penicillin fermentation process and an industrial debutanizer column.

Dynamical adaptive integral backstepping variable structure controller design for uncertain systems and experimental application

SummaryIn this study, a dynamical adaptive integral backstepping variable structure control (DAIBVSC) system based on the Lyapunov stability theorem is proposed for the trajectory tracking control of a nonlinear uncertain mechatronic system with disturbances. In this control scheme, no prior knowledge is required on the uncertain parameters and disturbances because it is estimated by two types of dynamical adaptive laws. These adaptive laws are integrated into the dynamical adaptive integral backstepping control and variable structure control (VSC) parts of the DAIBVSC. The dynamical adaptive law in the dynamical adaptive integral backstepping control part updates parametric uncertainties, while the other in the VSC part adapts upper bounds of non‐parametric uncertainties and disturbances. In order to achieve a more robust output tracking and better parameter adaptation, the control system is extended by one integrator and sliding surface is augmented by an integral action. Experimental evaluation of the DAIBVSC is conducted with respect to performance and robustness to parametric uncertainties. Experimental results of the DAIBVSC are compared with those of a traditional VSC. The proposed DAIBVSC exhibits satisfactory output tracking performance, good estimation of the uncertain parameters and can reject disturbances with a chattering free control law. Copyright © 2017 John Wiley & Sons, Ltd.

Predictive Control of Suspension Systems Through Combining Dynamic Matrix and Constrained Variable Structure Controllers

In this paper, a controller called dynamic matrix constrained variable structure controller (DM-CVSC) is proposed. The controller takes advantages of both dynamic matrix (DM) and constrained variable structure controllers. As a result, DM-CVSC is a robust trajectory tracking controller dealing with the constraints on control inputs and also makes decision based on the future behavior of the vehicle. The controller is applied to a linearized model of half-car suspension systems which are subject to different types of road disturbances and measurement noises. In this paper, it is shown that there is a simple formulation for calculating the range of sliding gains for single-input single-output (SISO) linear control systems. As for the multi-input multi-output (MIMO) linear control systems, the calculation of upper sliding gain profile for controller leads to a search problem. To show the efficiency of the proposed controller, it is applied to four different cases involving specific road disturbances and measurement noises. The performance of the proposed controller is compared to various control techniques.

Robust output regulation for a class of MIMO nonlinear systems with uncertain exosystems

The robust output regulation problem is considered for a class of multi-input multi-output (MIMO) nonlinear systems with uncertain exosystems. Under a mild assumption that the uncertainties of the controlled plant and exosystem have known boundary functions, a new design method of dynamic variable structure controller is proposed. It is proven that the tracking error of the closed-loop system can reach a specified manifold in a finite time and converge to zero asymptotically while all states involved are bounded. Simulation results of a permanent magnet synchronous motor servo system are given to validate the proposed approach.

Variable Structure Disturbance Rejection Control for Nonlinear Uncertain Systems with State and Control Delays via Optimal Sliding Mode Surface Approach

The paper considers the problem of variable structure control for nonlinear systems with uncertainty and time delays under persistent disturbance by using the optimal sliding mode surface approach. Through functional transformation, the original time-delay system is transformed into a delay-free one. The approximating sequence method is applied to solve the nonlinear optimal sliding mode surface problem which is reduced to a linear two-point boundary value problem of approximating sequences. The optimal sliding mode surface is obtained from the convergent solutions by solving a Riccati equation, a Sylvester equation, and the state and adjoint vector differential equations of approximating sequences. Then, the variable structure disturbance rejection control is presented by adopting an exponential trending law, where the state and control memory terms are designed to compensate the state and control delays, a feedforward control term is designed to reject the disturbance, and an adjoint compensator is designed to compensate the effects generated by the nonlinearity and the uncertainty. Furthermore, an observer is constructed to make the feedforward term physically realizable, and thus the dynamical observer-based dynamical variable structure disturbance rejection control law is produced. Finally, simulations are demonstrated to verify the effectiveness of the presented controller and the simplicity of the proposed approach.

Dynamic soft variable structure control of singular systems

The dynamic soft variable structure control (VSC) of singular systems is discussed in this paper. The definition of soft VSC and the design of its controller modes are given. The stability of singular systems with the dynamic soft VSC is proposed. The dynamic soft variable structure controller is designed, and the concrete algorithm on the dynamic soft VSC is given. The dynamic soft VSC of singular systems which was developed for the purpose of intentionally precluding chattering, achieving high regulation rates and shortening settling times enhanced the dynamic quality of the systems. It is illustrated the feasibility and validity of the proposed strategy by a simulation example, and an outlook on its auspicious further development is presented.

On the synchronization of uncertain master–slave chaotic systems with disturbance

This paper focuses on the synchronization of a class of master–slave chaotic systems with uncertainty and disturbance. A sliding surface is adopted newly to ensure the stability of the error dynamics in sliding mode and a dynamic variable structure controller (DVSC) is derived to realize chaos synchronization better. The typical numerical example is given to demonstrate the effectiveness of the result obtained.

Adaptive Variable Structure and Commanding Shaped Vibration Control of Flexible Spacecraft

In this paper, a new approach for vibration control of flexible spacecraft during attitude maneuvering is proposed. This control strategy integrates the command input shaping and the technique of dynamic variable structure output feedback control. More precisely, the input shaper is implemented outside of the feedback loop, which is designed for the reference model to achieve the exact elimination of residual vibration by modifying the existing command; whereas for the feedback loop, the controller is designed to make the closed-loop system follow the reference model with input shaper and eliminate the residual vibrations in the presence of parametric uncertainty and external disturbances. An attractive feature of this proposed dynamic variable structure output feedback control algorithm is that the parametric uncertainties of the system are not necessarily to satisfy the so-called matching condition or invariance conditions, provided that certain bounds are known. Furthermore, an adaptive version of the proposed controller is achieved through releasing the limitation of knowing the bounds of the uncertainties and perturbations in advance. The adaptive control law results in substantially simpler stability analysis and improves overall response. Compared with conventional methods, the developed control scheme guarantees not only the stability of the closedloop system, but also yields better performance and robustness in the presence of parametric uncertainties and external disturbance. Simulation results are presented for the spacecraft model to show the effectiveness of the proposed control techniques.

On a Dynamic Variable Structure Control Approach for the Active Control of Mechanical Vibrations

In this paper we will consider asymptotic stabilization using both static and dynamic variable structure controllers for the active control of mechanical vibrations. The proposed approach is mainly based on geometric input-output linearization, and is applied to a mechanical system with one degree of freedom, which uses an electro-hydraulic actuator. Computer simulations are used to show that the dynamic variable structure control system proposed exhibits robust behavior in the face of external disturbances and parameter variations.

Dynamical Variable Structure Controller for Power Regulation of Wind Energy Conversion Systems

The paper addresses the problem of output power regulation of fixed-pitch variable-speed wind energy conversion systems. Operation is constrained by practical reasons to the low-speed side of the turbine power-speed curve. Unfortunately, this region is characterized by a nonminimum phase dynamics which is an obstacle to perform the regulation task. A dynamical variable structure controller is developed that accomplishes the control objective despite this limitation. The proposed control strategy presents attractive features such as robustness to parametric uncertainties of the turbine and generator as well as to electric grid disturbances.

Dynamic output feedback sliding mode design for a class of linear unmatched uncertain systems

This paper investigates the output feedback stabilization problem for a class of multi-input/multi-output (MIMO) systems with matched/unmatched uncertainties by the variable structure system (VSS) with a new sliding mode control. In dynamic systems, a new idea of a sliding sector that consists of two sets of switching surfaces is proposed. A new invariance condition is derived such that the reduced-order system in the new sliding mode is completely invariant to both matched and unmatched uncertainties. Additionally, to demonstrate the effectiveness of the proposed scheme and to reduce the drawbacks of conventional VSS design, a modified dynamic variable structure controller using only the output variables is applied to perform the sliding mode. Furthermore, a stability analysis of the overall system is also provided. Finally, an example is given to illustrate the effectiveness of the control scheme.

Lyapunov stability theory of nonsmooth systems

This paper develops nonsmooth Lyapunov stability theory and LaSalle's invariance principle for a class of nonsmooth Lipschitz continuous Lyapunov functions and absolutely continuous state trajectories. Computable tests based on Filipov's differential inclusion and Clarke's generalized gradient are derived. The primary use of these results is in analyzing the stability of equilibria of differential equations with discontinuous right-hand side such as in nonsmooth dynamic systems or variable structure control. >

A dynamical variable structure control strategy in asymptotic output tracking problems

A dynamical discontinuous feedback strategy of the sliding mode type is presented for asymptotic output tracking problems in nonlinear dynamical systems. N. Fliess' (1989, 1990) generalized observability canonical form (GOCF) is used in the derivation of the dynamical variable structure feedback controller. For a large class of nonlinear systems, a truly effective smoothing of the sliding mode controlled responses is possible, while substantially reducing the chattering for the control output. For this reason, the technique is especially suitable for the control of some mechanical and electromechanical systems. An example, including simulations, is provided. >

Adaptive dynamical variable structure feedback regulation for nonlinear uncertain systems

AbstractIn this article an adaptive discontinuous dynamical feedback strategy is presented for asymptotic output stabilization problems defined on nonlinear controlled systems exhibiting linear parametric uncertainty. A dynamical feedback controller, ideally achieving output stabilization via exact linearization, is obtained by means of output differentiation and sliding mode control ideas. The adaptive version of the dynamical variable structure controller is then obtainable via standard, direct, overparametrized adaptive control techniques available for linearizable systems through static state feedback. An illustrative example from the chemical process control area, including simulations, is provided.

Dynamical variable structure systems approach to switched capacitor circuit models

A new model, of a dynamic nature, is proposed for the basic analysis of switched capacitor circuits. The model represents a significant conceptual departure from the traditional frequency-dependent equivalent resistor model. Using Filippov's concept of solution for systems governed by right-hand side discontinuous differential equations, such as those regulated by a pulse-width modulation control strategy, a general and more realistic dynamical model is proposed for switched capacitor circuits. The traditional model is easily rederived and reinterpreted as the outcome of a sequential singular perturbation procedure carried on an idealized version of the proposed dynamical model.