Unknown Load Disturbances Research Articles

Backstepping integral control for hydraulic servo system based on LuGre friction model

A hydraulic shaking table has obvious frictional characteristics that will result in the deterioration of test waveforms and affect test accuracy. In this study, the influence of nonlinear friction for a hydraulic shaking table test system is analyzed briefly. For dynamic friction parameters and unknown system load characteristics, a mathematical model is developed based on the LuGre friction model, and double nonlinear observers are constructed to estimate average deflection. A backstepping integral control method is proposed to compensate the nonlinear friction and estimate the unknown load disturbances. The stability of the closed-loop system is proved by the Lyapunov function. In the test, an iterative control algorithm is utilized to obtain the system inverse transfer function. The experimental results show that the proposed method can reduce the nonlinear friction of the hydraulic servo system, and improve the control accuracy of the hydraulic shaking table. In addition, high identification accuracy for system impedance is also achieved. Compared to no friction compensating method, the acceleration waveform distortion is reduced from 19 % to 5 % and the peak error is reduced from 14 % to 4.2 % by the proposed method, and the deviation of power spectral density is also reduced effectively.

Robust Stability of Networked Load Frequency Control Systems with Time-Varying Delays

In deregulated power system scenario coupled with smart grid technology, for networked load frequency control (LFC), an open communication structure, owing to low cost and flexibility, is preferred over dedicated networks in the feedback control loop for transmitting/receiving the data between the geographically displaced power system and the control center. In such a control scheme, closing the feedback loop through an open communication channel, in turn, introduces two additive time-varying delays of dissimilar characteristics in the feedback path. These delays degrade the performance of the closed-loop system, and exert a destabilizing effect on the overall system. In this paper, using Lyapunov-Krasovskii functional approach, a less conservative stability criterion is presented to ascertain delay-dependent stability of such network-controlled LFC systems with two additive time-varying delays in the feedback path in the presence of uncertain load disturbance conditions. Unlike the existing results, which are derived by combining these two delays into one, the proposed result considers the two delays as separate entities thereby imparting more generalization into the stability analysis. The effect of unknown exogenous load disturbance is incorporated by mathematically modeling them as a bounded non-linear time-varying function of current and delayed state vectors.

Identification of discrete-time output error model for industrial processes with time delay subject to load disturbance

In this paper, a bias-eliminated output error model identification method is proposed for industrial processes with time delay subject to unknown load disturbance with deterministic dynamics. By viewing the output response arising from such load disturbance as a dynamic parameter for estimation, a recursive least-squares identification algorithm is developed in the discrete-time domain to estimate the linear model parameters together with the load disturbance response, while the integer delay parameter is derived by using a one-dimensional searching approach to minimize the output fitting error. An auxiliary model is constructed to realize consistent estimation of the model parameters against stochastic noise. Moreover, dual adaptive forgetting factors are introduced with tuning guidelines to improve the convergence rates of estimating the model parameters and the load disturbance response, respectively. The convergence of model parameter estimation is analyzed with a rigorous proof. Illustrative examples for open- and closed-loop identification are shown to demonstrate the effectiveness and merit of the proposed identification method.

Backstepping Control of Electro-Hydraulic System Based on Extended-State-Observer With Plant Dynamics Largely Unknown

In this paper, an output position feedback control of the electro-hydraulic system (EHS) is proposed based on an extended-state-observer (ESO) with backstepping. On the basis of the augmented state model of the EHS, the ESO is designed to handle the unknown load disturbance and uncertain nonlinearity. Then, an observer bandwidth constraint is derived to compromise between the dynamic performance and the maximal load capability of EHS. The effectiveness of the proposed control for the suppression of largely unknown disturbances has been demonstrated by comparative experimental study, which indicates the proposed approach can achieve better dynamic performance on the motion control of two-degree-of-freedom robotic arm.

Enhanced Control Design of Simple DC-DC Boost Converter-driven DC Motors: Analysis and Implementation

The DC-DC boost converter is one of the simplest power electronic devices that has not been yet exploited in a wide range of industrial applications due to control design difficulties caused by its model inherent special structure. Such an industrial application is the DC motor speed regulation that is studied in the present work. Particularly, in this article, a novel, non-linear control scheme for the duty ratio input of the converter is proposed, which is extensively analyzed and experimentally tested. The proposed design, though non-linear, results in a very simple scheme, ensures that the duty ratio takes values exclusively in the permitted range [0,1), achieves precise speed regulation even in cases of high unknown load disturbances, and does not depend on system parameters and states. Simultaneously, the design is formulated in a manner that provides a closed-loop passive system, which, as proven in the article, satisfies all these assumptions and properties that make possible the application of a new advanced non-linear method that strongly connects passivity with stability. Thus, the boundedness of all the closed-loop states and the stability and convergence to the desired steady-state equilibrium are directly concluded. The theoretical analysis is verified through extended simulation and experimental results.

Stability Criteria for Nonlinearly Perturbed Load Frequency Systems With Time-Delay

In this paper, new criteria are presented for ascertaining delay-dependent stability of networked multi-area load frequency control (LFC) systems with feedback loop delay in presence of unknown and time-varying exogenous load disturbance. The proposed stability criteria are based on Lyapunov-Krasovskii (LK) functional approach, and they are expressed in linear matrix inequality (LMI) framework. Since the unknown exogenous load disturbance in the power system (cause) ultimately affects the evolution of the state variables of the system (effect), in the reported stability criteria, the load disturbance is mathematically modelled as a norm-bounded nonlinear time-varying function of current and delayed state vectors, and subsequently incorporated into the delay-dependent stability analysis. Two cases of time-delays are considered in the stability analysis: time-varying single feedback-loop delay element and time-invariant multiple feedback-loop delay elements. In addition, to realize less conservative stability criteria with minimal number of decision variables, in the stability analysis, tighter integral inequalities, viz., reciprocal convex combination lemma (for time-varying delay case) and Jenson integral inequality (for time-invariant delay case) are employed. The proposed results are illustrated on standard benchmark LFC systems, and supported suitably by simulation results.

Improved Results on Delay-Dependent Stability of LFC Systems with Multiple Time-Delays

In this paper, the problem of delay-dependent robust stability of uncertain load frequency control (LFC) systems with multiple time-delays and exogenous power system disturbance has been considered. Using Lyapunov–Krasosvskii functional method, less conservative delay-dependent stability criteria are proposed in linear matrix inequality formulation to compute the maximum value of the time-delays within which the LFC system under consideration remains asymptotically stable in the sense of Lyapunov. Compared to the existing result in the literature, the proposed result takes into account the effect of unknown exogenous load disturbance into the stability analysis, imparting more applicability and usefulness to the resulting stability criterion in real-time conditions.

A new nonlinear controller for dc-dc boost converter fed dc motor

This paper presents a novel, nonlinear control scheme for the duty-ratio input of the boost converter fed dc motor. The control technique is proposed, extensively analysed and experimentally tested. The proposed design though nonlinear, results in a very simple scheme, ensures that the duty-ratio takes values exclusively in the permitted range, achieves precise speed regulation even in cases of high unknown load disturbances, and does not depend on system parameters and states. Concurrently, the design is formulated in a manner that provides a closed-loop passive system which, as proven in the paper, satisfies all these assumptions and properties that make possible the application of a new advanced nonlinear method which strongly connects passivity with stability. Thus, it is concluded that the boundedness of all closed-loop states and system stability are converge to the desired steady-state equilibrium. Theoretical analysis of the proposed technique is verified and confirmed through simulation and experimental results.

Improved adaptive neural network control for humanoid robot hand in workspace

In order to improve the control performance of humanoid robot hand in workspace, an adaptive control method based on improved neural network was proposed. rival-penalized competitive learning and recursive orthogonal least-squares algorithms were applied to reinforce the learning capability of Gaussian radial basis function neural network and realize the real-time of neural network. Moreover, an improved neural network model for humanoid robot hand was established with Ge-Lee matrix and its operator, and a controller was designed. Finally, an example of humanoid robot hand finger was provided. The results showed that the proposed control method could effectively control the unknown nonlinear dynamic properties and load disturbances of the finger with a much smaller tracking errors.

Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System

This paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanent-magnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed controller combined the merits of the backstepping sliding mode control with robust characteristics and the WNN owning the capability of artificial neural networks for online learning and the capability of wavelet decomposition for identification. An observed error compensator is developed to compensate the estimated error of the WNN and the adaptive law is derived according to Lyapunov theorem. The effectiveness of the proposed controller is investigated in simulation under different operating conditions. The simulation results demonstrate the proposed WNNBSSM controller can provide precise tracking performance and robust characteristics despite unknown parameter uncertainties and load disturbance. Moreover, an implemental wavemaker system is established to verify the effectiveness of the proposed control algorithm.

Robust nonlinear HVAC systems control with evolutionary optimisation

Purpose – The purpose of this research is to design a robust high-performance nonlinear multi-input multi-output heating, ventilation and air conditioning (HVAC) system controller for temperature and relative humidity regulation. Buildings are complex systems which are subjected to many unknown disturbances. Further complicating the control problem is the fact that, in practice, buildings and their systems have static nonlinearities such as power saturation that make stability difficult to guarantee. Therefore, in order to overcome these issues, a control system must be designed to be robust (performance insensitive) against uncertainties, static nonlinearities and effectively respond to unknown heat load and moisture disturbances. Design/methodology/approach – A state of the art nonlinear inverse dynamics (NID) technique is combined with a genetic algorithm (GA) optimisation scheme in order to improve robustness against uncertainty in the system's modelling assumptions. The parameter uncertainty problem is addressed by optimising the control system parameters over a specified range of uncertainty. The NID control structure provides further robustness with effective disturbance handling and a stability criteria that holds in the presence of actuator saturation. Findings – The proposed method delivers significantly more energy efficient performance whilst achieving improved thermal comfort when compared with a current industry standard HVAC controller design such as proportional-integral-derivative. The expected excellent response to disturbances is also demonstrated. Research limitations/implications – This method can easily be extended to account for other parameters with a specified uncertainty range. Practical implications – This research presents a method of optimised NID controller design which can be easily implemented in real HVAC controllers of building energy management systems with a high degree of confidence to provide high levels of thermal comfort whilst significantly reducing energy usage. Originality/value – A novel HVAC optimised NID control strategy using the robust inverse dynamics estimation feedback control topology with GA optimisation for improved robustness and tuning over a range of parameter uncertainty is described, designed and its performance benefits shown through simulation studies.

Effective and Robust Generalized Predictive Speed Control of Induction Motor

This paper presents and validates a new proposal for effective speed vector control of induction motors based on linear Generalized Predictive Control (GPC) law. The presented GPC-PI cascade configuration simplifies the design with regard to GPC-GPC cascade configuration, maintaining the advantages of the predictive control algorithm. The robust stability of the closed loop system is demonstrated by the poles placement method for several typical cases of uncertainties in induction motors. The controller has been tested using several simulations and experiments and has been compared with Proportional Integral Derivative (PID) and Sliding Mode (SM) control schemes, obtaining outstanding results in speed tracking even in the presence of parameter uncertainties, unknown load disturbance, and measurement noise in the loop signals, suggesting its use in industrial applications.

A robust disturbance reduction scheme for linear small delay systems with disturbances of unknown frequencies

A robust disturbance reduction scheme for linear small delay systems with disturbances of unknown frequencies is presented in this paper. Unlike other methods, the proposed scheme does not require disturbance frequencies to be known. The linear systems modeled in this study are nominally stable and minimum phase systems with relative degree. The control structure is an integration of Astrom’s modified Smith predictor and the proposed scheme. The proposed scheme consists of an input disturbance reduction controller (IDRC) and a residual disturbance reduction controller (RDRC). The IDRC using an artificial neural network (ANN) is proposed to reduce unknown load disturbances and modeling uncertainties in stable systems and unstable systems. The ANN can appropriately approximate the product of an inverse time delay and a nonnegative gain in the IDRC. The residual signals including residual disturbances and residual uncertainties are suppressed by the RDRC based on a disturbance observer. Simulation examples are illustrated to show the effectiveness of the proposed robust disturbance reduction scheme for linear delay uncertain systems with periodic or non-periodic unknown load disturbances.

Modified Smith predictor with a robust disturbance reduction scheme for linear systems with small time delays

AbstractThis paper presents a robust disturbance reduction scheme using an artificial neural network (ANN) for linear systems with small time delays. It is assumed that the nominal linear systems are stable, minimum phase and relative degree one systems. The proposed structure is an integration of a modified Smith predictor and an ANN‐based disturbance reduction scheme. Unlike other disturbance rejection methods, the proposed approach does not require information about unknown load disturbance frequencies. An ANN is used to approximate the unknown load disturbances and to enhance the robustness of the proposed disturbance reduction scheme against modelling errors in the estimated time delay and the process model. Connective weights of the ANN are trained on‐line using a back‐propagation algorithm until uncertainties resulting from unknown load disturbances and modelling errors are minimized. The simulation results show the effectiveness of the presented disturbance reduction scheme for controlling linear delay systems subjected to step or periodic unknown load disturbances.

A disturbance reduction scheme for linear small delay systems with modeling uncertainties

A disturbance reduction scheme for linear delay systems with modeling uncertainties is presented in this paper. The linear systems in this study are assumed to be nominally stable, minimum phase and relative degree one systems. The control structure is based on Astrom's modified Smith predictor with a disturbance reduction scheme and an artificial neural network (ANN). Unlike other disturbance rejection methods, the proposed scheme does not require information about unknown disturbance frequencies. The ANN is used to approximate a product of an inverse of a time delay and a nonnegative gain and to augment the robustness of the proposed approach against modeling uncertainties including a time-varying delay. Connective weights of the ANN are trained on-line using a back-propagation algorithm according to a disturbance estimation error function. Simulation results show the effectiveness of the presented disturbance reduction scheme for linear delay systems with modeling uncertainties, subjected to both periodic and non-periodic unknown load disturbances.

Adaptive fuzzy mimo control of induction motors

This paper presents a new adaptive fuzzy control technique applied to induction motors (IM). The control task of such motors is considered complicated by the fact that these motors have uncertain time-varying parameters and are subjected to unknown load disturbance. A nonlinear multi-input multi-output (MIMO) state feedback linearizing control is designed for the IM modeled in a stationary reference frame. An adaptive fuzzy controller is proposed to estimate the unknown nonlinear functions that appear in the state feedback input–output linearizing control. Simulation results are presented to validate the effectiveness of the proposed controller even in the presence of motor parameter variations and unknown load disturbance.

NONLINEAR APPROACH FOR THE VFA REGULATION IN AN ANAEROBIC DIGESTER

This paper presents the design and the experimental validation of a nonlinear control approach for the regulation of volatile fatty acids (VFA) in an anaerobic digester. Such an approach is conformed by an output feedback control and an extended Luenberger observer which allows the estimation of the uncertain terms associated to the VFA dynamics ( i.e. , influent composition and kinetic terms). The nonlinear approach is experimentally validated in a 0.528m 3 up-flow fixed-bed anaerobic digester used for the treatment of industrial wine distillery wastewater. The experimental results show that the VFA regulation is achieved in spite of uncertain kinetics, restrictions in the control input and unknown load disturbances.

Control of Thermally Coupled Distillation Arrangements with Dynamic Estimation of Load Disturbances

Because of their potential energy savings, thermally coupled distillation sequences provide interesting alternatives to the use of sequences based on conventional distillation columns. Three thermally coupled structures have been particularly analyzed for the separation of ternary mixtures: the sequence with a side rectifier, the sequence with a side stripper, and the Petlyuk column. Design methods have been developed for such sequences, but their dynamic and operational characteristics still require a wider understanding to promote their practical implementation. Previous works have shown that thermally coupled systems can provide suitable control properties; most of the studies on closed-loop control analysis of thermally coupled systems have been based on proportional−integral (PI) controllers. In this work, a PI controller with dynamic estimation of uncertainties is implemented for the control of the thermally coupled distillation arrangements. The controller comprises three feedback terms: proportional, integral, and quadratic actions. The last term provides a dynamic estimation of unknown load disturbances to improve the closed-loop performance. Comparison with the classical PI control law was carried out to analyze the performance of the proposed controller in facing unknown load disturbances in feed composition and set point changes. The results show that the implementation of the controller with dynamic estimation of uncertainties improved noticeably the closed-loop responses provided by the PI controller.

Design of a Non-Overshooting PID Controller with an Integral Sliding Perturbation Observer for Motor Positioning Systems

This paper describes the design of a non-overshooting proportional-integral-derivative (PID) controller with both pre-assigned initial integral state and a perturbation compensation scheme for motor positioning systems. According to the requirement of non-overshooting output response and the specified settling time in tracking a step reference signal, the PID gains and the initial integral state are determined systematically. The proposed PID control provides a continuous control signal when the nominal system is subject to sudden set-point changes, implying smooth system performance. To compensate for the system perturbation resulting from parameter variations and unknown load disturbances, an integral sliding perturbation observer is proposed and integrated into the PID-controlled system, which requires neither access to the time derivative of the state vector nor accurate knowledge of system parameters. A sliding mode is ensured throughout the entire response, and system perturbation is estimated by feeding a switching signal through an integrator. Due to the low-pass filtering characteristic of the integral action and the reduction of the required switching gain, the proposed scheme not only precisely compensates for system perturbation within the interested frequency range, but also effectively alleviates the chattering problem in control signals. The usefulness of the proposed design is demonstrated through an experimental study of a motor drive under load variations and disturbances.

Estimation of Power System Parameters

This paper describes the use of a system identification technique to estimate the parameters of a low-order, dynamic model for a power system. The basic technique is to obtain an ARIMAX model by applying a prediction error method to data consisting of 1-s samples of the system frequency and the power output of the Dinorwig fast-response pumped-storage station. From this model the natural frequency, damping factor and stiffness (or beta) of the power system are obtained. The paper first establishes an appropriate order for the ARIMAX model. Simulation is used to validate the model and ensure that the results are not an artefact of either the input, the identification method or the model structure. It is shown that the precision of the parameter estimates is limited by unknown load disturbances. An example of the use of the technique to analyze the daily variation of parameters is given.