Input Disturbances Research Articles

Homogeneity based finite/fixed‐time observers for linear MIMO systems

AbstractThe article is devoted to the problem of finite‐time and fixed‐time observation of linear multiple input multiple output control systems. The proposed dynamic observers do not require system transformation to a canonical form and guarantee convergence of the observation error to zero in a finite or in a fixed time. It is shown that the observers are robust (in input‐to‐state sense) against input disturbances and measurement noises. The results are supported with simulation examples.

Specified QoS based networked observer and PI controller design with disturbance and noise rejection under random packet dropout

In this paper, a novel observer based networked PI controller has been designed by considering bounded disturbance and measurement noise under specified probability of random packet dropouts in networked control systems (NCSs). Lyapunov stability condition of the NCS with an observer and PI controller has been derived using an asynchronous dynamical system (ADS) approach as a linear matrix inequality (LMI) with nonconvex constraints to improve its quality-of-service (QoS) and quality-of-control (QoC). In order to improve QoC and QoS for NCS under process and measurement disturbance, we have used H∞ norm bound, and random packet drop rate in the NCS design. The algorithm has been tested and validated on four test-bench models of energy conversion systems. Confidence intervals and the mean responses of the state variables, state estimation errors of the observer and control signals have been reported using 100 Monte Carlo simulations. Our proposed NCS with PI controller and observer design method can be employed on a wide range of state space models of energy conversion systems. The specified probability of packet dropouts in the communication network between the sensor, observer, PI controller and actuator has been considered in the design which allows design of the NCS with guaranteed Lyapunov stability. We consider a switched linear dynamical system structure of the NCS with bounded noise and disturbance inputs while jointly searching for PI controller and observer gains. Using the controller and observer designed with 90% packet drop rate, we show the system performance degradation at 97%–99% QoS using phase portraits of the error dynamical system around the equilibrium as well as through univariate and multivariate hypothesis tests for equal dispersion around the equilibrium of the error dynamical system.

THEORETICAL STUDY OF THE EFFICIENCY OF USING A SPECIAL CUTTER TOOL HOLDER WITH AN ORIENTED POSITION OF CENTER OF RIGIDITY

The theoretical study of vibration resistance of the turning process is effectively carried out using modern mathematical modeling tools, which are characterized by sufficient accuracy of the relationship between input actions or disturbances and the dynamic response of the metal-cutting machine tools. To study and analyze the dynamic processes of a potentially unstable lathe support system during turning, it is important to form a calculation scheme for the processing process and a mathematical model of studies system. The implementation of such a model, which determines the influence of the elastic and dynamic characteristics of a special tool holder with an oriented center of rigidity on the relative vibrations of workpiece and cutter, in general on the main indicator of machine tools dynamic system quality - vibration resistance, is presented. The analytical dependences of tool holders elastic characteristics influence on tool-support system static characteristic, which allow determining its reduced stiffness parameters in main coordinate system directions are presented. Based on the simulation studies results of the cutting process vibration resistance is possible to determine the most rational tool holder elastic parameters, in which the vibration resistance of turning will be relatively increased, taking into account various combinations of the potentially unstable system elastic characteristics of the machine tool carriage during turning. The use of an additional tool holder reduces the overall rigidity in the direction of the cutting force vector and in the direction normal to the workpiece’s surface, while simultaneously increasing the vibration resistance of cutting process due to the reduction of the “coordinate links” negative influence between cutter vibrational movement and the cutting process.

Data-Driven Self-Optimizing Control With Parametric Programing Based Constraint Handling

AbstractSelf-optimizing control (SOC) aims to achieve near-optimal operation regardless of variation in disturbances. For constrained operation, the model-based SOC has been solved as an explicit model predictive control (MPC) problem, where the changes in active constraint sets under large variations in disturbance inputs can be determined via parametric programing. For constrained SOC design with historical data only, this paper presents an extended work for a data-driven method that Zhao et al. proposed (2022 “Global Self-Optimizing Control With Data-Driven Optimal Selection of Controlled Variables With Application to Chiller Plant”, ASME J. Dyn. Sys., Meas., Control., 144(2), p. 021008.) for obtaining global SOC solution. While this work remains to adopt the Koopman subspace model that has been used, the SOC-associated constrained optimization problem is solved by applying the multiparameter quadratic programing technique. The solutions consist of piecewise-affine control laws and active constraint sets that are determined via partitions of the disturbance space. The proposed approach is illustrated with a Modelica simulation model of a chilled-water plant, where minimizing the total power consumption is the economic objective. The results show that the proposed method yields the total power very close to that by an offline genetic algorithm-based global optimization procedure, and substantially better than a best-practice rule-based control strategy. The degree of constraint violations under high cooling load is also smaller with the solution of constrained SOC.

Managing the Handling-Comfort Trade-Off of a Quarter Car Suspension System using Active Disturbance Rejection Control and Vyshnegradsky Equation

In this paper, we tackle the handling-comfort conflicting problem of a quarter-car system using Active Disturbance Rejection Control (ADRC). ADRC parameters are tuned using Vyshnegradsky equations which determine the stability criteria of a third order system. To do this, a multi-objective optimization procedure for selection of ADRC observer coefficients is formulated using a genetic algorithm. Suspension deflection and sprung mass acceleration responses are tested to a random road disturbance input. Simulation results show that the compromised solution between handling and comfort can be achieved by introducing the sprung mass acceleration into the feedback loop of ADRC. Using this approach allows for improving the issue of comfort up to 50 percent with just 10 percent worse performance of the issue of handling.

Interval-Arithmetic-Based Robust Control of Fully Actuated Mechanical Systems

We propose a control approach for fully actuated mechanical systems using interval arithmetic, which guarantees global uniform ultimate boundedness of the tracking error and robust performance despite model uncertainties and input disturbances. Existing robust control methods often require computationally expensive or empirical estimations of bounds of state-dependent, nonlinear perturbations, arising from model mismatches. Our robust feedback control approach is different and removes these difficulties by using interval arithmetic to determine online the worst case perturbation acting on the error dynamics. We present two interval-arithmetic-based robust controllers by robustifying inverse-dynamics and passivity-based nominal control schemes. The effectiveness of our approach is demonstrated on a real robot manipulator with uncertain dynamics.

6G/B5G 네트워크 통신 기반의 스마트팩토리 내 자기부상 물류이송시스템의 시간지연 제어기 설계

Recently, smart factories have been actively introduced in the manufacturing process since the 4th industrial revolution. In particular, research on a logistics transport system using a in-track type magnetic levitational conveyor system is continuing in the semiconductor and display manufacturing industries that require a high-clean environment. This in-track type magnetic levitation system has problems in measuring uncertainty and increasing cost due to the sensors installing to each track. In this paper, we consider a method of installing the sensors to the vehicle and transmitting measurement data using network communication. At this time, end-to-end time latency occurs by using the 6G/B5G communication system in the smart factory. To solve this problem, we propose a controller design method of the magnetic levitation system considering time delay. The proposed controller is designed using the loop shape method for the sensitivity transfer function in frequency domain in order to reduce input disturbance and attenuate sensor noise. The effectiveness of the proposed controller is shown by the simulation.

H − / L ∞ UIO‐based actuator and sensor fault diagnosis design in the finite frequency domain for discrete‐time Lipschitz nonlinear systems

This study investigates the problem of fault diagnosis observer design in the finite frequency domain for discrete-time Lipschitz nonlinear systems simultaneously subjected to actuator and sensor faults and unknown input disturbances. An augmented descriptor system is developed by constructing an augmented state consisting of system states and sensor faults. A novel H−/L∞ unknown input observer (UIO) is proposed in the finite frequency domain using the generalized Kalman–Yakubovich–Popov lemma to render the residual sensitive to actuator faults and robust against disturbances, which can achieve a better fault diagnosis result compared with existing observers in the full frequency domain. In the fault diagnosis scheme, a time-varying threshold is presented based on the L∞ performance index to detect actuator faults, and the sensor fault estimation is simultaneously achieved through an asymptotical estimation of the augmented state. Design conditions for the proposed H−/L∞ UIO are derived and converted into linear matrix inequalities to solve its design matrices together. The effectiveness of the developed H−/L∞ UIO and the fault diagnosis scheme are validated via simulations of an example of a single-link flexible joint robot.

Robust NN‐based backstepping control of non‐affine ferry ship fuel cell‐based DC MGs under stochastic disturbance inputs

Fossil fuels play a significant role in the automobile industry, as well as marine vessel systems, with a lot of benefits like high-density and low-cost power supply, which is relatively easy to reserve, apply and carry. However, fossil fuel combustion produces several emissions such as CO2, which become greenhouse gas emissions and harmful to human health. One of the most favorable emission-free modern technologies is fuel cells, which can be applied to supply power to marine vessel propulsion systems. In such a most electrified ship, the whole of the shipboard grid can be regarded as a direct current (DC) stand-alone microgrid (MG) configuration with linear resistive and nonlinear constant power loads (CPLs). The main challenge in this new configuration of the shipboard MG is stabilizing the system's currents and voltages, subjected to stochastic disturbances arising from the effect of external winds and waves. Hence, the key objective of this research is to introduce a new modified robust adaptive stochastic backstepping controller, which is equipped with an artificial neural network, for stabilizing the current and voltage of the DC MG. The developed approach is robust against uncertainties and disturbances, has a systematic design procedure, and offers a low computational burden compared to the other complicated nonlinear controllers. In the end, a simulation is run for investigating the performance of the proposed controller over the state-of-the-art methods.

A reinforcement learning backstepping‐based control design for a full vehicle active Macpherson suspension system

This paper presents a reinforcement learning backstepping-based control scheme for the design of a full vehicle active suspension system such that both the superior ride comfort and stabilization are achieved. It is well known that the traditional backstepping control scheme is suitable to solve higher order nonlinear system based on the strict-feedback form; however, the disadvantage of the procedure requires computing the derivatives of virtual control signals, which is a very complex task. Therefore, a reinforcement learning scheme using the deep deterministic policy gradient (DDPG) control strategy is developed to replace the process of finding virtual control force in backstepping method. It not only avoids the complexity of analytically calculating the derivatives of the virtual control signals, but also retains the systems robustness when there exist random disturbances from road irregularities. To verify the performance of the proposed active suspension control scheme, the random road unevenness profiles according to ISO 8608 is considered as vertical and lateral disturbance input excitation of a full-vehicle suspension system. Compared with conventional passive suspension system and backstepping control scheme, the proposed approach can be demonstrated to not only effectively improve ride comfort under random road excitation, but also improve the transient response and robustness.

Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems.

This paper investigates the problem of distributed ellipsoidal intersection (DEI) fusion estimation for linear time-varying multi-sensor complex systems with unknown input disturbances and measurement data transmission delays. For the problem with external unknown input disturbance signals, a non-informative prior distribution is used to model the problem. A set of independent random variables obeying Bernoulli distribution is also used to describe the situation of measurement data transmission delay caused by network channel congestion, and appropriate buffer areas are added at the link nodes to retrieve the delayed transmission data values. For multi-sensor systems with complex situations, a minimum mean square error (MMSE) local estimator is designed in a Bayesian framework based on the maximum a posteriori (MAP) estimation criterion. In order to deal with the unknown correlations among the local estimators and to select the fusion estimator with lower computational complexity, the fusion estimator is designed using ellipsoidal intersection (EI) fusion technique, and the consistency of the estimator is demonstrated. In this paper, the difference between DEI fusion and distributed covariance intersection (DCI) fusion and centralized fusion estimation is analyzed by a numerical example, and the superiority of the DEI fusion method is demonstrated.

A SMART SENSING MODULE AIMS AT REDUCTION OF MOTION ARTIFACT FOR WEARABLE TONOMETRY BLOOD PRESSURE DEVICE

Objective: This work focuses on development a unique mini sensing module to alleviate muscle action induced rtefacts and in the meanwhile to enable the alignment free capability for radial BP measurement. Design and method: The coupling variations from motion rtefacts tremendously distorts the sensing signal thus leads the measuring results unreliable and limits the applications of wearable blood pressure monitoring in daily life. It is key to have capability of sensing and recognizing the muscle action induced stress at the measuring site and then have the opportunity to eliminate misleading coupling variation. The mini sensing module consists of two independent sensing channels, which are neighboring distributed in parallel (Fig. 1). The channels are arranged enough close with the spacing only 2 mm in order to guarantee them sensing the same induced stress. The two channels have identical dimension feature with10 mm length and 3 mm width. And a 3 mm × 3 mm hard block is integrated into the middle segment of one channel. The channels are filled with degassed silicon oil and a silicon die is embedded and serves as pressure sensor unit. TPU flexible film is used to seal the channels, which serves also as the mechanical stimulation interface. The smart module is fixed to wrist with channel direction transversal to the arterial path during radial BP measuring. The blocked channel isolates completely (partly at least) stimulating from the vascular and serves as skin stress sensing reference, and the other sensing channel gets combination of the skin stress together with fully stimulating of vascular BP. It is straightforward that the difference two signal can partly at least eliminate the stress induced error, which also is presented in Fig.2. Further rtefacts reduction can be achieved by exploiting specific adaptive filtering algorithm with the blocked signal as disturbance input and the other as source. Results: Experiments have proved the effectiveness of the scheme. Conclusions: By comparison with the BP signal under optimal condition, the sensing module can provide with accurate BP pulse waves in the presence of motion interference.

Hybrid neural network controller for uncertain nonlinear discrete-time systems with non-symmetric dead zone and unknown disturbances

An adaptive hybrid neural control scheme is presented for uncertain non-linear discrete-time Systems (UNLDTS) with non-symmetric dead-zone input and unknown disturbances. This work aims to design an efficient control scheme for the proposed systems in the case of non-symmetric dead-zone as the input due to the presence of which the control of such systems becomes very complex and difficult. The system is converted into an n-step ahead predictor and an adaptive compensative term is introduced to overcome the non-symmetric dead-zone present in the system. A hybrid neural network controller is constructed for the control of the proposed class of systems. The designed controller is proved to be semi-globally uniformly ultimately bounded with the assistance of Lyapunov theory and the error is proved to approach very close to zero. The effectiveness of the proposed scheme is validated through two simulation examples and one example has been inspired from a real word system named continuous stirred tank reactor taken in discrete-time form which ensures the applicability of this controller in real-world applications.

A linear hybrid active disturbance rejection controller design to extenuate powerline bushfires in resonant grounded distribution power systems

This paper proposes a robust linear hybrid controller by combining the active disturbance rejection and proportional–integral controllers (ADRC+PI) for inverter-based arc suppression coils (ASCs) in resonant grounded distribution power systems (RGDPSs). Resonant grounding techniques are used in real power distribution networks for reducing the fault current in order to reduce the severity of powerline bushfires in the presence of single line-to-ground (SLG) faults. The severity of bushfire hazards due to these SLG faults depends on environmental conditions (e.g., wet or dry grounds) that define the behavior of the system. With respect to these conditions, the fault resistance will be lower for wet grounds for which the system model comprises one dominant pole while dry grounds force the system to have one dominant zero with two dominant poles. By considering the circumstances of such groundings, the behavior of power distribution systems changes when there are SLG faults. This paper investigates a detailed analysis in frequency- and time-domains to design a robust arc mitigator based on the hybrid ADRC+PI controller. Furthermore, the robustness of the proposed hybrid controller against the input disturbances is explored in terms of transient and steady-state stability analysis and the results are compared with the ADRC and PI controllers. In addition, the performance of the proposed hybrid ADRC+PI controller is justified by utilizing virtual- and real-time implementations in a digital signal processor (DSP) through MATLAB/SIMULINK platform on a 22 kV (line-to-line) RGDPS under distinctive grounding conditions.

Parameter-dependent robust anti-saturation control of supercavitating vehicle with time-delay characteristics

Aiming at the time-delay characteristics and nonlinear planing force of supercavitating vehicles in the process of motion, a parameter-dependent robust controller with anti-saturation compensator is proposed. First, the system model with nonlinear planing force is transformed into a parameter dependent linear-parameter-varying model containing time-delay part and non-time-delay part. Then consider the external disturbance input to design a robust controller and use convex polyhedron theory and the parameter dependence characteristics of controller to ensure the stability of the system with cavitation radius perturbation. Finally, the anti-saturation compensation method is used to overcome the input constraints caused by the physical limitations of the control mechanism. The simulation results show that the controller has good control effect and robust performance for supercavitating vehicles with time-delay characteristics.

Fault estimation in discrete-time linear systems with mixed uncertainties using proportional integral observer

In this article, the problem of fault estimation is addressed for the class of discrete-time linear systems subject to mixed uncertainties (norm bounded and stochastic uncertainty) and unknown input disturbances. A robust proportional integral (PI) observer is proposed based on H∞ norm minimization for dealing with uncertainties and disturbances. An augmented observer is constructed first where the sensor fault is treated as a state variable and then, H∞ norm is minimized in the linear matrix inequalities (LMI) framework with the application of the Lyapunov function and Young inequalities. The application of the DC motor system and well-known three tank system is given to demonstrate the performance of the proposed observer. Through simulation results, a proposed PI observer has shown the precise estimation of a sensor fault.

Asymptotic stabilisation of coupled delayed time fractional reaction diffusion systems with boundary input disturbances via backstepping sliding-mode control

In this paper we study the asymptotic stabilisation for coupled time fractional reaction diffusion (FRD) systems with time varying delays and input disturbances by backstepping-based boundary sliding-mode control. Here, the spatially varying diffusion coefficients can be same or distinct. To stabilise the system, we first use an invertible backstepping transformation to convert an original dynamics into a target dynamics with new manipulable inputs and perturbations. Then, we employ the sliding-mode algorithm to design this discontinuous controller to suppress disturbances. In this case, we obtain the combined backstepping/sliding-mode controller of the original dynamics. By using the fractional Halanay's inequality, the delay-independent stability condition is obtained for the asymptotic stabilisation of the delayed target system and therefore of the delayed original system under the combined controller. Fractional numerical examples are given to verify the efficiency of the proposed synthesis.

Performance Portrait Method: An Intelligent PID Controller Design Based on a Database of Relevant Systems Behaviors.

The article deals with a computer-supported design of optimal and robust proportional-integral-derivative controllers with two degrees of freedom (2DoF PID) for a double integrator plus dead-time (DIPDT) process model. The particular design steps are discussed in terms of intelligent use of all available information extracted from a database of control tracking and disturbance rejection step responses, assessed by means of speed and shape-related performance measures of the process input and output signals, and denoted as a performance portrait (PP). In the first step, the performance portrait method (PPM) is used as a verifier, for whether the pilot analytical design of the parallel 2DoF PID controller did not omit practically interesting settings and shows that the optimality analysis can easily be extended to the series 2DoF PID controller. This is important as an explicit observer of equivalent input disturbances based on steady-state input values of ultra-local DIPDT models, while the parallel PID controller, allowing faster transient responses, needs an additional low-pass filter when reconstructed equivalent disturbances are required. Next, the design efficiency and conciseness in analyzing the effects of different loop parameters on changing the optimal processes are illustrated by an iterative use of PPM, enabled by the visualization of the dependence between the closed-loop performance and the shapes of the control signals. The main contributions of the paper are the introduction of PPM as an intelligent method for controller tuning that mimics an expert with sufficient experience to select the most appropriate solution based on a database of known solutions. In doing so, the analysis in this paper reveals new, previously undiscovered dimensions of PID control design.

Nonlinear control of a class of non-affine variable-speed variable-pitch wind turbines with radial-basis function neural networks

Due to complicated dynamics, wind turbines’ governing equations are subject to uncertainties and unknown disturbance sources. Despite uncertainties and disturbance sources, the paper’s focus is to design an adaptive controller that enables trajectory-tracking with a zero-converging tracking error. As the main result of a zero tracking error, the turbine can operate at maximum power efficiency. In addition, novel Lyapunov functions are proposed introducing auxiliary adaptive terms to result in closed-loop asymptotic stability in the presence of a non-affine controller input, uncertainties, and unknown disturbance sources. Considering the turbine dynamics, one can divide the wind turbine control problem into torque and pitch control phases. For addressing the nonlinearities and uncertainties of the dynamics in each phase, RBF neural networks are utilized to develop novel control and adaptive laws. To address the non-affine dynamics stemming from the pitch angle, a neural network alongside the implicit function and mean-value theorems are utilized to transform the dynamics into the control affine form. Several auxiliary adaptive variables are proposed in the transformation procedure, leading to closed-loop asymptotic stability. Moreover, using the Lyapunov stability analysis, closed-loop asymptotic stability is obtained for each phase. In the end, simulation results are presented to verify the analytical results where the proposed controller’s performance is compared to that of an existing method in different scenarios. The proposed controller’s simulation results suggest dramatic improvement over those of the existing method in both trajectory-tracking and required control action.

A graph signal processing‐based multiple model Kalman filter (GSP‐MMKF) tool for predictive analytics: An air separation unit process application

AbstractThe industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.