Handle Parameter Uncertainties Research Articles

Observer-Based Security Fuzzy Control for Nonlinear Networked Systems Under Weighted Try-Once-Discard Protocol

This paper is concerned with the observer-based security fuzzy control problem for a class of nonlinear networked systems with imperfectly matched membership functions (MFs) under weighted try-once-discard (WTOD) protocol. The nonlinear networked system is constructed based on interval type-2 (IT2) Takagi-Sugeno (T-S) model, which can properly handle parameter uncertainties via lower and upper MFs. To mitigate the communication load, WTOD protocol is exploited to schedule the transmission order of the signals, in which those signals may be subjected to randomly occurring deception attacks. The observer is constructed under unmeasurable premise variables, and then the controller with imperfect matching MFs is designed according to the estimated states. Moreover, the observer-based controller is derived with Lyapunov stability theory such that the stochastic stability of the augmented closed-loop system with predefined disturbance attenuation performance can be guaranteed. Subsequently, sufficient conditions are provided for the desired observer and controller gain matrices based on linear matrix inequalities (LMIs) technique. Numerical simulation example demonstrates the validity of the proposed observer-based security fuzzy control design scheme.

Low-Power Fault-Tolerant Control for Nonideal High-Order Fully Actuated Systems

This article presents a novel observer-based fault-tolerant controller framework for a class of nonideal time-varying high-order fully actuated systems (HOFASs). The HOFAS theory is an emerging nonlinear dynamical system theory, which can yield global stability. In order to improve the ability of HOFAS theory to handle parameter uncertainties, actuator faults, and measurement noises, a nonlinear extended state observer and low-power fully actuated controller framework is established in this article. Particularly, the linear framework conforms to the generalized separation principle and highlights the advantages of HOFAS parametric design. Moreover, the work takes into account fault tolerance and noise suppression in engineering applications, reducing the dependence of HOFAS theory on model accuracy. The uniformly bounded stability and noise suppression performance are also proved theoretically and illustrated experimentally.

Preventing inlet unstart in air-breathing hypersonic vehicles using adaptive backstepping control with state constraints

Aiming at the problem that the inlet is prone to unstart during the flight of air-breathing hypersonic vehicles (AHV), an adaptive backstepping control method with state constraints is proposed to prevent the occurrence of inlet unstart. Firstly, a parametric model of AHV with velocity, flight path angle, angle of attack and angle of attack rate as state variables is derived. Then, adaptive dynamic inverse control is adopted for the velocity subsystem, and adaptive backstepping control with state C is adopted for the flight path angle subsystem. The tracking errors of angle of attack and angle of attack rate are constrained by asymmetric barrier Lyapunov function (ABLF) and symmetric barrier Lyapunov function (SBLF) respectively, and smooth saturation function is constructed to restrict the virtual control commands. Adaptive laws are used to handle parameter uncertainty. Finally, it is proved by Lyapunov theory that the states of the closed-loop system can be uniformly and ultimately bounded, and the constraints on angle of attack and angle of attack rate can be satisfied. The simulation results show that compared with the adaptive backstepping control method, the proposed method can guarantee that the angle of attack and angle of attack rate are always within the operating limits of scramjet engine, and effectively avoid the occurrence of inlet unstart.

A model to analyze human and organizational factors contributing to pandemic risk assessment in manufacturing industries: FBN-HFACS modelling

This study presents a holistic model based on Fuzzy Bayesian Network-Human Factor Analysis and Classification System (FBN-HFACS) to analyze contributing factors in the pandemic, Covid 19, risk management under uncertainty. The model contains three main phases include employing a) HFACS to systematically identify influencing factors based on validation using content validity indicators, b) Fuzzy Set Theory to obtain the prior probability distribution of contributing factors in pandemic risk and address the epistemic uncertainty and subjectivity, and finally, c) Bayesian network to develop causality model of the risk, probabilistic inferences and handle parameter and model uncertainties. The Ratio of Variation (RoV), as BN-driven importance measures, is utilized to conduct sensitivity analysis and explore the most critical factors that yield effective safety countermeasures. The model is tested to investigate four large manufacturing industries in South Khorasan (Iran). It provided a deep understanding of influencing human and organizational factors and captured dependencies among those factors, while quantitative finding paves a way to efficiently make risk-based decisions to deal with the pandemic risks under uncertainty.

Precision motion control for electro-hydraulic axis systems under unknown time-variant parameters and disturbances

This article focuses on asymptotic precision motion control for electro-hydraulic axis systems under unknown time-variant parameters, mismatched and matched disturbances. Different from the traditional adaptive results that are applied to dispose of unknown constant parameters only, the unique feature is that an adaptive-gain nonlinear term is introduced into the control design to handle unknown time-variant parameters. Concurrently both mismatched and matched disturbances existing in electro-hydraulic axis systems can also be addressed in this way. With skillful integration of the backstepping technique and the adaptive control, a synthesized controller framework is successfully developed for electro-hydraulic axis systems, in which the coupled interaction between parameter estimation and disturbance estimation is avoided. Accordingly, this designed controller has the capacity of low-computation costs and simpler parameter tuning when compared to the other ones that integrate the adaptive control and observer/estimator-based technique to dividually handle parameter uncertainties and disturbances. Also, a nonlinear filter is designed to eliminate the “explosion of complexity” issue existing in the classical back-stepping technique. The stability analysis uncovers that all the closed-loop signals are bounded and the asymptotic tracking performance is also assured. Finally, contrastive experiment results validate the superiority of the developed method as well.

A double-oracle, logic-based Benders decomposition approach to solve the [formula omitted]-adaptability problem

The K-adaptability problem is a special case of adaptive robust optimization with discrete recourse that aims to prepare K solutions under uncertainty, and select among them upon full knowledge of the realized scenario. We propose a novel approach to solve K-adaptability problems with linear objective and constraints, binary first-stage decision variables, second-stage objective uncertainty, and a polyhedral uncertainty set. A logic-based Benders decomposition is applied to handle the first-stage decisions in a master problem, thus the Benders subproblem becomes a min–max–min robust combinatorial optimization problem. To solve the subproblem, a double-oracle algorithm that iteratively generates adverse scenarios and recourse decisions and assigns scenarios to K-subsets of the decisions by solving p-center problems is devised. Extensions of the proposed approach to handle parameter uncertainty in both the first-stage objective and the second-stage constraints, and for integer first-stage decision variables and nonlinear functions, are also provided. We show that, under mild conditions, the proposed algorithm converges to an optimal solution and terminates in a finite number of iterations. Numerical results obtained from experiments on benchmark instances of the adaptive shortest path problem, the regular knapsack problem, the asset liability-management problem, and a generic K-adaptability problem demonstrate the performance advantage of the proposed approach when compared to state-of-the-art methods in the literature.

Design of Sliding Mode Controllers using Reduced-order Koopman Mode Representations

The Koopman operator framework allows for a linear, but infinite-dimensional, representation of the dynamics of a non-linear system. The Koopman modes, or observables, and the resulting linear dynamics are derived purely using a data-driven framework, where the data are system outputs measured at discrete samples; improving accuracy of the Koopman representation requires a large number of such modes to be considered. Recent results consider the system input as well, in the derivation of the discrete linear dynamics, thus enabling the design of controllers. Sliding mode controllers (SMCs), including the discrete-time versions, can handle parameter uncertainties and variations and also ensure that the control objective is satisfied in finite time. In this paper, a discrete-time SMC is designed for the output control of a dynamic system approximated by fewer Koopman modes; the SMC is expected to handle uncertainties introduced by the ignored modes. Conditions are identified for the closed-loop system to be stable, with the occurrence of sliding mode.

Distributed fault detection for uncertain Lipschitz nonlinear multi‐agent systems in finite frequency domain

AbstractThis article focuses on distributed fault detection for uncertain Lipschitz nonlinear multi‐agent systems (MASs). To handle parameter uncertainties, a finite‐frequency unknown input observer (UIO) is designed to formulate the reference residual system and based on it, a robust UIO corresponding to the fault detection system is designed using an model matching approach. In the UIO constructed for one agent, the unknown input composed of faults in the other agents and disturbances in the whole system is partitioned to decouple part of them, which avoids the constraint of the rank condition of UIO and allows for fault detection for more than one faulty agent. The generated residual is sensitive to the corresponding fault while robust against the nondecoupled unknown input and parameter uncertainties, of which the design conditions are transformed into a set of linear matrix inequalities (LMIs). A numerical simulation is conducted to illustrate the effectiveness of the proposed method.

Output feedback adaptive RISE control for uncertain nonlinear systems

AbstractIn this article, committed to extending the robust integral of the sign of the error (RISE) feedback control to the working condition of output feedback, a novel output feedback controller with a continuously bounded control input which combines the adaptive control and integral robust feedback will be proposed for trajectory tracking of a family of nonlinear systems subject to modeling uncertainties. A novel adaptive state observer (ASO) with disturbance rejection performance is creatively constructed to derive real‐time estimation of the unmeasured state signals. Moreover, a projection‐type adaption law is integrated to handle parameter uncertainties and an integral robust term is employed to deal with external disturbances. It is shown that asymptotic estimation performance and meanwhile asymptotic tracking result can eventually be derived. Simulation validations are implemented to demonstrate the high tracking performance of the presented controller. Notably, the synthesized control algorithm can be readily extended to the Euler–Lagrange systems. Typically, it can be extended to practical electromechanical equipment such as three‐dimensional vector forming robots to improve the real‐time forming accuracy.

Energy-maximising tracking control for a nonlinear heaving point absorber system commanded by second order sliding modes

Energy-maximising control has proven to be of fundamental aid in the pathway towards commercialisation of wave energy conversion technology. The WEC control problem is based upon the design of a suitable control law capable of maximising energy extraction from the wave resource, while effectively minimising any risk of component damage. A particularly well-established family of WEC controllers is based upon a composite structure, where an optimal velocity reference is generated via direct optimal control procedures, followed by a suitable tracking control strategy. This paper presents the design and synthesis of a second order sliding mode controller to attain a reference tracking for a wave energy system. The presented approach can inherently handle parameter uncertainty in the model, which is ubiquitous within hydrodynamic modelling procedures. Furthermore, the proposed sliding mode controller has relatively mild computational requirements, and finite-time convergence to the designed surface, hence being an ideal candidate for real-time energy-maximising control of WEC systems.

Cost-effectiveness of using amyloid positron emission tomography in individuals with mild cognitive impairment

BackgroundAmyloid positron emission tomography (PET) makes it possible to diagnose Alzheimer’s disease (AD) in its prodromal phase including mild cognitive impairment (MCI). This study evaluated the cost-effectiveness of including amyloid-PET for assessing individuals with MCI.MethodsThe target population was 60-year-old patients who were diagnosed with MCI. We constructed a Markov model for the natural history of AD with the amyloid positivity (AP). Because amyloid-PET can detect the AP MCI state, AD detection can be made faster by reducing the follow-up interval for a high-risk group. The health outcomes were evaluated in quality-adjusted life years (QALYs) and the final results of cost-effectiveness analysis were presented in the form of the Incremental Cost-Effectiveness Ratio (ICER). To handle parameter uncertainties, one-way sensitivity analyses for various variables were performed.ResultsOur model showed that amyloid-PET increased QALYs by 0.003 in individuals with MCI. The estimated additional costs for adopting amyloid-PET amounted to a total of 1250 USD per patient when compared with the cost when amyloid-PET is not adopted. The ICER was 3,71,545 USD per QALY. According to the sensitivity analyses, treatment effect of Donepezil and virtual intervention effect in MCI state were the most influential factors.ConclusionsIn our model, using amyloid-PET at the MCI stage was not cost-effective. Future advances in management of cognitive impairment would enhance QALYs, and consequently improve cost-effectiveness.

Stable fuzzy controllers via LMI approach for non-linear systems described by type-2 T–S fuzzy model

PurposeThe purpose of this paper is to stabilize the type-2 Takagi–Sugeno (T–S) fuzzy systems with the sufficient and guaranteed stability conditions. The given conditions efficaciously handle parameter uncertainties by the upper and lower membership functions of the type-2 fuzzy sets (FSs).Design/methodology/approachThis paper reports on a relevant study of stable fuzzy controllers and type-2 T–S fuzzy systems and reported that the synthesis of controller for nonlinear systems described by the type-2 T–S fuzzy model is a key problem and it can be resolve to convex problems via linear matrix inequalities (LMIs).FindingsThe multigain fuzzy controllers are established to improve the solvability of the stability conditions, and the authors design multigain fuzzy controllers which have extensive information of upper and lower membership grades. Consequently, the authors derive the traditional stability condition in terms of LMIs. One simulation examples illustrate the effectiveness and robustness of the derived stabilization conditions.Originality/valueThe uncertain MIMO nonlinear system described by Type-2 Takagi-Sugeno (T-S) fuzzy model, and successively LMI approach used to determine the system stability conditions. The proposed control approach will give superior fault-tolerant control permanence under the actuator fault [partial loss of effectiveness (LOE)]. Also the controller robust against the unmeasurable process disturbances. Additionally, the statistical z-test are carried out to validate the proposed control approach against the control approach proposed by Himanshukumar and Vipul (2019a).

NMPC-based control scheme for a semi-batch reactor under parameter uncertainty

Exothermic reactions are often performed in SBR because the generated reaction heat can be more easily kept under control in such construction. However, an unsuitable control system can lead to the development of thermal runaway, which may cause lethal damage. NMPC with implemented thermal runaway criteria is a promising tool to operate SBRs. However, engineers should always consider plant-model mismatch because uncertain predictions can cause undesirable scenarios. A novel control framework is proposed to operate SBRs and consists of NMPC with the implemented runaway criterion, extended Kalman filter and parameter identification algorithm. Both Multi-Stage NMPC and NMPC with the worst-case scenario are investigated and tested in terms of ability to handle parameter uncertainty. The former is 38 times slower than the latter with no noticeable increase in reactor performance. NMPC initialized based on the worst-case scenario with updating uncertain kinetic parameters results in a promising control structure for SBRs.

Load Frequency Control Optimization using PSO Based Integral Controller

This paper presents Automatic Generation Control (AGC) of a power system using integral controller. In the present day power systems, it has become absolutely essential to maintain the quality of the power generated indicating the need of a robust system that can handle parameter uncertainties neglecting disturbances. Although,extensive research has been done in thisarea, design of an efficient and robust system still remains one of the important issues that need to be addressed. Hence in this paperan integral controller has been designed for a single-area thermal power system without reheat turbine. The optimum controller gain is obtained by Particle Swarm Optimization (PSO) based on Integral of Absolute Error (IAE) and Integral of Square Error (ISE) criterion. The second part of the investigation includes robustness testing of the designed controller against different load conditions and plant parameter variations. The results obtained are compared to those obtained by other control methodologies presented in the recent literature. The results of the simulation validate the superiority of the approach in terms of improvement in the transient response and robustness to plant parameter variations.

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

In this paper, an adaptive neural fault-tolerant tracking control scheme is presented for the yaw control of an unmanned-aerial-vehicle helicopter. The scheme incorporates a non-affine nonlinear system that manages actuator faults, input saturation, full-state constraints, and external disturbances. Firstly, by using a Taylor series expansion technique, the non-affine nonlinear system is transformed into an affine-form expression to facilitate the desired control design. In comparison with previous techniques, the actuator efficiency is explicit. Then, a neural network is considered to approximate unknown nonlinear functions, and a time-varying barrier Lyapunov function is employed to prevent transgression of the full-state variables using a backstepping technique. Robust adaptive control laws are designed to handle parameter uncertainties and unknown bounded disturbances to cut down the number of learning parameters and simplify the computational burden. Moreover, an auxiliary system is constructed to guarantee the pitch angle of the UAV helicopter yaw control system to satisfy the input constraint. Uniform boundedness of all signals in a closed-loop system is ensured via Lyapunov theory; the tracking error converges to a small neighborhood near zero. Finally, when the numerical simulations are applied to a yaw control of helicopter, the adaptive neural controller demonstrates the effectiveness of the proposed technique.

Performance-Guaranteed Switching Adaptive Control for Nonlinear Systems With Multiple Unknown Control Directions

In this paper, an adaptive control strategy with novel performance-guaranteed switching mechanism is proposed for a class of nonlinear parametric strict-feedback systems with unknown parameters and multiple unknown control directions. Firstly, adaptive controller is designed by combining tuning function design and barrier Lyapunov function (BLF) techniques, to handle parameter uncertainties and avoid over-parametrization phenomenon. The unknown control directions are estimated online with the proposed novel switching criterion composed of a BLF-type monitoring function and a threshold function. It is shown that the correct directions can be identified rapidly with only finite times of switching and mis-switching problem is avoided. At switching moments, a novel reset mechanism is triggered to adjust the value of integrator to avoid failure of BLF method. Consequently, tracking error of reference signal converges to zero asymptotically and all closed-loop signals are proved to be bounded. In addition, the tracking errors can be restricted into prescribed intervals without information of control directions. Numerical results demonstrate the effectiveness and performance of the proposed method.

Adaptive Projection Neural Network for Kinematic Control of Redundant Manipulators With Unknown Physical Parameters

Redundancy resolution is of great importance in the control of manipulators. Among the existing results for handling this issue, the quadratic program approaches, which are capable of optimizing performance indices subject to physical constraints, are widely used. However, the existing quadratic program approaches require exactly knowing all the physical parameters of manipulators, the condition of which may not hold in some practical applications. This fact motivates us to consider the application of adaptive control techniques for simultaneous parameter identification and neural control. However, the inherent nonlinearity and nonsmoothness of the neural model prohibits direct applications of adaptive control to this model and there has been no existing result on adaptive control of robotic arms using projection neural network (PNN) approaches with parameter convergence. Different from conventional treatments in joint angle space, we investigate the problem from the joint speed space and decouple the nonlinear part of the Jacobian matrix from the structural parameters that need to be learnt. Based on the new representation, we establish the first adaptive PNN with online learning for the redundancy resolution of manipulators with unknown physical parameters, which tackles the dilemmas in existing methods. The proposed method is capable of simultaneously optimizing performance indices subject to physical constraints and handling parameter uncertainty. Theoretical results are presented to guarantee the performance of the proposed neural network. Besides, simulations based on a PUMA 560 manipulator with unknown physical parameters together with the comparison with an existing PNN substantiate the efficacy and superiority of the proposed neural network, and verify the theoretical results.

The term structure of exchange rate predictability: Commonality, scapegoat, and disagreement

In this paper, we study the exchange rate predictability across a range of investment horizons by proposing a generalized (term structure) model to capture the dynamics between the risk premium component of exchange rates and a broad set of variables meanwhile handle both parameter and model uncertainty. We also demonstrate the projections of common predictable information over the term structure, and existence of time-varying term-structural effect and model disagreement effect of exchange rate predictors in FX trading, which in turn validates the practical use of our model. We then utilize the time-variation in the probability weighting to identify the scapegoat drivers of customer order flows. We further comprehensively evaluate both statistical and economic significance of the model allowing for a full spectrum of currency investment management, and find that the model generates substantial performance fees of 6.5% per annum.

Improved Approach to Robust Control for Type-2 T-S Fuzzy Systems

This paper is concerned with the robust stability conditions to stabilize the type 2 Takagi-Sugeno (T-S) fuzzy systems. The conditions effectively handle parameter uncertainties using lower and upper membership functions. To improve the solvability of the stability conditions, we establish a multigain controller with comprehensive information of the lower and upper membership grades. In addition, a well-organized relaxation technique is proposed to fully exploit relationship among fuzzy weighting functions and their lower and upper membership grades, which enlarges a set of feasible solutions. Therefore, we derive a less conservative stabilization condition in terms of linear matrix inequalities (LMIs) than those in the literature. Two simulation examples illustrate the effectiveness and robustness of the derived stabilization conditions.

Adaptive robust tracking control of a proportional pressure-reducing valve with dead zone and hysteresis

Proportional pressure-reducing valves (PPRVs) are typical proportional valves widely used in the hydraulic industry because they are inexpensive, not prone to malfunction, easy to handle and service. However, they suffer from performance degradations due to the existence of dead zone and hysteresis. An effective control method should be developed to improve the performance of PPRVs, as their characteristics significantly affect the entire hydraulic system. This article focuses on high-performance pressure-tracking control of a PPRV with dead zone and hysteresis. A non-linear phenomenological model is put forward to describe the characteristics of dead zone and hysteresis, as well as dynamic behaviour of the PPRV. The proposed phenomenological model is described using an ideal third-order linear model preceded by a dead zone term and a hysteresis term. To handle parameter uncertainties and uncertain non-linearities in the phenomenological model, an adaptive robust controller is synthesized without constructing a dead zone inverse or hysteresis inverse. The developed controller guarantees bounded pressure-tracking error and precise steady-state tracking accuracy. Comparative simulations and experiments with different pressure trajectories are carried out to verify the effectiveness of the proposed method. Both simulation and experimental results show that with the proposed adaptive robust control (ARC) approach, the non-linear dead zone and hysteresis may be well compensated and improved pressure-tracking performance achieved.