Uncertain Terms Research Articles

Adaptive Finite-Time Optimal Formation Control for Second-Order Nonlinear Multiagent Systems

This article addresses an adaptive backstepping finite-time optimal formation control problem for second-order multiagent systems (MASs) with unknown nonlinear dynamics. Neural networks (NNs) are used to identify the unknown uncertain terms in the controlled system. Then, the finite-time optimal formation control is designed by constructing a novel optimal performance index function containing exponential power terms based on the framework of identifier–actor–critic. It is proved that all signals in the system are bounded in finite time, and the formation control is simultaneously achieved at minimum cost. The effectiveness and superiority of the proposed control algorithm are verified by simulation comparisons and data analyses.

Hierarchical prescribed-time bipartite consensus for multiple Euler–Lagrange systems with input-to-output redundancy and directed matrix-weighted signed graph

This paper studies the prescribed-time bipartite consensus problem for multiple Euler–Lagrange systems (MELSs) under directed matrix-weighted signed graph, in which input-to-output redundancy, external disturbances and uncertain dynamic terms have been taken into consideration. Firstly, this paper proposes the prescribed-time hierarchical control (PTHC) algorithm to tackle the aforesaid issue. It is worth pointing out that the convergence time can be arbitrarily prescribed based on actual engineering needs. Then, the corresponding sufficient conditions for achieving the prescribed-time bipartite consensus are obtained by invoking Lyapunov stability analysis. Eventually, the numerical simulation results are performed, which vividly reflect the effectiveness and feasibility of the developed control algorithm.

Adaptive neural network control of multiple‐sectioned flexible riser with time‐varying output constraint and input nonlinearity

AbstractIn this paper, an adaptive neural network controller is proposed for vibration suppression of a multisectional riser system with unknown boundary disturbance, time‐varying asymmetric output constraint, and input nonlinearity. The considered riser system is composed of a continuous connection of several different pipes, and its dynamic models are represented by a set of multiple continuously connected partial differential equations (PDEs) and an ordinary differential equation (ODE) at the top boundary. Considering input nonlinearity, external disturbance, and system uncertainty, radial basis function (RBF) neural networks are adopted to eliminate the effect of these uncertain terms. Besides, a barrier Lyapunov function is employed to guarantee the restrictions. With the proposed boundary control, the stability of the closed‐loop system is proved and simulations are given to illustrate the well performance of the proposed control strategy.

Adaptive Gain Tuning Rule for Nonlinear Sliding-Mode Speed Control of Encoderless Three-Phase Permanent Magnet Assisted Synchronous Motor

In this paper, an adaptive gain tuning rule is designed for the nonlinear sliding mode speed control (NSMSC) in order to enhance the dynamic performance and the robustness of the permanent magnet assisted synchronous reluctance motor (PMa-SynRM) with considering the parameter uncertainties. A nonlinear sliding surface whose parameters are altering with time is designed at first. The proposed NSMSC can minimize the settling time without any overshoot via utilizing a low damping ratio at starting along with a high damping ratio as the output approaches the target set-point. In addition, it eliminates the problem of the singularity with the upper bound of an uncertain term that is hard to be measured practically as well as ensures a rapid convergence in finite time, through employing a simple adaptation law. Moreover, for enhancing the system efficiency throughout the constant torque region, the control system utilizes the maximum torque per ampere technique. The nonlinear sliding surface stability is assured via employing Lyapunov stability theory. Furthermore, a simple sliding mode estimator is employed for estimating the system uncertainties. The stability analysis and the experimental results indicate the effectiveness along with feasibility of the proposed speed estimation and the NSMSC approach for a 1.1-kW PMa-SynRM under different speed references, electrical and mechanical parameters disparities, and load disturbance conditions.

Pharmacological treatment of Lambert-Eaton Myasthenic Syndrome

Lambert-Eaton myasthenic syndrome (LEMS) is a very rare antibody-mediated autoimmune disease of the neuromuscular junction. Therapy can be divided in symptomatic treatment and immunosuppressive treatment. Symptomatic treatment with amifampridine is the only therapy currently authorized for use in LEMS patients. In the Netherlands the first choice drug is amifampridine base in an extended release formulation instead of the currently authorized amifampridine phosphate. This formulation has lower costs and is possibly safer due to lower peak concentrations. Other therapy used in LEMS patients is prescribed off-label and is based on experience in patients with myasthenia gravis. In many cases pyridostigmine is added as symptomatic treatment. In almost half of patients immunosuppressive therapy is started, mostly corticosteroids with or without azathioprine. Intravenous immunoglobulins and plasma exchange are used as emergency treatment.
 Currently no randomized clinical trials with new therapies are ongoing or announced in patients with LEMS, although multiple new therapies for myasthenia gravis are being investigated. These future therapies can be differentiated in symptomatic and immunomodulating drugs. The immunomodulating drugs can be further differentiated in early stage drugs which target the B-cell, later stage drugs which target the circulating antibodies and targeted therapy which have a disease-specific target. Some early and later stage immunomodulating drugs show promising results in myasthenia gravis although high cost and uncertain long term safety may be limiting for incorporating these drugs in LEMS treatment guidelines.
 Clinical trials in LEMS patients are lacking due to the rarity of the disease and we suggest the following requirements for future trials of potential new treatments: Sufficient power by performing multicenter or n-of-1 trials when appropriate, a cross-over design to reduce the number of patients and using a LEMS-specific quantitative primary outcome measure like the 3TUG score.

Digital deployment control of a tethered satellite system with constrained tension

This paper investigates the dynamics and digital control issues of deploying a tethered satellite using constrained tether tension. To cater to the requirements of the digital techniques applied in engineering applications, unlike existing results, this study develops a digital feedback control strategy to achieve the rapid and stable tether deployment. The proposed digital control law consists of discretized and quantized tether states and is derived based on continuous-time dynamical model instead of the discrete-time one. To keep tether tension at a positive value when implementing control action, the constraint on tether tension is explicitly accounted for during controller design. Furthermore, the discretization and quantization errors are treated as the uncertain terms in the closed-loop system which can be handled by robust analysis and synthesis techniques. A Lyapunov functional, together with an integral performance index, is made to prove the stability of the system, and the desired controller is obtained by solving an optimization problem. Finally, the effectiveness of the proposed controller is demonstrated by numerical simulation.

Quadrotor Trajectory Tracking Using Model Reference Adaptive Control, Neural Network-Based Parameter Uncertainty Compensator, and Different Plant Parameterizations

A quadrotor trajectory tracking problem is addressed via the design of a model reference adaptive control (MRAC) system. As for real-world applications, the entire quadrotor dynamics is typically unknown. To take that into account, we consider a plant model, which contains uncertain nonlinear terms resulting from aerodynamic friction, blade flapping, and the fact that the mass and inertia moments of the quadrotor may change from their nominal values. Unlike many known studies, the explicit equations of the parameter uncertainty for the position control loop are derived in two different ways using the differential flatness approach: the control signals are (i) used and (ii) not used in the parametric uncertainty parameterization. After analysis, the neural network (NN) is chosen for both cases as a compensator of such uncertainty, and the set of NN input signals is justified for each of them. Unlike many known MRAC systems with NN for quadrotors, in this study, we use the kxx+krr baseline controller, which follows from the control system derivation, with both time-invariant (parameterization (i)) and adjustable (parameterization (ii)) parameters instead of an arbitrarily chosen non-tunable PI/PD/PID-like one. Adaptive laws are derived to adjust the parameters of NN uncertainty compensator for both parameterizations. As a result, the position controller ensures the asymptotic stability of the tracking error for both cases under the assumption of perfect attitude loop tracking, which is ensured in the system previously developed by the authors. The results of the numerical experiments support the theoretical conclusions and provide a comparison of the effectiveness of the derived parameterizations. They also allow us to make conclusions on the necessity of the baseline controller adjustment.

A Model-Free-Based Control Method for Robot Manipulators: Achieving Prescribed Performance and Ensuring Fixed Time Stability

This paper addresses three significant challenges in controlling robot manipulators: improving response time, minimizing steady-state errors and chattering, and enhancing controller robustness. It also focuses on eliminating the need for computing the robot’s dynamic model and unknown functions, as well as achieving global fixed-time convergence and the prescribed performance for the control system. To achieve these objectives, a fixed-time sliding mode function is designed, which uses transformation errors to achieve prescribed control performance, with adjustments made to the maximum overshoot, convergence time, and tracking errors to keep them within predefined bounds. Additionally, a radial basis function neural network (RBFNN) is used to eliminate the need for knowledge of the robot’s dynamical properties and uncertain terms, which also reduces negative chattering. Finally, a novel fixed-time terminal sliding mode control (TSMC) algorithm is developed for robot manipulators without using their dynamical model. The fixed-time stability of the control system is thoroughly demonstrated by applying Lyapunov criteria and conducting simulations on a robot manipulator to showcase its effectiveness.

Distributed state estimation for uncertain nonlinear time-delay AC islanded micro grids

This article deals with the problem of distributed observer design containing time delays for uncertain nonlinear distributed generators systems in alternating current–islanded micro grids. In contrast with the centralized observer with the ability to accumulate full output of the plant, we design a set of distributed observers, each having access to partial output of the plant through a distributed observer network. Each observer obtains partial plant output and communicates with its neighboring observers through consensus network. The design method is based on the Lipschitz condition of uncertain nonlinear terms while having time-delay system in view. Suitable selection of Lyapunov functions is made, and the Jensen inequality is considered in delayed uncertain nonlinear micro grids in an undirected network. Furthermore, via design algorithm, the necessary and sufficient conditions for the design of parameters are determined so as to stabilize the error dynamics. Finally, numerical simulations are provided to consider the effectiveness of the proposed method.

Networked fault detection for fuzzy systems using a quantizer with event-triggered strategy

This paper concerns with the problem of fault detection for networked fuzzy systems. Considering the digital communication channel, quantization is adopted to make the data transmission feasible and the induced data distortion is converted into a bounded uncertain term. For the purpose of saving network resources, a new quantizer with an event-triggered strategy is designed to reduce the length and the number of the transmitted data packets, simultaneously. In the process of stability analysis, the T-S fuzzy model approach is employed to approximate the nonlinear networked system, and a more flexible fuzzy design method is presented to get the fault detection filter which can detect the sensor fault in the presence of external disturbance. And sufficient conditions are constructed to guarantee the stability of the filter error system in terms of linear matrix inequalities. Finally, the simulation example is given to verify the feasibility of the method.

Control-oriented meta-learning

Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory tracking performance, provided that any uncertain dynamics terms are linearly parameterizable with known nonlinear features. However, it is often difficult to specify such features a priori, such as for aerodynamic disturbances on rotorcraft or interaction forces between a manipulator arm and various objects. In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal parametric model of these nonlinear features. Our key insight is that we can better prepare the controller for deployment with control-oriented meta-learning of features in closed-loop simulation, rather than regression-oriented meta-learning of features to fit input-output data. Specifically, we meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. With both fully actuated and underactuated nonlinear planar rotorcraft subject to wind, we demonstrate that our adaptive controller outperforms other controllers trained with regression-oriented meta-learning when deployed in closed-loop for trajectory tracking control.

Design of an adaptive terminal sliding mode to control the PMSM chaos phenomenon

This paper schemes a new nonlinear method for controlling and suppressing of chaos instability in the PMSM. First, a nonlinear model of PMSM is considered that is able to describe the dynamic behaviour of the PMSM in different functional conditions, including the occurrence of chaos. As an integral part of most physical systems, disturbance with an unknown upper limit is considered in the design of the control scheme. Parametric uncertainty is also included in the PMSM system model and a new control technique is presented based on robust adaptive and sliding mode approaches to prevent chaos. In the proposed method, adaptive technique is used to estimate the upper bound of uncertain terms, disturbances and nonlinear terms, and the robust sliding mode scheme is designed to stabilize the closed-loop PMSM system in contradiction of chaos. Using comparisons, the efficiency of the proposed method is shown in different working conditions.

Distributed Adaptive Control Framework for Enhanced Voltage and Frequency Regulation in Inverter Interfaced Autonomous Distribution Network

This article presents a unique design method for an adaptive neural network based backstepping-like control (ANNBC) scheme. The technique is employed for synthesizing the primary controller for inverter interfaced distributed generators (IIDGs) integrated to an autonomous distribution network. Further, an optimal distributed secondary control framework is developed for a multiple IIDGs-based autonomous distribution network. The secondary controller facilitates optimal gain selection to regulate the frequency of the system and voltage of the critical bus to their desired set points. The framework also achieves accurate real and reactive power sharing among the IIDGs according to their power ratings. The novel design procedure of the proposed control framework takes into account the entire system dynamics of the IIDG including the uncertain terms (viz., load current and network dynamics) and is completely independent of the system parameters information. Suitable update laws are designed for estimating the unknown weights of the neural network and the uncertain system parameters. Lyapunov analysis is used to show that the tracking errors and parameter estimation errors are uniformly ultimately bounded. Finally, case studies are conducted on a typical autonomous distribution network having a single and multiple IIDGs modeled in MATLAB/Simulink platform.

Design space covering for uncertainty

Managing uncertainty in engineering design continues to challenge engineers. By combining the concept of regret from robust optimization with that of design space reduction from set-based design, a new complementary view on uncertainty referred to as Design Space Covering for Uncertainty (DSC-U) is introduced. This method develops a trade-space between reducing the design space and locking in sub-optimal performance while considering interval uncertainty that impacts the problem. A double-loop algorithm and a more efficient set covering algorithm are presented along with the structure of the method. These are demonstrated on three sample problems, a previously-studied cantilever tube optimization, the Rosenbrock function with different levels of coupling between the uncertain parameters, and a ship midship section design problem with thirty-six design variables and four uncertain terms. The DSC-U approach is shown to be practical in providing a complementary insight into the impact of uncertainty for a wide range of design techniques.

TODIM-VIKOR method based on hybrid weighted distance under probabilistic uncertain linguistic information and its application in medical logistics center site selection.

At a time of global epidemic control, the location of the medical logistics distribution center (MLDC) has an important impact on the operation of the entire logistics system to reduce the operating costs of the company, enhance the service quality and effectively control the COVID-19 on the premise of increasing the company's profits. Thus, the research on the location of MLDC has important theoretical and practical application significance separately.Recently, the TODIM and VIKOR method has been used to solve multiple-attribute group decision-making (MAGDM) issues. The probabilistic uncertain linguistic term sets (PULTSs) are used as a tool for characterizing uncertain information. In this paper, we design the TODIM-VIKOR model to solve the MAGDM in PULT condition. Firstly, some basic concept of PULTSs is reviewed, and TODIM and VIKOR method are introduced. The extended TODIM-VIKOR model is proposed to tackle MAGDM problems under the PULTSs. At last, a numerical case study for medical logistics centersite selection (MLCSS) is given to validate the proposed method.

Fault detection and prediction scheme for nonlinear stochastic distribution systems

AbstractModel‐based fault detection technique has a broad range of applications because of the small change to the system when the system state is known to be available and the low cost. For nonlinear stochastic distribution systems containing uncertain disturbance term, a model‐based fault detection and failure time prediction scheme is proposed in this paper, and observers are designed to detect whether the incipient fault has occurred in the system. The residual is obtained by comparing the output of the actual system with the output of the observer. When the residual exceeds the threshold value obtained by derivation, it is determined that the fault has occurred in the system. The fault size can then be estimated in real time and used to determine the time to failure (TTF) or the remaining useful life of the system. The TTF of the system is obtained by comparing the magnitude of the current system fault with the fault threshold. Finally, the feasibility of the presented fault detection scheme is proved by the Lyapunov stability theory and the validity of the scheme is proved by computer simulation.

A probabilistic uncertain linguistic FMEA model based on the extended ORESTE and regret theory

As a means of identifying, preventing, and controlling potential system failures, failure mode and effect analysis (FMEA) has been extensively applied in many industries. However, the classical FMEA method has several defects in actual situations, such as being unable to express uncertain information, overlooking the weights of FMEA experts and risk factors (RFs), and without taking into account the experts’ psychological behaviors. To break through these constraints, a novel FMEA model based on probabilistic uncertain linguistic term sets (PULTSs), Organísation, rangement et Synthèse de données relarionnelles (in French) (ORESTE) and regret theory (RT) is proposed in this paper. First, PULTSs are introduced to express FMEA experts’ assessments under uncertainty. Then, the maximizing consensus model with PULTSs context (PUL-MCM) is constructed to obtain the weights of FMEA experts. Moreover, direct assessment weighting and maximizing deviation method (MDM) are utilized to determine the subjective and objective weights of RFs respectively, so as to reflect the importance of RFs comprehensively. Afterward, the extended ORESTE and RT are combined to prioritize FMs, which not only considers the psychological behaviors of experts and the detailed relations between failure modes (FMs) but avoids the information loss caused by the defuzzification of subjective weights of RFs. Finally, an example of the pasting process failures in lead-acid battery production is presented to illustrate the feasibility and rationality of the novel FMEA model, while sensitivity and comparative analyses are also performed to further demonstrate the flexibility and superiority of the novel FMEA model.

Revolution in 1525: Thomas Müntzer and Mühlhausen

Scholars of Thomas Müntzer, the revolutionary religious leader of the German Peasants War of 1524–6 and Engels’s hero, have been waiting for this book to appear. It is the first fully archivally grounded study of the Peasants’ War in Mühlhausen, and it does not disappoint. The result of two decades of research, it is a major contribution not only to studies of Thomas Müntzer, but to our understanding of the early Reformation. Müller finally places Müntzer in his context, and Mühlhausen within the wider history of urban revolt. Beautifully written and structured, it also marks a historiographical shift that unites the scholarly traditions of both the former Eastern and Western Germanies, taking the best of each. Müller’s method, he explains, is ‘thick description’, drawing on the work of the anthropologist Clifford Geertz. But this is not a conventional microhistory. Rather, it is the most thorough reconstruction of ‘what happened’, using all the available sources and allowing for a multiplicity of actors, contexts, and goals. The result is a complex narrative. There was no single ‘Müntzer party’, Müller shows, and the Mühlhausen Reformation was driven by a group of well-educated, passionate clergy, many of whom were influenced by the theologian Andreas Karlstadt perhaps more than by Müntzer; the period of Müntzer’s ascendancy lasted only three months of March, April and May and even then, it was not total. As Müller strikingly puts it, Luther’s characterization of Müntzer as the ‘devil who reigns at Mühlhausen’ was simply not true; Müntzer never reigned, and even his famous call to his fellow citizens at the civic muster was undercut by the captain, who told him in no uncertain terms that he could ‘preach in the church, but not in the field’.

Optimization of fuzzy K-objective fractional assignment problem using fuzzy programming model

The assignment problem is one of the core combinatorial optimization problems in the optimization branch, and the theory and applications of fractional programming have made great strides in recent years. Usually, the possible coefficient values of linear fractional programming and real-world problems are frequently only known to the decision in vague or uncertain terms. Hence, it would be more acceptable to interpret the coefficients for as fuzzy numerical information. In this paper, a fuzzy bi-objective fractional assignment (FBOFAP) has been formulated. A problem has been defined. Here, triangular shapes are used to indicate the parameters. The fuzzy problem is turned into a typical crisp problem through α-cut using a fuzzy number and then the compromise solution is generated by fuzzy programming.

Finding Bede in the “Lindisfarne” Gospels: Aldred the Scribe and “beda ðe broema boecere”

Finding Bede in the “Lindisfarne” Gospels: Aldred the Scribe and “beda ðe broema boecere”