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Summary: This paper describes the application of an indirect linear fractional transformation (LFT)–based state‐space adaptive control scheme to a transport aircraft, within the context of the European project REconfiguration of CONtrol in Flight for Integral Global Upset REcovery. The principle of the scheme is to design and validate off‐line a gain‐scheduled controller, depending on the plant parameters to be estimated, and to combine it online with a model estimator, so as to minimize the onboard computational time and complexity. A modal approach, very classical for the design of a flight control law, is used to directly synthesize the static output feedback LFT controller, depending on the control and stability derivatives, ie, the parameters of the linearized aerodynamic state‐space model to be estimated. Since the gain‐scheduled LFT controller online depends on the parameter estimates instead of the true values, its robustness to transient and asymptotic estimation errors needs to be assessed using μ and integral quadratic constraint analysis techniques. A primary concern being an online implementation, a fully recursive frequency‐domain estimation technique is proposed, with a low online computational burden and the capability to track time‐varying parameters. Full nonlinear simulations along a trajectory validate the good performance properties of the combined estimator and gain‐scheduled flight controller. To some extent, minimal guaranteed stability and performance properties of the adaptive scheme can be ensured by switching to a robust controller when the parameter estimates are not reliable enough, thus bypassing the Certainty Equivalence Principle. [ABSTRACT FROM AUTHOR]

In modern control theory, it is more applicable for digital computers using a sampled-data controller than a continuous-time controller. Meanwhile, input saturation and disturbance inevitably exist in control systems. Thus, how to propose an applicable sampled-data controller for nonlinear systems considering input saturation together with external disturbances is essential for applications. To deal with this control problem, a novel auxiliary control signal is designed in this paper. Then, by using Lyapunov method, the control design process is simplified compared with the traditional backstepping technique, and only one Lyapunov function is needed. In addition, the state and disturbance observers are proposed with the new auxiliary control signal to track the unavailable states and external disturbances. Finally, we propose a novel controller reflecting the sampled-data output-feedback for the nonlinear systems to ensure the system stability. The effectiveness of proposed control method in this paper is confirmed through two sets of numerical simulation results. [ABSTRACT FROM AUTHOR]

This work presents a new graphical system to represent hydrological dynamical models and their interactions. We propose an extended version of the Petri Nets mathematical modeling language, the Extended Petri Nets (EPN), which allows for an immediate translation from the graphics of the model to its mathematical representation in a clear way. We introduce the principal objects of the EPN representation (i.e., places, transitions, arcs, controllers, and splitters) and their use in hydrological systems. We show how to cast hydrological models in EPN and how to complete their mathematical description using a dictionary for the symbols and an expression table for the flux equations. Thanks to the compositional property of EPN, we show how it is possible to represent either a single hydrological response unit or a complex catchment where multiple systems of equations are solved simultaneously. Finally, EPN can be used to describe complex Earth system models that include feedback between the water, energy, and carbon budgets. The representation of hydrological dynamical systems with EPN provides a clear visualization of the relations and feedback between subsystems, which can be studied with techniques introduced in nonlinear systems theory and control theory. Key Points: We present a graphical system to represent hydrological dynamical systems called Extended Petri Nets (EPN), with a one‐to‐one correspondence with the driving equationsEPN topology and connections clarify the causal relationship between compartments and the feedback between them; two different types of feedback are presentedEPN can be used to formalize perceptual models from field work into equations [ABSTRACT FROM AUTHOR]

• The control is designed globally and accomplished locally. • Design a nonlinear to assure stability of the whole system. • Storage devices are used to stabilize MicroGrid on different time scales. • Rigorous analysis of stability of the overall system. • The control performance is verified by simulations and by experimentation. This paper is a contribution for DC MicroGrid control and introduces a rigorous dynamics' analysis, taking into account intermittency effects. We propose an hierarchical control scheme, based on nonlinear control theory, in particular Lyapunov, backstepping and input/output feedback linearization. The resulting algorithms are simple, comparable to standard nested PIs, and do not need important computer resources, such to be easily implemented in small micro-controllers. In addition to allowing an explicit stability analysis, they present the advantage of preserving performance for the whole operation region, and to be easy to tune. The proposed DC MicroGrid and its control are then verified either by computer simulations and by experiments. The results show the good performance of the system under variations on production and on consumption. Finally, the proposed scheme is compared to standard PI based controls, with better performance with simpler tuning procedure. [ABSTRACT FROM AUTHOR]

This paper introduces a control problem of regulation of energy flows in a two-site electricity and heat supply system, where two combined heat and power (CHP) plants are interconnected via electricity and heat flows. The control problem is motivated by recent development of fast operation of CHP plants to provide ancillary services of power system on the order of tens of seconds to minutes. Due to the physical constraint that the responses of the heat subsystem are not necessary as fast as those of the electric subsystem, the target controlled state is not represented by any isolated equilibrium point, implying that stability of the system is lost in the long-term sense on the order of hours. In this paper, we first prove in the context of nonlinear control theory that the state-space model of the two-site system is nonminimum phase due to nonexistence of isolated equilibrium points of the associated zero dynamics. Instead, we locate a one-dimensional (1D) invariant manifold that represents the target controlled flows completely. Then, by utilizing a virtual output under which the state-space model becomes minimum phase, we synthesize a controller that achieves not only the regulation of energy flows in the short-term regime but also stabilization of an equilibrium point in the long-term regime. Effectiveness of the synthesized controller is established with numerical simulations with a practical set of model parameters. [ABSTRACT FROM AUTHOR]

The paper aims to develop a new fractional-order nonlinear sliding mode controller with conformable fractional-order (CFO) derivative for a class of uncertain non-autonomous systems considering disturbance. The control method involves a novel CFO nonlinear surface as well as a new CFO switching function. The stability of the CFO controller is proved by means of the Lyapunov stability theorem. The simulation results show the improvement in the developed controller in terms of faster convergence speed, chattering reduction and lower control effort. Moreover, it needs simple calculations. [ABSTRACT FROM AUTHOR]

Continuous material processing operations like printing and textiles manufacturing are conducted under highly variable conditions due to changes in the environment and/or in the materials being processed. As such, the processing parameters require robust real-time adjustment appropriate to the conditions of a nonlinear system. This paper addresses this issue by presenting a hybrid feedforward-feedback nonlinear model predictive controller for continuous material processing operations. The adaptive feedback control strategy of the controller augments the standard feedforward control to ensure improved robustness and compensation for environmental disturbances and/or parameter uncertainties. Thus, the controller can reduce the need for manual adjustments. The controller applies nonlinear generalized predictive control to generate an adaptive control signal for attaining robust performance. A wavelet-based neural network model is adopted as the prediction model with high prediction precision and time-frequency localization characteristics. Online training is utilized to predict uncertain system dynamics by tuning the wavelet neural network parameters and the controller parameters adaptively. The performance of the controller algorithm is verified by both simulation, and in a real-time practical application involving a single-input single-output double-zone sliver drafting system used in textiles manufacturing. Both the simulation and practical results demonstrate an excellent control performance in terms of the mean thickness and coefficient of variation of output slivers, which verifies the effectiveness of this approach in improving the long-term uniformity of slivers. [ABSTRACT FROM AUTHOR]

The Vienna rectifier with unbalanced input voltage and load transient is analyzed. A nonlinear control strategy for Vienna rectifier under unbalanced input is proposed. From the view of positive and negative sequence components, the proposed nonlinear control strategy suppresses the twice frequency ripple and guarantees the dynamic response characteristic at the same time. Thanks to the proposed nonlinear control strategy, the DC bus capacitor can be reduced a lot since the voltage ripple and drop can be suppressed. A 10 kW Vienna rectifier is built to verify the proposed control strategy. After applying the proposed nonlinear control strategy, the voltage ripple is only 7 V and decreases over 75% over the traditional PI control when the unbalanced degree is 20%. The voltage drop can be reduced about 80% than former control strategy which is helpful to reduce the DC bus capacitor and achieve higher power density. The volume of the capacitor can be reduced by 83.3% with the new control method. [ABSTRACT FROM AUTHOR]

In order to solve the nonlinear uncertain system of ship heading control and deal with the influence of interference factors such as external environment and ship speed on ship heading control, this paper proposes a ship controller design method combining the backstepping technique and the adaptive control theory considering the robustness, at first, it describes the mathematical model for ship motion and proposes a nonlinear ship heading control system mathematical model considering the influence of external interference factors; Then, on this basis, the paper designs a ship heading adaptive nonlinear controller with robustness function and achieves the adaptive tracking of ship heading. Simulation results show that the heading control system with adaptive robustness can eliminate influence of certain interferences. The research results indicate that the study of this paper is of great significance for further promoting the development of ship heading nonlinear control theory. [ABSTRACT FROM AUTHOR]

This study introduces an improved multiple model adaptive control (MMAC) algorithm for a class of nonlinear discrete-time systems. The controller consists of a linear direct adaptive controller, a neural network-based nonlinear direct adaptive controller and a switching mechanism. The assumption of the nonlinear term is relaxed by incorporating a parameter estimator with an augmented error. The control direction of the system is not required to be known by employing a linear direct adaptive controller with the discrete Nussbaum gain and future output predictions. The stability of the closed-loop systems applying the proposed MMAC method is proved and the improved transient performance of the system is illustrated by the simulation results. [ABSTRACT FROM AUTHOR]

An iterative model predictive control (MPC) scheme for constrained nonlinear systems is presented. The idea of the method is to detour from the solution of a non‐convex optimization problem using a time‐variant linearization of the nonlinear system model that is adjusted iteratively by solving an iterative quadratic programming optimization problem at each sampling time. The main advantage is the faster resolution of the optimization problem by using quadratic programming instead of non‐convex programming and yet, properly describing the nonlinear dynamics of the process being controlled. In this article, a general framework of the method is presented together with a discussion on the conditions under which the iterations converge and on the uncertainty of its results due to the linearization used, as well as some practical considerations about its implementation. The performance of the proposed controller is illustrated via two examples. [ABSTRACT FROM AUTHOR]

Lightning channels are made of plasma. As a consequence, the driving electrical current changes the channel's resistance in a nonlinear fashion. The resistance has an intricate dependence on the history of Joule heating and various cooling processes, as well as on the various kinetic processes that dictate the population balance of electrons within the channel. Such dependence cannot be captured by an analytic function, as often attempted. In this paper, we introduce a minimal numerical model that can qualitatively capture the temporal dynamics of the key plasma properties of a lightning channel, including its electric field, temperature, plasma density, radius, and the resulting nonlinear resistance. Through a series of novel parameterizations, we introduce six zero‐dimensional equations that can capture both nonequilibrium/low‐temperature and local thermodynamic equilibrium/high‐temperature plasma regimes. In this manuscript, we go to great lengths to validate the model, showing that it can reproduce the finite time scale of streamer‐to‐leader transition, replicate the negative differential resistance behavior of steady‐state plasma arcs, and properly describe the temporal evolution of temperature in a return stroke channel. Finally, the model is applied to the simulation of optical emissions from rocket‐triggered lightning strikes, explaining the measured delay between the rise of current and visible light, as well as reproducing the direct relationship between peak current and peak radiated power and between charge transferred to ground and total radiated energy. Plain Language Summary: A number of unsolved puzzles in lightning physics are rooted in the plasma nature of lightning channels. One such example is why do negative cloud‐to‐ground lightning flashes transfer charge to ground in a series of multiple strikes that reuse the same channel, while positive flashes mostly have a single stroke? In this paper we introduce a computer simulation tool to capture the plasma nature of lightning and model its properties. One of the key results presented here is the model's ability to explain the experimentally derived relationship between optical and electrical properties in triggered lightning flashes. Key Points: A novel physics‐based yet computationally efficient model of the lightning channel nonlinear resistance is introducedThe model reproduces the finite time scale of streamer‐to‐leader transition and the steady‐state negative differential resistanceThe model captures the experimentally inferred direct relationship between electrical and optical properties of rocket‐triggered lightning [ABSTRACT FROM AUTHOR]

Featured Application: This work deals with the dynamic response calculation and energy dissipation analysis of a linear elastic single-degree-of-freedom (SDOF) system with a supplemental nonlinear eddy current damper (ECD) subjected to base excitation, in preparation for optimum design and performance evaluation for structural vibration control with ECDs. The nonlinear model and energy dissipation of a rotary axial eddy current damper (ECD) and the dynamic responses to harmonic and seismic base excitations of a linear elastic SDOF system with the nonlinear ECD (SDOF-ECD) are investigated. Firstly, the nonlinear force-velocity relationship of the ECD is studied using finite element simulation, experimental testing and mathematical model fitting. Secondly, the energy dissipated by the nonlinear ECD under a cycle of harmonic motion is derived analytically and its optimal critical velocity is determined such that the energy dissipation is maximized. Finally, the responses of the SDOF-ECDs subjected to harmonic and seismic base excitations are calculated using numerical algorithm, where the displacement and acceleration control performance and the energy dissipation capacity of the ECD are compared with those of the conventional fluid viscous dampers (FVDs). The results indicate that the seismic control performance of ECDs outperforms that of FVDs in most cases and it is anticipated that the ECDs can be used as good alternative devices to conventional FVDs for seismic control applications. [ABSTRACT FROM AUTHOR]

This paper investigates a novel offset-free control scheme based on a multiple model predictive controller (MMPC) and an adaptive integral action controller for nonlinear processes. Firstly, the multiple model description captures the essence of the nonlinear process, while keeping the MPC optimization linear. Multiple models also enable the controller to deal with the uncertainty associated with changing setpoint. Then, a min–max approach is utilized to counter the effect of parametric uncertainty between the linear models and the nonlinear process. Finally, to deal with other uncertainties, such as input and output disturbances, an adaptive integral action controller is run in parallel to the MMPC. Thus creating a novel offset-free approach for nonlinear systems that is more easily tuned than observer-based MPC. Simulation results for a pH-controller, which acts as an example of a nonlinear process, are presented to demonstrate the usefulness of the technique compared to using an observer-based MPC. • Combining model predictive control with classical integral controller. • Achieves offset-free control without using observer or modeling of uncertainty. • Nonlinear control of a pH system. • Outperforms classical observer based techniques for test system. [ABSTRACT FROM AUTHOR]

The paper aims to approach a design method for transforming a non-linear system in order to find a state-feedback controller form for it. The method is based on a recursive algorithm and takes into account the controllability matrix of the initial system. The efficiency of the recursive algorithm compared to classical feedback linearization method is tested on the 2d mixing flow dynamical system, in a perturbed form with a logistic-type term. It is outlined both the shorter time for the calculus, and the final form less complex for the global non-linear controller associated to the model. [ABSTRACT FROM AUTHOR]

A new nonlinear decoupling control method without state feedback based on a generalized inverse system of a least squares support vector machine (LS‐SVM) is proposed for a multiple‐input multiple‐output nonlinear discrete system. According to the mathematical model of multivariable nonlinear discrete systems, the reversibility of the LS‐SVM generalized inverse system is analyzed and verified. Using the ability of LS‐SVM to approximate nonlinear functions, the multivariable nonlinear generalized inverse model of stateless feedback can be obtained offline. The generalized inverse system obtained by LS‐SVM identification is connected before the original nonlinear system to decouple the multivariable nonlinear discrete systems into q independent single‐input single‐output pseudolinear subsystems. The theoretical derivation and simulation results show that the presented LS‐SVM generalized inverse decoupling control method can realize the dynamic decoupling of multivariable nonlinear systems. And the pseudolinear composite systems show good dynamic performance. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Nonlinear mixed-effects (NLME) models have become popular in various disciplines over the past several decades. However, the existing methods for parameter estimation implemented in standard statistical packages such as SAS and R/S-Plus are generally limited to single- or multi-level NLME models that only allow nested random effects and are unable to cope with crossed random effects within the framework of NLME modeling. In this study, we propose a general formulation of NLME models that can accommodate both nested and crossed random effects, and then develop a computational algorithm for parameter estimation based on normal assumptions. The maximum likelihood estimation is carried out using the first-order conditional expansion (FOCE) for NLME model linearization and sequential quadratic programming (SQP) for computational optimization while ensuring positive-definiteness of the estimated variance-covariance matrices of both random effects and error terms. The FOCE-SQP algorithm is evaluated using the height and diameter data measured on trees from Korean larch (L. olgensis var. Changpaiensis) experimental plots as well as simulation studies. We show that the FOCE-SQP method converges fast with high accuracy. Applications of the general formulation of NLME models are illustrated with an analysis of the Korean larch data. [ABSTRACT FROM AUTHOR]

Abstract The Karush–Kuhn–Tucker conditions for a given nonlinear programming problem are generated as the transversality conditions of an optimal control problem. The directional derivatives of the objective- and constraint-functions supply the vector fields for the optimal control problem with the search vector as the control variable. Zero-Hamiltonian trajectories along the steepest descent of control Lyapunov functions provide optimal optimization algorithms. The optimality of the algorithm also depends upon the choice of a metric for the finite dimensional control space. Many well-known algorithms – such as Newton's method, the first-order Lagrangian method, the steepest descent method and Richardson's method, to name a few – are derived by minimizing the Lie derivative of a quadratic control Lyapunov function. Merit functions in optimization may also be generated using the concept of a control Lyapunov function. These results suggest that optimal control principles hold the potential for a unified theory for optimization. [ABSTRACT FROM AUTHOR]

Copyright of Ciencia e Ingenieria Neogranadina is the property of Ciencia e Ingenieria Neogranadina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this research, an adaptive fuzzy-neural fractional-order current controller using a terminal sliding controller is developed to track ideal current of an active power filter (APF) with limited time control performance. First, an adaptive fractional-order finite-time controller using terminal sliding strategy is put forward to realize high precision and finite-time control properties with guaranteed stable sliding surface. Then a fuzzy-neural estimator is proposed to estimate the unknown APF system nonlinearities. Numerical analysis is provided to prove the validity of the proposed adaptive fuzzy-neural fractional-order terminal sliding controller to track the ideal current and suppress the harmonic distortion. [ABSTRACT FROM AUTHOR]

Dissolved oxygen (DO) concentration is a key variable in the operation of wastewater treatment processes (WWTPs). In this paper, an adaptive fuzzy neural network-based model predictive control (AFNN-MPC) is proposed for the control problem of DO concentration. The main contributions of AFNN-MPC are threefolds: First, an AFNN, based on a novel learning method with adaptive learning rate, is designed to model the unknown nonlinearities of WWTPs with high predicting performance. Second, a gradient method is used to solve the optimal control problem of AFNN-MPC to reduce the computational cost. Third, the convergence of AFNN, as well as the stability analysis of AFNN-MPC, has been given in detail. Finally, the proposed AFNN-MPC is applied to the benchmark simulation model No. 2. The promising results indicate that the proposed AFNN-MPC is a suitable solution to control DO concentration. Moreover, the comprehensive experiments clearly show the superiority and efficacy of the proposed AFNN-MPC. [ABSTRACT FROM AUTHOR]

We consider inverse optimal control for linearizable nonlinear systems with input delays based on predictor control. Under a continuously reversible change of variable, a nonlinear system is transferred to a linear system. A predictor control law is designed such that the closed-loop system is asymptotically stable. We show that the basic predictor control is inverse optimal with respect to a differential game. A mechanical system is provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a near optimal adaptive event-based sampling scheme for tracking control of an affine nonlinear continuous-time system. A zero-sum game approach is proposed by introducing a novel performance index. The optimal value function, i.e., the solution to the associatedHamilton–Jacobi–Issac (HJI) equation is approximated using a functional link neural network (FLNN) with event-based aperiodic state feedback information as inputs. The saddle point approximated optimal solution is employed to design the near optimal event-based control policy and the sampling condition. An impulsive weight update scheme is designed to guarantee local ultimate boundedness of the closed-loop parameters, which is analyzed via extension of Lyapunov stability theory for the impulsive hybrid dynamical systems. Zeno-freeness of the event-sampling scheme is enforced and its effect on stability is analyzed. Finally, numerical simulation results are included to corroborate the analytical design, which shows a 48.82% reduction of feedback communication and computational load. [ABSTRACT FROM AUTHOR]

This paper presents robust nonlinear control of a variable-pitch quadrotor with the flip maneuver. Backstepping approach is chosen for nonlinear control design. A control allocation loop dynamically computes the blade pitch angle of each rotor. A systematic method to select controller gains is presented that ensures closed-loop stability. Detailed analysis of the flip maneuver in the presence of input saturation is presented for the first time. Performance of the proposed control law is first verified through simulation. This is then implemented on a PixHawk open source autopilot board and flight tests are performed on an off-the-shelf variable-pitch quadrotor frame. [ABSTRACT FROM AUTHOR]

Highlights • A P2 hybrid powertrain model is built to support developing control strategy. • A multiphase control strategy is developed for different engine-start situations. • A nonlinear feedforward-feedback controller is designed for trajectory tracking. • Simulations and experiments have verified the developed control strategy. Abstract Instead of using conventional starter, engine can be started through slipping of the disconnect clutch in P2 hybrid automatic transmission. However, both riding comfort and engine-start time are challenges for control of the starting process. In order to achieve smooth and quick engine start, this paper proposed an optimized control strategy with respect to different engine-start situations. Firstly, a model of P2 hybrid powertrain is developed to characterize the transient behavior during engine start and provide support for developing control strategies. A multiphase control strategy is then proposed to cope with the control objective of either good riding comfort or short starting time depending on different driving situations. A nonlinear feedforward-feedback controller based on differential flatness and proportional-integral-derivative (PID) algorithm is developed to regulate the current of direct operating solenoid valve. Thus the pressure of the disconnect clutch can be fine-tuned and good tracking capability for the reference trajectory of engine speed can be realized. Finally, simulation and test results demonstrate that the proposed control strategy and controller can satisfy different requirements for engine start. Both riding comfort and engine-start time of a P2 hybrid vehicle can be effectively improved in the engine starting process. [ABSTRACT FROM AUTHOR]

In this paper, an active disturbance rejection control scheme with a nonlinear sampled-data extended state observer (NSESO) is proposed for a pneumatic muscle actuator (PMA) system. The phenomenon of the actuator saturation is taken into account in the design procedure. The NSESO is utilized to estimate the so-called total disturbance that reflects the aggregated impacts of unmodeled nonlinearities and disturbances. Then, an NSESO-based composite control strategy is designed, where the estimates provided by the designed NSESO serve as the compensation to eliminate the total disturbance. Finally, a number of practical experiments on the PMA systems are conducted to verify the validity and applicability of the proposed design method. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive backstepping controller for a mechanism of pneumatic muscle actuators via an adaptive extended state observer. A dynamic model of the mechanism is established with two unknown parameters estimated by using adaptive laws. An adaptive extended state observer is established to estimate total disturbances and states of the mechanism. Moreover, adaptive extended state observer gains are obtained by adaptive laws and parallel D-eigenvalues, whose time-varying multiplier $n$ th-order derivatives are derived by tracking differentiators. Finally, a nonlinear adaptive backstepping controller is designed and the effectiveness of the proposed method is expressed by experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic formulation of inverse optimal control (IOC) law based on a rather straightforward reduction of control Lyapunov function (CLF), applicable to a class of second-order nonlinear systems affine in the input. This method exploits the additional design degrees of freedom resulting from the non-uniqueness of the state dependent coefficient (SDC) formulation, which is widely used in pseudo-linear control techniques. The applicability of the proposed approach necessitates an apparently effortless SDC formulation satisfying an SDC matrix criterion in terms of the structure and characteristics of the state matrix, A(x). Subsequently, a sufficient condition for the global asymptotic stability (g.a.s) of the closed-loop system is established. The SDC formulations conforming to the sufficient condition ensure the existence and determination of a smooth radially unbounded polynomial CLF of the form V(x)=xTP(x)x, while offering a benevolent choice for the gain matrix P(x), in the CLF. The direct relationship between the gain matrix P(x) and state weighing matrix Q(x) ensures optimization of an equivalent s(x)=xTQ(x)x. This feature enables one to rightfully choose the gain matrix P(x) as per the performance requisites of the system. Finally, the application of the proposed methodology for the speed control of a permanent magnet synchronous motor validates the efficacy and design flexibility of the methodology. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the adaptive control problem for a class of switched nontriangular nonlinear systems is investigated, which allows the control gains to vanish at some points. Neither the triangular structure nor the solvability of the adaptive control problem is required for each subsystem. A key point of this work is to solve the adaptive control problem of switched nontriangular nonlinear systems with vanishing control gains by the dual design of the controllers and switching signals. To this end, firstly, a hysteresis‐type control gains‐dependent switching law is designed, which makes the control gain of the active subsystem not vanish and thus the associated designed control input can enter into the active subsystem. Moreover, the designed switching law guarantees a dwell time between any adjacent switching instants, which rules out the Zeno behavior. Secondly, when the adaptive control problem of each subsystem is unsolvable, a sufficient condition ensuring the solvability of the adaptive control problem of switched nontriangular nonlinear systems is developed even if no control input can enter into subsystems at some points. Finally, the effectiveness of the proposed result is illustrated by two examples. [ABSTRACT FROM AUTHOR]

We are concerned with the vortex sheet solutions for the inviscid two-phase flow in two dimensions. In particular, the nonlinear stability and existence of compressible vortex sheet solutions under small perturbations are established by using a modification of the Nash-Moser iteration technique, where a priori estimates for the linearized equations have a loss of derivatives. Due to the jump of the normal derivatives of densities of liquid and gas, we obtain the normal estimates in the anisotropic Sobolev space, instead of the usual Sobolev space. New ideas and techniques are developed to close the energy estimates and derive the tame estimates for the two-phase flows. [ABSTRACT FROM AUTHOR]

This paper concerns the following nonlinear Choquard equation:−ε2Δw + V(x)w = ε−θW(x) (Iθ ∗ (W|w|p))|w|p−2w, x ∈ RN, (∗) where ε > 0, N > 2, Iθ is the Riesz potential with order θ ∈ (0,N), p ∈ [2, N+θ/N−2), min V > 0 and infW > 0. Under proper assumptions, we explore the existence, concentration, convergence and decay estimate of semiclassical solutions for (∗). The multiplicity of solutions is established via pseudo-index theory. The existence of sign-changing solutions is achieved by minimizing the energy on Nehari nodal set. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a simultaneous local stabilisation and fault detection (FD) problem for Takagi-Sugeno (T-S) fuzzy systems is investigated. The authors' attention is focused on synthesising an FD observer and an observer-based controller to achieve the objectives of local stabilisation and FD simultaneously. First, by establishing the relationship among the reachable set, the ellipsoid set and the objective area, a new local stabilisation criterion is proposed. The presented criterion guarantees that the state trajectory starting from a given local region can remain in this region even when the system is subject to disturbances and faults. Meanwhile, the FD observer is designed under a mixed L∞/H_ framework such that the residual signal is robust to the persistent disturbance and sensitive to the fault. Different from some existing methods, via the proposed scheme, the state can be restricted into the modelling area so that the non-linear control problem can be addressed by the T-S fuzzy model approach effectively. Additionally, two different methods are utilised in the co-design of the observer and the controller such that the disturbance is not required to be energy-bounded. Finally, the effectiveness of the proposed approach is demonstrated by an example. [ABSTRACT FROM AUTHOR]

Abstract The oil well drilling is a complex process, requiring that the bottomhole pressure be maintained within an operational window, limited by fracture pressure and pore pressure (and/or wellbore collapse). During the drilling process, there are several phenomena that directly influence the annulus bottomhole pressure: kick, lost circulation, inefficient removal of solids, well length increase, reservoir parameters and pipe connection procedure (surge and swab effects). The main objective of this work is to develop neural network controllers able to regulate the annulus bottomhole pressure under pipe connection procedure, kick and lost circulation disturbances. For this purpose, an experimental drilling unit was built, depicting the oil well drilling process primordial characteristics. Besides, real oil well offshore data were employed for validating the neural network controllers. Neural networks were built for predicting the direct plant dynamics (inverse neural network controller) and the inverse plant dynamics (direct neural network controller). It is noteworthy to mention that direct plant dynamics neural modelling was employed as the nonlinear model of a gain scheduling control approach; in addition, the inverse plant dynamics neural modelling constituted a nonlinear controller, directly predicting the manipulated variable. Both controllers employed real-time neural network training, being, therefore, adaptive schemes for regulating the oil well drilling process, which nature is inherently transient. Highlights • Experimental facility and offshore oil well data were employed for neural network controller synthesis. • Pipe connection procedure, kick, lost circulation problems and inside operational window scenarios were analysed. • Off-line and on-line (stochastic learning) data were implemented for neural network training. • Neural networks were built for predicting the direct (inverse controller) and the inverse plant dynamis (direct controller). [ABSTRACT FROM AUTHOR]

This paper presents a L1 adaptive controller augmenting a dynamic inversion controller for UAV (unmanned aerial vehicle) carrier landing. A three axis and a power compensator NDI (nonlinear dynamic inversion) controller serves as the baseline controller for this architecture. The inner-loop command inputs are roll-rate, pitch-rate, yaw-rate, and thrust commands. The outer-loop command inputs come from the guidance law to correct the glide slope. However, imperfect model inversion and nonaccurate aerodynamic data may cause degradation of performance and may lead to the failure of the carrier landing. The L1 adaptive controller is designed as augmentation controller to account for matched and unmatched system uncertainties. The performance of the controller is examined through a Monte Carlo simulation which shows the effectiveness of the developed L1 adaptive control scheme based on nonlinear dynamic inversion. [ABSTRACT FROM AUTHOR]