Search

In this paper, we are concerned with dynamic stabilisation for a one-dimensional Euler–Bernoulli beam equation with boundary moment control and matched nonlinear uncertain disturbance. In the case of no disturbance, we show that a boundary feedback control law exponentially stabilises the system and Riesz basis generation holds for the closed-loop system. The well-posedness of the system in the sense of Salamon-Weiss, which is essentially important for the design of observer, is verified. We design an infinite-dimensional disturbance estimator, which doesn't need slow variation or high gain or boundedness of the derivation of the disturbance, to estimate the total disturbance. Based on the disturbance estimator, we design an output feedback control law. The Riesz basis generation and exponential stability of a couple system including the original equation is proved. Moreover, the boundedness of the closed-loop system is verified. Some numerical simulations are presented to illustrate the results. [ABSTRACT FROM AUTHOR]

In this paper, a particle filter design scheme for a robust nonlinear control system of uncertain heat exchange process against noise and communication time delay is presented. The particle filter employs a cluster of particles and associated weights to approximate the posterior distribution of states and is capable of handling nonlinear and non-Gaussian issues. However, when the realistic given noise is much larger than that of the one modeled by the particle filter, the estimated posterior distribution is no longer reliable. Considering that, the exponential weights take the place of the original absolute particle weights in this paper, which act as an adjustment to the particle filter weights for a better state estimation. This adjustment for the weight of the particle filter takes into account the practical significance and can ensure the stability, tracking performance, and continuous operation of the control process incorporated with the particle filter. The simulation verifies the feasibility and usefulness of the method. [ABSTRACT FROM AUTHOR]

In this paper, the problem of controlling PWM single-phase AC/DC converters is addressed. The control objectives are twofold: (i) regulating the output voltage to a selected reference value, and (ii) ensuring a unitary power factor by forcing the grid current to be in phase with the grid voltage. To achieve these objectives, the singular perturbation technique is used to prove that the power factor correction can be done in the open-loop system with respect to certain conditions that are not likely to take place in reality. It is also applied to fulfill the control objectives in the closed-loop through a cascade nonlinear controller based on the three-time scale singular perturbation theory. Additionally, this study develops a rigorous and complete formal stability analysis, based on multi-time-scale singular perturbation and averaging theory, to examine the performance of the proposed controller. The theoretical results have been validated by numerical simulation in MATLAB/Simulink/SimPowerSystems environment. [ABSTRACT FROM AUTHOR]

In this paper, the trajectory tracking problem of industrial manipulator with fixed periodic external force on human-robot cooperative assembly line is studied. An advanced fuzzy adaptive supertwisting sliding mode control (FASTSMC) algorithm is proposed to realize the rapid convergence and continuous control of nonlinear control system. In order to enhance the robustness of the system, an arctangent terminal sliding mode surface is designed to deal with the concentrated uncertain disturbance. The supertwisting method is used to overcome the chattering problem in the control law. The fuzzy adaptive technique is used to compensate the centralized disturbance with unknown upper bound. A stiffness identification model is designed to estimate the deviation of the manipulator under external force. Finally, the feasibility of the proposed control scheme is verified by a simulation example of a 4-DOF manipulator. [ABSTRACT FROM AUTHOR]

This article proposes a distance-based formation control and target tracking for multiagent systems, where agents are modeled using second-order nonlinear systems in the presence of disturbance. By applying a rigid graph theory, we developed a neural network (NN)-based backstepping controller to address the distance-based formation control problem of nonlinear multiagent systems. To compensate for the unknown nonlinearity in the system dynamics, the radial basis function NN was used where the NN tuning law was derived based on Lyapunov stability theory. We rigorously proved the uniform ultimate boundedness of the formation distance error and NN weights’ norm estimation error. Finally, using simulation results, we demonstrated the proposed method’s performance on the second-order, nonlinear multiagent systems. To provide further evaluation, we compared the proposed distance-based method and existing displacement-based methods. [ABSTRACT FROM AUTHOR]

In this paper, a mixed event-triggered and continuous-time control method is proposed, which can guarantee finite-time stabilisation of the fixed point in a class of time-varying nonlinear systems. Benefiting from an event-triggered framework, which is constructed based on the indefinite Lyapunov theory, the communication/computation costs in the transient time can be reduced by using the proposed method. In a special case, this method is converted to a fully event-triggered control strategy for asymptotic stabilisation of the fixed point in the considered class of time-varying nonlinear systems. The effectiveness of the proposed method is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, the problem of asynchronous sliding mode control (SMC) is addressed for discrete-time singular Markovian jump systems (DSMJSs) with external disturbances and randomly occurring nonlinearities. Moreover, the states of DSMJSs are unavailable for measurement, and the synchronisation between the controller and the system modes is not surely ensured. Our attention is mainly focused on solving the control problem by proposing an observer-based adaptive sliding mode controller for such a class of complex systems. First, a non-fragile observer is designed not only to estimate the system states but also to relax the multiplicative gain variations and to construct an appropriate mode-dependent sliding mode surface function. Then, by assuming that the bounds of nonlinear terms are unknown, an adaptive SMC control law is synthesised to drive the system trajectories onto the specified sliding surface and completely compensate for the influences of the external disturbances and fluctuations of the controller and observer gains. Sufficient conditions are established to ensure that the closed-loop system is stochastically admissible with a γ level of the mixed H ∞ /passive performance, and to provide the observer and controller gain matrices. Finally, to numerically substantiate the efficacy of the proposed control scheme, two examples are given. [ABSTRACT FROM AUTHOR]

Summary: This paper reports the first application of nonlinear substructuring control (NLSC) to shake table experiments in which the table dynamics is significantly affected by the specimen. Substructuring experiments using a shake table require simultaneous control of acceleration and displacement of the table because it functions as both an actuation system and a part of the substructure in the experiments. NLSC was developed as an enhanced version of linear substructuring control (LSC), the basic method for the control of dynamically substructured system. This paper first describes the formulation of substructures for shake table experiments using a multiple‐degree‐of‐freedom structure and NLSC design for the substructures. The stability analysis of NLSC, which has three controllers, becomes much more complicated, more frequently showing conditional stability than is observed in conventional experiments. To assess various conditionally stable systems, this paper proposes an equivalent approach that can systematically count encirclements of the critical point in Nyquist plots. In the stability analysis using the proposed approach, NLSC is more robust against parameter variations in the substructures than LSC. In addition, the error feedback controller in both LSC and NLSC enhances the robustness against the variations. This study numerically and experimentally examined control performances for shake table substructuring experiments involving nonlinear characteristics. In the examinations, NLSC achieved the expected displacement and acceleration control with stability and reasonable accuracy, whereas LSC could not. The error feedback controller design of NLSC was found to be essential for performing shake table substructuring experiments with severe nonlinear characteristics. [ABSTRACT FROM AUTHOR]

Summary: An innovative nonlinear optimal control scheme based on θ‐D approximation for structural vibration control with R‐AMD (active mass damper with rotating actuator) is developed in this paper. In the R‐AMD which was proposed in our previous work, rotating actuator rather than linear actuator is used to accelerate and decelerate the inertial mass, thus avoiding stroke‐related problems. The rotational motion of the inertial mass makes R‐AMD a special nonlinear AMD, which raises the level of difficulty in controller designing. Taking the control–structure interaction (CSI) effect into consideration, the modeling of target structure/R‐AMD coupled system is fulfilled using the Lagrange equations. Regarding the nonlinearity in the R‐AMD system, a nonlinear optimal control scheme is designed based on θ‐D approximation, during which the important role of diffeomorphism transformation of coordinates is presented. Experiments are conducted to validate the effectiveness of the proposed control algorithm and the R‐AMD device. [ABSTRACT FROM AUTHOR]

A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is nonmodel-based and has a simplified fuzzy neural network structure and adapts with a novel bidirectional brain emotional learning algorithm. It is applied to control all six degrees-of-freedom of a QUAV for accurate trajectory tracking and to handle the payload uncertainties and disturbances in real-time. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on a QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications. [ABSTRACT FROM AUTHOR]

Aiming at reducing the influence caused by data injection attacks, an asynchronous event‐triggered robust H‐infinity control (AERHC) is proposed for the stability of nonlinear ICPSs suffering from data injection attacks. A robust H‐infinity memory dynamic feedback control technique is designed to guarantee asymptotic stability of the closed‐loop system under a Lyapunov functional and the robust H∞ stability criterion. Furthermore, a memory dynamic feedback controller which has five controller gains different from each other for mitigating the conservative of nonlinear ICPSs and for resisting data injection attacks in ICPSs. Meanwhile, an asynchronous event‐triggered strategy is adopted to reduce the data communication frequency among nonlinear ICPSs components, and it effectively handle the influence of coupling problem between the measurement channel and the control channel. Finally, simulation tests are conducted by using MATLAB to control the robustness of the motor angle position of electromechanical system by using AERHC, which verify that the correctness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a new active controller technique effect of functionally graded (FG) cylindrical shell structure model within joint harmonic and parametric excitations. The new active controller involves Nonlinear Proportional-Derivative (NPD) plus Negative Cubic Velocity Feedback (NCVF) as a new nonlinear control method (NPDCVF). The motivation of this study is the well-known observation that NPDCVF control method can offer a means to improve the performance of plant systems. The coupled nonlinear differential equations with two modes have been derived applying the von Kármán nonlinear theory, Galerkin's process, and the static condensation technique. Static condensation is the process of decreasing the number of free displacements or degrees of freedom. We have added other three different controller methods over the structure to select the best controller. The three applied controller methods to the considered structure are: Integral Resonant Control (IRC), Positive Position Feedback (PPF), and Nonlinear Integral Positive Position Feedback (NIPPF) which are applied to the considered structure. We establish that the best one for dipping the high vibration amplitudes is the NPDCVF as a new controller. The perturbation technique is useful to solve the present model including quadratic and cubic nonlinearities subjected to mixed harmonic and parametric forces within the simultaneous primary resonance case (Ω = ω 1 , Ω = ω 2 ) as the worst resonance case of the system. All numerical outcomes have been completed with the help of MATLAB Ra 18.0 program software over the investigated model. The obtained results have proved that this new controller is excellent for improving the structure by reducing the risky vibrations caused during primary resonances. Frequency response equations have helped in observing the stability examination of the obtained numerical solutions. The actions of several factors performed on the controlled model have been examined and described. Some comparisons have also been prepared with the available recent published papers of a similar model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department 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.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering 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.)

We propose a neural fuzzy inference network (NFIN) based on a symbiotic Taguchi-based modified differential evolution (STMDE) algorithm for solving nonlinear control problems. The proposed STMDE algorithm not only uses the Taguchi method in its search for the best individual but also employs adjustable parameter control to tune the scaling factor, which can prevent a solution from being trapped at local optima and reinforce the search ability. Moreover, symbiotic evolution (SE) is applied to improve the structure of individual compositions. Unlike the traditional differential evolution (DE) algorithm, SE regards each individual in a population as being the partial solution to a problem instead of the full solution. Compared with traditional DE, the proposed STMDE algorithm reduces the error by 7.95, 4.51, 5.22, and 51.34% in terms of regulation performance, noise rejection ability, robustness to parameter variation of the controlled system, and controller tracking capability, respectively. In addition, our experimental results also indicate that the proposed STMDE algorithm exhibits superior performance to other algorithms used for solving nonlinear temperature-sensing control problems. [ABSTRACT FROM AUTHOR]

This article investigates the fixed‐time stabilization problem for a class of high‐order nonlinear systems with monotone degrees and output constraints. Firstly, a new fixed‐time stability criterion is established, which is less conservative in the explicit and delicate selection of Lyapunov functions. Then, by choosing a novel tan‐type barrier Lyapunov function (BLF) and using the generalized adding a power integrator (AAPI) technique, a fixed‐time stabilization controller is explicitly constructed while achieving the requirement of state constraints. The novelty of the proposed control strategy can tackle simultaneously the fixed‐time stabilization of the involved systems with and without output constraints, without changing the controller structure. A simulation example is given to show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the system can be expressed in the linear span of a set of known basis functions. While this assumption leads to efficient algorithms, it limits applications to very specific classes of systems. We introduce a novel nonparametric adaptive algorithm that estimates an infinite-dimensional density over parameters online to learn an unknown dynamics in a reproducing kernel Hilbert space. Surprisingly, the resulting control input admits an analytical expression that enables its implementation despite its underlying infinite-dimensional structure. While this adaptive input is rich and expressive - subsuming, for example, traditional linear parameterizations - its computational complexity grows linearly with time, making it comparatively more expensive than its parametric counterparts. Leveraging the theory of random Fourier features, we provide an efficient randomized implementation that recovers the complexity of classical parametric methods while provably retaining the expressivity of the nonparametric input. In particular, our explicit bounds only depend polynomially on the underlying parameters of the system, allowing our proposed algorithms to efficiently scale to high-dimensional systems. As an illustration of the method, we demonstrate the ability of the randomized approximation algorithm to learn a predictive model of a 60-dimensional system consisting of ten point masses interacting through Newtonian gravitation. By reinterpretation as a gradient ow on a specific loss, we conclude with a natural extension of our kernel-based adaptive algorithms to deep neural networks. We show empirically that the extra expressivity afforded by deep representations can lead to improved performance at the expense of the closed-loop stability that is rigorously guaranteed and consistently observed for kernel machines. [ABSTRACT FROM AUTHOR]

We investigate asymptotically stabilisation for the cascaded system of a nonlinear ODE and a wave PDE with damping terms by a predictor control. Based on the backstepping transformations, the cascaded system is transferred to a target system. Further, a predictor control is designed. It is proved that the target system is asymptotically stable by using a Lyapunov functional. Further the stability of the original system under the proposed control law is equivalent to that of the target system. An example is given to illustrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the distributed finite‐time control problem for the class of systems interconnected over an undirected graph is considered. First, the concepts of well‐posedness, finite‐time boundedness, and contractiveness for the considered systems are given. Then, a sufficient condition is developed to ensure that the plant is well‐posed, finite‐time bounded, and contractive, where the results are stated by terms of linear matrix inequalities (LMIs). For the control synthesis, we construct a distributed dynamic output‐feedback controller inheriting the structure of the plant such that the resulting closed‐loop system is finite‐time bounded and contractive. Since the analysis condition of the plant used for the closed‐loop system is presented by some nonlinear inequalities in given finite‐time interval, we further derive a sufficient condition in terms of LMIs with some fixed parameter for the existence of an appropriate output‐feedback controller such that the closed‐loop system is contractive. Finally, numerical simulations are provided to show the validity of the proposed results. [ABSTRACT FROM AUTHOR]

This paper presents a robust hybrid fault‐tolerant optimal predictive control (HFTPC), for an industrial robot arm under hybrid (electric and pneumatic) actuator faults and/or varying time‐delays. Based on the error dynamics, estimated states, and a predictive controller, a new state feedback control law is proposed and implemented for the reformulation of the optimal control problem of a nonlinear faulty hybrid actuator system based on predictive control via linear matrix inequalities (LMIs). First, a robust MPC scheme is performed in which the future control sequence is used to compensate the varying time‐delays. Then, a robust stable hybrid fault tolerant predivtive control is implemented to handle actuators faults to effect robust trajectory tracking. In fact, the stability of hybrid systems based on the proposed control scheme is a very sensitive criterion. Therefore, stability conditions are required for controlling the industrial arm under faulty hybrid (electric and pneumatic) actuator, based on the Lyapunov‐Krasovskii (L‐K) theory, less conservative stable conditions in terms of LMIs are given and used to ensure the asymptotically robust stability of closed‐loop constrained system that dependent delay‐range. To highlight the robustness and effectiveness of the proposed approach, a simulation study of an industrial robot arm example is proved, where the results showed the prompt and the accuracy of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, a literature review on two-wheeled vehicle systems is methodically performed and presented. For this purpose, the principal aspects concerning the kinematic, dynamic, control, and identification features of articulated mechanical systems described within the multibody formulation approach are emphasized in this review article. First, the scientific investigations on two-wheeled vehicle modelling are chronologically described employing a historical literature review approach. This is done to set a consistent context for the subsequent developments analyzed in the paper. Then, following the systematic literature review methodology described in this work, a rich corpus of relevant documents in the time span between 2013-present is analyzed. Moreover, bibliometric methods are used to construct the conceptual structure map of the research field, which also allowed for formulating a thematic classification. Thus, considering the full-texts of the identified corpus of documents, this work presents a synthetic analysis of the fundamental issues about the multibody approaches for modelling two-wheeled vehicles. Finally, future research perspectives are pointed out in this article. [ABSTRACT FROM AUTHOR]

In this paper, we provide a complete framework for the design of genetically evolved cognitive tracking controller based on interval type‐2 (IT2) fuzzy cognitive map (FCM). We construct the cognitive controller based on a nonlinear controller by transforming its representation into a FCM. This representation gives the opportunity to prove the stability of the cognitive controller in the framework of nonlinear control theory. Moreover, with the deployment of IT2‐fuzzy sets which are known to be capable to handle high level of uncertainty, the proposed cognitive controller has the ability to deal with uncertainty that are encountered in real‐time world applications. To accomplish the design of the cognitive controller, we present a systematic approach based on genetic algorithm to optimize its parameters and learn fuzzy rules by extracting them from model space (e.g., a set of rules). Within the paper, all steps in constructing and designing the IT2‐ FCM‐based cognitive controller are presented. We first show the performance improvements of the proposed IT2‐FCM‐based tracking controller with extensive and comparative simulation results and then with experimental results that were collected on real‐world mobile robot. The results clearly show the superiority of proposed cognitive control systems when compared to its conventional and fuzzy controller counterparts. We believe that the proposed genetically evolved design approach of the IT2‐FCM‐based cognitive controller will provide a bridge between the well‐developed cognitive sciences and control theory. [ABSTRACT FROM AUTHOR]

This article studies the performance-oriented fault detection (FD) for nonlinear control systems in the data-driven framework. Unlike the traditional residual- or test statistic-based FD methods, the proposed approach focuses on evaluating the system performance changes/degradations caused by the faults. Specifically, the regulation performance described by an infinite-time cost function is first introduced as a control performance index of the feedback system. Using only the process input and output (I/O) data, a Takagi–Sugeno fuzzy dynamic model is constructed with the aid of subspace identification method. Then, the internal system state variables are expressed in terms of the I/O data and a fuzzy cost function is proposed to approximate the performance index. On this basis, the temporal difference error induced by the Bellman equation is adopted as the process evaluation indicator, whose threshold is determined through the randomized algorithm. To demonstrate the effectiveness of the proposed FD approach, case studies are performed in the end on a ship propulsion system and a three-tank system. [ABSTRACT FROM AUTHOR]

In this paper, the synchronization between complex fractional-order chaotic systems and the integer-order hyper-chaotic system has been investigated. Due to increased complexity and the presence of additional variables, it seems to be very interesting and can be associated with real-life problems. Based on the idea of tracking control and nonlinear control, we have designed the controllers to obtain the synchronization between the chaotic systems. To establish the efficacy of the methods computations have been carried out. Excellent agreement between the analytical and computational studies has been observed. The achieved synchronization is illustrated in the field of secure communication. The results have been compared with published literature. [ABSTRACT FROM AUTHOR]

This paper focuses on the passive fault-tolerant control for an uncertain switched nonlinear random system with multiple interval time-varying delays and intermittent faults in sensors and actuators. There are also nonlinear functions, exogenous disturbances and measurement noise in the system. There are few tries to realize noise-to-state exponentially mean-square stability for switched random nonlinear systems subject to multiple interval time-varying delays allowing various time delay cases. Random differential equations are more common than stochastic differential equations in practice. Thus, the suggested method is more practicable. First, a nonlinear dynamic output feedback fault-tolerant controller is constructed. Next, an augmented closed-loop system can be obtained in the framework of multiple interval time-varying delays. Then piecewise Lyapunov function is utilized to realize stability analysis for the closed-loop system. At last, this feasibility of this approach presented in this study is verified through two examples. [ABSTRACT FROM AUTHOR]

Differential-geometric-control theory represents a mathematically elegant combination of differential geometry and control theory. Practically, it allows exploitation of nonlinear interactions between various inputs for the generation of forces in non-intuitive directions. Since its early developments in the 1970s, the geometric-control theory has not been duly exploited in the area of fluid mechanics. In this paper, we show the potential of geometric-control theory in the analysis of fluid flows, exemplifying it as a heuristic analysis tool for discovery of symmetry-breaking and unconventional force-generation mechanisms. In particular, we formulate the wing unsteady aerodynamics problem in a geometric-control framework. To achieve this goal, we develop a reduced-order model for the unsteady flow over a pitching–plunging wing that is (i) rich enough to capture the main physical aspects (e.g. nonlinearity of the flow dynamics at large angles of attack and high frequencies) and (ii) efficient and compact enough to be amenable to the analytic tools of geometric nonlinear control theory. We then combine tools from geometric-control theory and averaging to analyse the developed reduced-order dynamical model, which reveals regimes for lift and thrust enhancement mechanisms. The unsteady Reynolds-averaged Navier–Stokes equations are simulated to validate the theoretical findings and scrutinize the underlying physics behind these enhancement mechanisms. [ABSTRACT FROM AUTHOR]

This paper is concerned with the finite-time stabilization of a class of switched nonlinear singular systems under asynchronous control. Asynchronism here refers to the delays in switching between the controller and the subsystem. First, the dynamic decomposition technique is used to prove that such a switched singular system is regular and impulse-free. Secondly, based on the state solutions of the closed-loop system in the matched time period and the mismatched time period of the system instead of constructing a Lyapunov function, the sufficient conditions for the finite-time stability of the asynchronous switched singular system are given, there is no limit to the stability of subsystems. Then, the mode-dependent state feedback controller that makes the original system stable is derived in the form of strict linear matrix inequalities. Finally, numerical examples are given to verify the feasibility and validity of the results. [ABSTRACT FROM AUTHOR]

Mixed-signal analog/digital circuits emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as "neuromorphic engineering". However, analog circuits are sensitive to process-induced variation among transistors in a chip ("device mismatch"). For neuromorphic implementation of Spiking Neural Networks (SNNs), mismatch causes parameter variation between identically-configured neurons and synapses. Each chip exhibits a different distribution of neural parameters, causing deployed networks to respond differently between chips. Current solutions to mitigate mismatch based on per-chip calibration or on-chip learning entail increased design complexity, area and cost, making deployment of neuromorphic devices expensive and difficult. Here we present a supervised learning approach that produces SNNs with high robustness to mismatch and other common sources of noise. Our method trains SNNs to perform temporal classification tasks by mimicking a pre-trained dynamical system, using a local learning rule from non-linear control theory. We demonstrate our method on two tasks requiring temporal memory, and measure the robustness of our approach to several forms of noise and mismatch. We show that our approach is more robust than common alternatives for training SNNs. Our method provides robust deployment of pre-trained networks on mixed-signal neuromorphic hardware, without requiring per-device training or calibration. [ABSTRACT FROM AUTHOR]

This paper formulates and solves a new problem of global practical inverse optimal exponential path-tracking control of mobile robots driven by Lévy processes with unknown characteristics. The control design is based on a new inverse optimal control design for nonlinear systems driven by Lévy processes and ensures global practical exponential stability almost surely and in the pth moment for the path-tracking errors. Moreover, it minimizes cost function that penalizes tracking errors and control torques without having to solve a Hamilton–Jacobi–Bellman or Hamilton–Jaccobi–Isaacs equation. [ABSTRACT FROM AUTHOR]

This paper synthesises a constrained sampled-data output feedback controller for uncertain multivariable nonlinear systems in the presence of various constraints, using the filtered adaptive control architecture. Nonlinear uncertainties are estimated online by solving the error dynamics between the output predictor and the real system with the neglection of unknowns, the yielded adaptive parameters are piecewise constant. In the proposed control scheme, the nonlinear uncertainties are compensated for within the bandwidth of low-pass filters, while the trade-off between tracking and constraints violation avoidance is formulated as a numerical constrained optimisation problem which is solved periodically. Priority is given to constraints violation avoidance at the cost of deteriorated tracking performance. System transformations are performed to find the sufficient conditions that can guarantee the stability of the closed-loop system with the sampled-data controller, where both the states and control inputs are held constant over the sampling period. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimal control design framework for hybrid nonlinear dynamical systems involving an interacting combination of continuous-time and discrete-time dynamics. Two numerical algorithms are proposed to approximate the continuous-time and discrete-time portions of the hybrid Hamilton-Jacobi-Bellman (HJB) equation. Galerkin's spectral method is utilised to approximate the value function involved in the continuous-time HJB equation, thereby computing the optimal control gains between impulsive events. Employing the spectral collocation method, the discrete-time HJB equation is then approximated to find the optimal control gain vector at impulsive instants. These two algorithms are ultimately combined to obtain the desired hybrid nonlinear optimal control law. Describing practical considerations for implementing the algorithms, some illustrative examples are presented to evaluate the functionality of the proposed hybrid nonlinear optimal controller. [ABSTRACT FROM AUTHOR]

This paper addresses the leader–follower consensus control problem for a nonlinear multi-agent robot system with control input constraint and external disturbances. Robot system is one of the most important practical systems in the industry. Due to the presence of disturbances in most practical systems, this paper considers the issue of finite-time leader–follower consensus control of the nonlinear multi-agent robot system along with actuator saturation and bounded disturbance. The modified terminal sliding mode control method is suggested for the system which is able to guarantee the stability of the overall system and fast finite-time leader–follower consensus control. For two different scenarios, the simulation of multi-agent robot system has been performed. The results show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This research introduces a new online optimal control based on the input–output feedback linearization and a multi-crossover genetic algorithm for under-actuated nonlinear systems having parametric uncertainties. At first, the input–output feedback linearization method is successfully implemented to derive the control law for a two degrees of freedom cart-pole nonlinear system. Then, the regarded optimization algorithm is applied to find the design parameters of the controller for different values of the uncertain variables. Next, an approximation function is suggested to calculate the optimum gains of the controller in the presence of the uncertainties in the system parameters. The simulation results are illustrated to prove the effectiveness and adeptness of the introduced scenario to overcome some common issues in the actual systems, i.e. under-actuating nonlinearities and uncertainties. [ABSTRACT FROM AUTHOR]

In this study, a new low-speed control strategy based on active disturbance rejection control (ADRC) + fractional-order proportion integration differentiation (FOPID) is proposed to realize fast and accurate control of permanent magnet synchronous motor (PMSM) at low speed. First, a new nonlinear function with enhanced smoothness and continuity around the origin is designed. On the basis of the above nonlinear function, an improved extended state observer (ESO) is developed. Then, the parameters of ESO are adjusted online by the designed fuzzy rules. Considering the improved dynamic performance of the FOPID controller, the nonlinear state error feedback control law module of ADRC is replaced by FOPID to calculate the proportion, differential, and integral of the error signal. Finally, the simulation analysis of the new low-speed control system based on ADRC + FOPID is carried out. The results show that the proposed algorithm has faster response speed and better anti-interference ability in the low-speed control of PMSM than the traditional ADRC. [ABSTRACT FROM AUTHOR]

Research is increasingly shifting from the design and control of single robots toward collaborative methods for multirobot systems. Particularly interesting are heterogeneous systems since the combination of different capabilities can tackle tasks that are difficult, if not impossible, to perform with a single robot. In this article we propose a scheme for the control of a novel heterogeneous multirobot system designed for bricklaying where the use of a single robot is not feasible due to the weight of the payloads to be managed. The proposed solution is based on a crane which cooperates with a lightweight rigid robot. The correct cooperation between the two robotic subunits poses a series of control challenges that must be studied in the context of cooperative manipulation of an object. In this article, we first derive the mathematical model of this novel robotic system during the positioning of the block. Then a control law is proposed. The proposed law is based on an ad hoc inverse dynamic control which takes advantage of the overactuation of the resulting connected system to unload the robotic arm from the weight of the block. A physical CAD‐based simulator of the system is used to show the effectiveness of the proposed control law and to show that the proposed architecture allows to position precisely large and heavy blocks while using a relatively small rigid robotic manipulator. [ABSTRACT FROM AUTHOR]

In an empirical context, a method to use nonlinear control theory in the dynamic analysis of supply chain resilience is developed and tested. The method utilises block diagram development, transfer function formulation, describing function representation of nonlinearities and simulation. Using both ‘shock’ or step response and ‘filter’ or frequency response lenses, a system dynamics model is created to analyse the resilience performance of a distribution centre replenishment system at a large grocery retailer. Potential risks for the retailer’s resilience performance include the possibility of a mismatch between supply and demand, as well as serving the store inefficiently and causing on-shelf stock-outs. Thus, resilience is determined by investigating the dynamic behaviour of stock and shipment responses. The method allows insights into the nonlinear system control structures that would not be evident using simulation alone, including a better understanding of the influence of control parameters on dynamic behaviour, the identification of inventory offsets potentially leading to ‘drift’, the impact of nonlinearities on supply chain performance and the minimisation of simulation experiments. [ABSTRACT FROM PUBLISHER]

For the nonlinear TCP/AWM network congestion control system with external disturbances, under the framework of backstepping control method, an adaptive network congestion control strategy based on event-triggered mechanism is proposed. This strategy reduces the waste of network resources by introducing an event-triggered mechanism and uses RBF neural network to deal with external disturbances. The Lyapunov theory is used to prove that all signals in the closed-loop system are bounded. Finally, the proposed method is verified by simulation and is compared with PID, which further verifies the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

Summary: Nonlinear vibration control theory has resolved some bottleneck problems that cannot be solved by traditional linear vibration control theories. In this study, intelligent controllable magneto‐rheological dampers (MR‐Ds) and high‐static‐low‐dynamic variable stiffness vibration isolators (VS‐VIs) are innovatively applied to a traditional steel‐spring floating slab track (FST) to improve its low‐frequency vibration‐reduction effect. To determine the reasonable parameter group of the VS‐VI and the parameter matching when used with MR‐D, parameter groups were first preliminarily proposed through static and dynamic analyses of the equivalent single‐degree‐of‐freedom (SDOF) model of the FST. A vertical vehicle–variable stiffness‐magneto‐rheological FST coupled dynamic model was established, and the parameter group was optimized based on safety and vibration‐reduction analyses. The simulated results showed that the positive and negative stiffnesses were the key parameters that determined the stiffness nonlinearity level and dynamic support capacity of the VS‐VI. Improving the positive stiffness is necessary to achieve a low‐dynamic stiffness under no vehicle load and to ensure the millimeter‐level dynamic displacement of the FST under vehicle load. An appropriate stiffness nonlinearity level can achieve a better vibration‐reduction effect, and excessive nonlinearity level will not bring more significant improvements. The damping ratio in the dynamic analysis is a key parameter to prevent the jumping phenomenon, and the recommended value should not be less than 5%. Moreover, when the VS‐VI and the MR‐D were efficiently incorporated, a smaller MR damping force and larger displacement threshold could not only achieve a better vibration‐reduction effect but also reduce the energy consumption. [ABSTRACT FROM AUTHOR]

In this paper, a subdivision strategy for the adjoining cell mapping method to solve the global optimal control problems for multi‐input‐multi‐output systems is proposed. The subdivision strategy constructs the discrete optimal control table first by a rough discretization of the state space, which can identify uncontrollable regions. Then the optimal feedback control from the selected initial state is instituted. The rough cells where the feedback control trajectory crosses are subdivided into smaller cells and the finer discrete optimal control table is obtained after the second search. The further discretization of the target cell and control inputs can further enhance the terminate control precision. The calculation can also take the advantage of GPU parallel technology that will greatly enhance the computation efficiency. Two examples illustrate the feasibility and validity of the subdivision strategy to solve optimal control problems for multi‐input‐multi‐output systems. The proposed method offers an efficient alternative numerical way to obtain the approximate optimal control solution with global optimum for nonlinear multi‐input‐multi‐output systems. [ABSTRACT FROM AUTHOR]

This paper investigates the control synthesis for discrete-time semi-Markov jump systems with nonlinear input. Observer-based controllers are designed in this paper to achieve a better performance and robustness. The nonlinear input caused by actuator saturation is considered as a group of linear controllers in the convex hull. Moreover, the elapse time and mode dependent Lyapunov functions are investigated and sufficient conditions are derived to guarantee the H∞ performance index. The largest domain of attraction is estimated as the existing saturation in the system. Finally, a numerical example is utilized to verify the effectiveness and feasibility of the developed strategy. [ABSTRACT FROM AUTHOR]

This paper mainly studies the output feedback control problem of the stochastic nonlinear system based on loose growth conditions and applies the research results to the valve control system of underwater oil and gas pipelines, which can improve the speed and stability of the equipment system. First, the concept of randomness is introduced to study the actual tracking control problem of output feedback of stochastic nonlinear systems, remove the original harsher growth conditions, make it meet the more general polynomial function growth conditions, and propose a combination of static and dynamic output feedback practices. The design of the tracking controller makes all the states of the system meet boundedness and ensures that the tracking error of the system converges to a small neighborhood of zero. Second, the system is extended to the parameter-uncertain system, and the output feedback tracking controller with complete dynamic gain is constructed by proving the boundedness of the system state and gain. Further, the time-delay factor is introduced, and the nonlinear term of the system satisfies the more relaxed power growth condition, combined with the inverse method to cleverly construct a set of Lyapunov functions and obtain the output controller to ensure that the system is asymptotically probabilistic in the global scope. Stability. Finally, through the ocean library in the Simulation X simulation software, the controller design results are imported into the underwater electro-hydraulic actuator model to verify the effectiveness of the controller design. [ABSTRACT FROM AUTHOR]

In this work, a new optimal and robust controller is proposed for nonlinear systems by combining the H-infinity control approach with the model reference control technique. It is carried out with the aid of integral dynamic state feedback control to achieve an adequate transient response by incorporating a reference model. The black hole optimization is used as a novel and efficient optimization algorithm to optimize the design approach. In addition, the proposed controller gain matrix is calculated by solving the H-infinity algebraic Riccati equation. Moreover, the stability properties of the controlled system are demonstrated using Lyapunov stability analysis. Two types of nonlinear systems are used as case studies to show the effectiveness of the proposed controller. Finally, the simulation results confirm that the proposed controller is very effective in enhancing the stability and performance of the nonlinear systems by compensating them and forcing their states to track the reference model states asymptotically with an acceptable and workable control action. [ABSTRACT FROM AUTHOR]

This paper mainly investigates formation control problems for a group of anonymous mobile robots with unknown nonlinear disturbances on a plane, in which all robots can asymptotically converge to any formation patterns without collision, and maintain any required relative distance with neighboring robots. To solve this problem, all robots are modeled as kinematic points and can only acquire information from other robots and their targets. Furthermore, a flexible distributed control law is designed to solve the formation problem while no collisions between any robots can be guaranteed during the whole process. The outstanding feature of the proposed control method is that it can force all mobile robots to form not only uniform circle formations but also non-uniform and non-circular formations with moving target centers. At last, both theoretical analysis and numerical simulations show the feasibility of the proposed control law. [ABSTRACT FROM AUTHOR]

The system information of 2-D systems is usually in propagation along two independent directions. This article focuses on the issue of output feedback sliding mode control (SMC) for 2-D nonlinear systems through T-S fuzzy affine models. Associated with the sliding surface function, a singular system is established to describe the sliding mode dynamics. Based on piecewise quadratic Lyapunov functions, some new stability analysis results on the sliding mode dynamical system are attained, and the analysis conservatism can be further reduced. A robust output feedback dynamic SMC synthesis scheme is developed to guarantee the finite-time reachability of the sliding surface. It is also worthy of mentioning that the proposed dynamic SMC synthesis method can handle the mismatched disturbances, and the control input channels are permitted to have parameter uncertainties. Simulation studies are given to illustrate the validity of the developed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear optimal control approach for mulitple degrees of freedom (DOF) brachiation robots, which are often used in inspection and maintenance tasks of the electric power grid. Because of the nonlinear and multivariable structure of the related state-space model, as well as because of underactuation, the control problem of these robots is nontrivial. The dynamic model of the brachiation robots undergoes first approximate linearization with the use of Taylor series expansion around a temporary operating point which is recomputed at each iteration of the control method. For the approximately linearized model, an H-infinity feedback controller is designed. The linearization procedure relies on the Jacobian matrices of the brachiation robots' state-space model. The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the brachiation robots, under model uncertainties and external perturbations. For the computation of the controller's feedback gains an algebraic Riccati equation is solved at each time-step of the control method. The global stability properties of the control scheme are proven through Lyapunov analysis. The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the brachiation robots, under moderate variations of the control inputs. [ABSTRACT FROM AUTHOR]

In this article, a L1 neural network adaptive fault-tolerant controller is exploited for an unmanned aerial vehicle attitude control system in presence of nonlinear uncertainties, such as system uncertainties, external disturbances, and actuator faults. A nonlinear dynamic inversion controller with sliding mode control law is designed as the outer-loop controller to track the attitude angles quickly and accurately which reduces dependence on model accuracy. A L1 neural network adaptive controller of the inner loop is introduced to compensate the nonlinear uncertainties and have a good attitude tracking. The radial basis function neural network technique is introduced to approximate a lumped nonlinear uncertainty and guarantee the stability and transient performance of the closed-loop system, instead of converting it to a half-time linear system by the parametric linearization method. Simulation results demonstrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

In this paper, a theory and practice-based research is conducted using real data, in order to increase the efficiency of a Vestas 660 kW wind turbine. The data obtained from Binalood's wind farm, shows the degraded performance of these turbines. This is mainly because of the weak performance of the classical controller in tracking to reach the maximum power in the partial load region. Another factor with a negative effect on efficiency of the turbines, is the low range of generator speed variation. In this research, two solutions are proposed to solve these problems. First, a nonlinear state feedback controller with wind speed calculator is designed which improves the turbine performance. Second, it is shown by simulation results that the turbine efficiency is improved by increasing the range of generator speed variation. Also, the model is validated using actual data and FAST model. [ABSTRACT FROM AUTHOR]