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The present work aims at investigating the adaptive iterative learning control (AILC) design for uncertain high‐order fully actuated nonlinear systems. In order to show the design principles, three types of nonlinear systems are considered, including systems with just parametric uncertainty, systems with both parametric and input distribution uncertainties as well as systems with both parametric uncertainty and an unknown control gain matrix. For different systems, the corresponding AILC schemes are proposed with different techniques in dealing with various system uncertainties, for which the convergence analysis are conducted rigorously based on the composite energy function. The effectiveness of the proposed AILC strategies is verified through numerical simulations. [ABSTRACT FROM AUTHOR]

In this article, a multi‐event‐triggered (MET) near‐optimal tracking control algorithm is developed for a discrete‐time (DT) nonlinear non‐zero‐sum game (NZSG). Firstly, the iterative dual heuristic dynamic programming (DHP) approach is utilized to obtain the optimal tracking control set. Subsequently, distinct independent triggering conditions are formulated for control inputs to improve the utilization efficiency of resources and ensure that the independence between them and the closed‐loop system has an excellent control performance. Meanwhile, input‐to‐state stability (ISS) is proven for the MET tracking plant. Additionally, neural networks (NNs) are designed in the iterative DHP algorithm to construct the model module, the critic module and the action modules, with aims to identify the unknown system, approximate costate function and estimate optimal tracking control strategy set, respectively. Finally, two numerical experimental simulations are conducted to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, the H∞$$ {H}_{\infty } $$ optimal output tracking control problem for Markov jump systems is investigated, where the two cases with known or completely unknown transition probabilities are both considered. Based on game theory and H∞$$ {H}_{\infty } $$ performance, quadratic cost is considered, where a discount parameter is introduced into the quadratic cost in order to track unstable systems and eliminate the assumption that the noise energy is bounded. The game coupled algebraic Riccati equation and the corresponding controller are presented by dynamic programming. The stochastic stability of the tracking error system is further investigated. Moreover, iterative and reinforcement learning‐based algorithms are proposed for solving the H∞$$ {H}_{\infty } $$ optimal tracking controller with known or completely unknown transition probabilities, respectively. Finally, some numerical simulations on a DC motor are performed to validate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This study investigates the iterative learning control method for discrete‐time systems with data quantization, and it employs a quantizer based on spherical polar coordinates. The quantizer uses spherical polar coordinates to transform an unquantized signal into one with a predetermined quantization level. The quantization capability is dependent on the size of the support sphere, which is dynamically adjustable. In the scenarios of system output quantization, general quantization, and tracking error quantization, the learning control schemes are developed with consideration of the smallest sphere quantization features. The radii of the support spheres are designed independently based on the characteristics of the quantized signals (system output, control input, tracking error). The findings suggest that the system can achieve a bounded error convergence in the scenarios of system output and general quantization. In the context of quantization in tracking error, it is achievable to attain tracking performance with zero error. The example of a permanent magnet synchronous motor is provided to illustrate the findings of the study. [ABSTRACT FROM AUTHOR]

The fully distributed output leader‐follower event‐triggered resilient control algorithm is proposed for heterogeneous multi‐agent systems (MASs) under communication link faults and constraints. An estimation of the information of the leader is achieved by designing a dynamic event‐triggered observer with resilience to link faults without using the eigenvalues of the global matrix. Zeno behavior is also excluded under the dynamic triggering mechanism. Considering that the state information of the follower is not available, a distributed output leader‐follower control law is further designed to achieve asymptotically convergence of the leader‐follower consensus error, and only the output information of followers is required. Finally, the availability of the theoretical results is shown in two numerical examples. [ABSTRACT FROM AUTHOR]

Summary: Data losses and noises in both forward and feedback channels significantly impact the convergence of networked iterative learning control (ILC) systems. To address this issue, this article considers a class of linear time‐invariant objects controlled by proportional ILC controllers, an optimal state filter is then designed at the ILC controller side that aims to guarantee the convergence of the input transmitted by ILC controllers. First, two data transmission processes are introduced to account for the effects of data losses and noises. Second, a filtering model is established utilizing only the object information and the aforementioned data transmission processes. Third, the optimal state filter is designed on the basis of the orthogonal projection principle. This filtered state facilitates the acquisition of actual output errors, thus improving the convergence of the input transmitted by ILC controllers. Simulation results demonstrate the effectiveness of the proposed state filters. [ABSTRACT FROM AUTHOR]

Before starting stability analysis of the multivirtual synchronous machine (n‐VISMA) power system, it is necessary to obtain the steady‐state operating points (SSOPs) of all dynamic nodes in the network. Modified traditional iterative schemes using the concept of droop bus technique in an islanded microgrid are not feasible for load flow analysis of VISMA microgrid incorporating non‐control dynamics. This paper proposes closed‐form steady‐state, fundamental‐frequency models for islanded VISMA microgrids using the concept of virtual swing buses. In this technique, the virtual internal buses of all VISMAs in the network are governed by the swing equation. The voltage at all buses is variable except the virtual buses in which the pole wheel voltages are prespecified. The algorithm was extended by a droop control localized to each VISMA. The suitability of the proposed algorithm to obtaining SSOPs of VISMA was tested on IEEE‐9 bus system with VISMA replacing electromechanical synchronous machines and also on a low‐voltage distribution system. To validate the applicability of the proposed algorithm and prove its accuracy, the case study systems were also modeled in the SIMULINK environment for detailed time domain analysis. The algorithm was found to be computationally effective for a load flow analysis of the VISMA microgrid. The results also reveal that the addition of external droop control improves the frequency stability of the system. [ABSTRACT FROM AUTHOR]

This paper investigates the design of a model‐free adaptive iterative learning controller(MFAILC) based on sampled‐data under the presence of denial‐of‐service(DoS) attacks in nonlinear networked control systems. First, the MFAILC is presented only using I/O data, where a compensation mechanism for DoS attacks is proposed. With dynamic linearization techniques, the nonlinear system is transformed into a linear system in the iteration domain. Then an improved MFAILC is designed to actively compensate the lost data caused by DoS attacks, where the estimation of pseudo‐partial derivative (PPD) is improved by establishing the AR model. The proposed algorithm can weaken the adverse effects of the DoS attacks and ensure the excellent tracking performance of the system. Finally, the stability of the method is proved, and the effectiveness of the proposed algorithm is demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]

In order to meet the increasing demand for high‐speed joining processes, laser beam micro welding is used to reproducibly weld metallic joining partners such as steel. Long weld seams or high cycle speeds, however, remain a challenge to achieve a constant welding depth throughout the entire welding process without major fluctuations. This paper shows the development and evaluation of a weld penetration depth control system based on interferometric measurement of the depth of the vapor capillary. High path speeds and small weld depths are challenging for real‐time depth control. Therefore, the offline data of the interferometric measurements are used to implement an iterative learning control of the weld penetration depth. The quality of the control is verified by welding with and without spatial power modulation on 1.4301 steel while following a linear and sinusoidal trajectory. [ABSTRACT FROM AUTHOR]

Summary: In this article, the decentralized repetitive tracking controller design for fractional‐order large‐scale Takagi–Sugeno fuzzy system with time delays is developed. We mainly focus on the design of a decentralized repetitive tracking controller based on the Lyapunov stability theory, by which the addressed large‐scale system asymptotically stabilized with H∞$$ {H}_{\infty } $$ performance index. Further, the repetitive control with quantized signal is developed to ensure the good tracking performance with the presence of interconnected model and external disturbances. Specifically, a logarithmic quantizer is used to quantify the control signal which can reduce the data transmission rate in the network. Finally, a numerical example is presented to verify the effectiveness of the proposed controller design technique. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive control (MPC) approach based on the extended disturbance observer (EDOB) for trajectory tracking of a coaxial octorotor unmanned aerial vehicle (UAV). First, the system dynamic model is derived using Newton–Euler relations in the presence of time‐varying centre of gravity (COG); then, a two‐loop cascade structure is presented to perform the trajectory tracking task. Both loops are controlled using MPC with feedforward compensation based on the EDOB to improve disturbance rejection abilities. When the mass changes, the moment of inertia and COG are affected. The EDOB simultaneously estimates the effects of time‐varying mass, external disturbances, and parametric uncertainties in six degrees of freedom. After obtaining virtual control inputs using designed controllers, constrained control allocation is used to obtain rotors speed in a valid range. The proposed control scheme is evaluated using simulation. The simulation results show the ability of the developed control strategy in accurate trajectory tracking and stable flight in different conditions and being robust to uncertainty and disturbance. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed model predictive control (DMPC) algorithm for dynamic decoupled discrete‐time nonlinear systems subject to nonlinear (maybe non‐convex) coupled constraints and costs. Solving the resulting nonlinear optimal control problem (OCP) using a DMPC algorithm that is fully distributed, termination‐flexible, and recursively feasible for nonlinear systems with coupled constraints and costs remains an open problem. To address this, we propose a fully distributed and globally convergence‐guaranteed framework called inexact distributed sequential quadratic programming (IDSQP) for solving the OCP at each time step. Specifically, the proposed IDSQP framework has the following advantages: (i) it uses a distributed dual fast gradient approach for solving inner quadratic programming problems, enabling fully distributed execution; (ii) it can handle the adverse effects of inexact (insufficient) calculation of each internal quadratic programming problem caused by early termination of iterations, thereby saving computational time; and (iii) it employs distributed globalization techniques to eliminate the need for an initial guess of the solution. Under reasonable assumptions, the proposed DMPC algorithm ensures the recursive feasibility and stability of the entire closed‐loop system. We conduct simulation experiments on multi‐agent formation control with non‐convex collision avoidance constraints and compare the results against several benchmarks to verify the performance of the proposed DMPC method. [ABSTRACT FROM AUTHOR]

This article studies the control barrier function (CBF) guided tracking performance optimization for switched systems under iterative learning control (ILC). CBF is an effective method for handling control system state safety. If the safe set is designed as expected performance, and the unsafe set is not expected, then this may be a good choice for optimizing control system performance. We propose a tracking performance optimization method based on CBF to speed up the convergence of the tracking error. A safe set is used to describe the expected tracking performance, by utilizing CBF, safety is further transformed into a set of inequality conditions that constrain ILC controller gains, which ensures that the tracking error is always within the safe set. In addition, an event‐triggering mechanism (ETM) is considered to reduce unnecessary data transmission, and sufficient conditions for exponential stability of the tracking error of switched systems and ILC controllers design are obtained. Finally, a simulation example is given to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This article studies the model‐free learning‐based distributed cooperative tracking control of human‐in‐the‐loop multi‐agent systems in the presence of an active leader. The core role of human‐in‐the‐loop is to use the ground station to send control commands to the non‐zero control input of the leader, and then directly or indirectly control a group of agents to complete complex tasks. Meanwhile, three essential demands including the completely unknown system model, the control objective obtained optimally, as well as no initial admissible control strategy requirement, are satisfied simultaneously. It is worth emphasizing that the relevant results only satisfy one or two demands at most, which are essentially not applicable to this problem. In this article, a model‐based human‐in‐the‐loop learning algorithm is first presented to achieve the optimal tracking control, as well as the convergence of the proposed learning algorithm is proved. Then, a bias‐based data‐driven learning algorithm is proposed, which provides the potential opportunities to overcome the difficulties caused by the above‐mentioned three demands. Finally, the validity of theoretical results is testified by a numerical example. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we develop an online learning algorithm for solving the Bellman equation for affine in the control discrete‐time nonlinear uncertain dynamical systems. To ensure accelerated learning of our algorithm in generating optimal control policies, we use an actor‐critic structure predicated on higher‐order tuner laws. More specifically, we construct a Nesterov‐like architecture involving momentum‐based learning laws leading to an accelerated convergence of the optimal control policy. The proposed online learning‐based optimal control framework guarantees uniform ultimate boundedness of the closed‐loop system under the assumption that the system is persistently excited. Finally, two illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

The power amplifiers (PAs) will embody the nonlinear distortion if they work at saturation so as to increase efficiency. The neural network (NN)‐based digital predistortion (DPD) is widely applied for the linearization of the PA in wireless communication. However, the current NN‐based DPD schemes only focus on designing the NN hidden layer and ignore improving the NN input layer, which loses the degree of freedom to fit the DPD. Therefore, this paper first proposes the generalized memory polynomial (GMP) input NN structure. The training data of GMP‐NN outputs are obtained by iterative learning control. The experimental results show that the GMP‐NN presents a great advantage in performance compared with the widely used GMP model and recurrent NN (RNN) model. From the testing data, it is observed that the minimum mean square error (MMSE) of the GMP‐NN is 4 dB lower than the conventional GMP model and is nearly 8 dB lower than the RNN model. The proposed GMP‐NN model is also verified in the DPD application, which shows 2.61 dB improvement in comparison with the GMP DPD model. [ABSTRACT FROM AUTHOR]

This article studies a new iterative method for a class of closed‐loop Hammerstein systems. The new iterative method solves the crossproducts between the parameters of the linear block and the nonlinear block by using the key term separation technique, decomposes a system into two subidentification models by utilizing the hierarchical identification principle for reduced computational complexity, and maximizes the maximum likelihood cost function by using the input and output data with a data window for improved parameter estimation accuracy. A numerical simulation example and a continuous stirred tank reactor experiment are presented to demonstrate that the proposed algorithm can work effectively. [ABSTRACT FROM AUTHOR]

Summary: In this article, we give an alternative representation and prior estimation of solution to delayed linear discrete systems with a second‐order difference with the help of the notation of delayed discrete matrix functions via their norm estimations. As an application, this solution is used to study iterative learning control under the suitable updating laws and sufficient conditions to guarantee that asymptotic convergence tracking results are presented. Finally, two numerical examples are given to verify the theoretical results. [ABSTRACT FROM AUTHOR]

The real‐time tracking process of dim targets in space is mainly achieved through the correlation and prediction of dots after the detection and calculation process. The on‐board calculation of the tracking needs to be completed in milliseconds, and it needs to reach the microsecond level at high frame rates. For real‐time tracking of dim targets in space, it is necessary to achieve universal tracking calculation acceleration in response to different space regions and complex backgrounds, which poses high requirements for engineering implementation architecture. This paper designs a Kalman filter calculation based on digital logic parallel acceleration architecture for real‐time solution of dim target tracking on‐board. A unified architecture of Vector Processing Element (VPE) was established for the calculation of Kalman filtering matrix, and an array computing structure based on VPE was designed to decompose the entire filtering process and form a parallel pipelined data stream. The prediction errors under different fixed point bit widths were analyzed and deduced, and the guidance methods for selecting the optimal bit width based on the statistical results were provided. The entire design was engineered based on Xilinx's XC7K325T, resulting in an energy efficiency improvement compared to previous designs. The single iteration calculation time does not exceed 0.7 microseconds, which can meet the current high frame rate target tracking requirements. The effectiveness of this design has been verified through simulation of random trajectory data, which is consistent with the theoretical calculation error. [ABSTRACT FROM AUTHOR]

In this study, a robust adaptive event‐triggered iterative learning control (ET‐ILC) method is developed for discrete‐time non‐repetitive nonlinear systems with both time‐varying system parameters and time‐iteration‐varying non‐parametric uncertainty. The proposed method utilizes an iteration‐varying dead‐zone function to estimate both parametric and non‐parametric uncertainties simultaneously. Moreover, an event‐triggered control mechanism including a mixed triggering strategy is employed to minimize the unnecessary usage of communication resource. This strategy updates the controller only when the triggering condition is met, thereby reducing the frequency of controller actions. The results are also extended to a class of multiple‐input–multiple‐output (MIMO) non‐repetitive nonlinear systems. The designed robust adaptive ET‐ILC algorithm not only guarantees perfect learning convergence in the iteration domain but also contributes to saving the system resources. The effectiveness of the presented method is demonstrated by virtue of the rigorous theoretical analysis and the illustrative simulation examples. [ABSTRACT FROM AUTHOR]

Considering the precise model information of the aerospace model is not always available under the effect of external disturbance and model uncertainty, a new data‐driven point‐to‐point iterative learning control (PTPILC) method is proposed for aerospace vehicles to track the predetermined trajectory. Firstly, the system of the aerospace vehicle is converted into a discrete form without any omission, which reflects the dynamics of the original system by input and output data. Then, the objective function of the tracking error is proposed in the quadratic form to facilitate the application of the conjugate gradient algorithm, which develops an improved data‐driven PTPILC method. And the convergence analysis of the proposed method is presented. Furthermore, the control parameters are optimized to make the proposed method more robust against different parameter deviations. Finally, the effectiveness of the proposed method is illustrated by different simulations. [ABSTRACT FROM AUTHOR]

Summary: This study proposes a novel iterative learning control scheme for discrete‐time linear systems based on the Broyden‐class optimization method. To overcome the difficulty of lacking system information, a cost function is introduced for the performance index by constructing a positive‐definite matrix with little system information. An optimization‐based learning control algorithm is proposed using a Hessian matrix approximation and the generated input sequence is demonstrated to exhibit a superlinear convergence rate. The proposed scheme is extended to address the point‐to‐point tracking problem. Numerical simulations are provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Element property is a crucial aspect of building information modeling (BIM) for almost all BIM‐based engineering tasks. Since there are limited properties predefined in Industry Foundation Classes (IFC) specifications, a vast number of property concepts were customized and stored in BIM models, which lack labor‐intensive data modeling and alignment for effective information management and reuse. To tackle the challenge, this study presents a natural language understanding (NLU)‐based method for the automatic ontological knowledge modeling of project‐specific property concepts from BIM models. A soft pattern matching model was used to acquire contextual definitions of concepts from a domain corpus before applying deep NLU models to transform the concept names and definitions into dense vector representations. These outputs were then fed into two stacking ensemble learning models to carry out two tasks: (a) classifying whether an unseen concept overlaps with the IFC ontology, and (b) aligning the repetitive concepts with the most relevant concepts in the ontology. Finally, all fresh properties were appended to an IFC ontology, either as new objects or new synonyms. The performance was evaluated based on 327 property concepts from real‐life BIM models. The results show that the proposed approach incorporating reading comprehension of definitions outperforms the existing name similarity‐based methods. Finally, a case study on a renovation project demonstrates the effectiveness of this study in automatic ontology modeling of property concepts. [ABSTRACT FROM AUTHOR]

By applying iterative learning control approach, the consensus is studied for multi‐agent systems (MASs) with one‐sided Lipschitz (OSL) nonlinearity. Firstly, the P‐type and D‐type learning schemes with initial state learning are introduced for such MASs. Then, utilizing the OSL and the quadratically inner‐bounded constraints, the convergence conditions of the consensus algorithms are presented and analyzed under a directed communication graph. We show that both algorithms, on a fixed finite‐time interval, can achieve perfect consensus tracking. Finally, the correctness of the obtained results is illustrated with simulation examples. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a PI‐type adaptive iterative learning control (PI‐AILC) method for nonlinear processes, which targets enhancing system tracking capabilities by adapting setpoints of the PI controller. First, the proposed method employs compact form dynamic linearization technology to obtain a local linear representation of unknown nonlinear systems. Subsequently, the iterative learning controller gain is adaptively updated using the local linear expression to ensure the optimality of the setpoints. Finally, a pre‐learning mechanism for offline data is introduced to augment the efficiency of the iterative mechanism further. The proof of strict convergence for PI‐AILC is established. Experimental results substantiate the efficacy of PI‐AILC. [ABSTRACT FROM AUTHOR]

We present a new framework integrating the AI model GPT‐4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT‐4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT‐4 in various capacities, wherein GPT‐4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in‐context learning of AI in the next iteration. This iterative human‐AI interaction enabled GPT‐4 to learn from the outcomes, much like an experienced chemist, by a prompt‐learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT‐4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine‐tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT‐4 to enhance the feasibility and efficiency of research activities. [ABSTRACT FROM AUTHOR]

We present a new framework integrating the AI model GPT‐4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT‐4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT‐4 in various capacities, wherein GPT‐4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in‐context learning of AI in the next iteration. This iterative human‐AI interaction enabled GPT‐4 to learn from the outcomes, much like an experienced chemist, by a prompt‐learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT‐4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine‐tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT‐4 to enhance the feasibility and efficiency of research activities. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive robust control problem based on reinforcement learning for an affine nonlinear system with unknown time‐varying uncertainty. Inspired by the ability to estimate uncertainty of neural network, a novel policy iteration algorithm is proposed which alternates between the value evaluation, uncertainty estimation, and policy update steps until the adaptive robust control law is obtained. Especially during the step of uncertainty estimation, the unknown time‐varying uncertainty is approximated by a radial basis function neural network and introduce it into the reinforcement learning framework. By designing an appropriate utility function, the algorithm improves both convergence rate and final approximate error comparing with existing reinforcement learning algorithm. The Lyapunov stability theorem provides theoretical demonstrations of the stability and convergence. Furthermore, the uniformly ultimately bounded stability of the affine nonlinear system is demonstrated with unknown time‐varying uncertainty. Finally, the performance of the proposed algorithm is demonstrated through a torsion pendulum system. [ABSTRACT FROM AUTHOR]

To solve the problem of contour error (CE) arising from the dual‐axis coupling of the X–Y coordinate planar motor stage under reciprocating motion conditions in integrated circuit (IC) manufacturing, the following two perspectives are considered: indirect intervention from a single‐axis servo tracking error (TE) control and direct intervention from a dual‐axis cross‐coupling control (CCC). An improved motion controller is designed using the iterative learning control (ILC) principle in conjunction with the motion characteristics of the dual‐axis mechanism. Specifically, the variable gain iterative learning controller with a variable forgetting factor (VFF‐VGILC) is designed to address the characteristics of single‐axis trajectory repetition and variable velocity planning in dual‐axis coupling. The significance of the VFF‐VGILC is to increase the servo control bandwidth of a single axis. On the other hand, the cross‐coupled iterative learning controller (CCILC) is designed on the dual‐axis cross‐coupling, while a pre‐compensation strategy is introduced in the CCC structure to form the pre‐CCILC. After experimental designing and testing are performed, the X–Y coordinate planar motor stage can effectively reduce the CE under various trajectory movements compared with the traditional controller. The trajectory curves drawn can meet the motion control requirements better and effectively enhance the motion control capability of the mechanism. [ABSTRACT FROM AUTHOR]

This article mainly studies the problem of the robust security iterative learning control for nonlinear cyber‐physical systems, which suffer from external disturbances and denial‐of‐service (DoS) attacks. First, the nonlinear system can be transformed into an iterative linear data model, which is only used for the controller parameter design and the stability analysis without the physical meaning. Then, an extended state observer is introduced to estimate external disturbances along the iteration axis. At the same time, considering the influence of DoS attacks, an attack compensation mechanism is designed for DoS attacks along the iteration axis. In addition, an iterative‐varying penalty is designed to accelerate the convergence of the tracking error. Further, the mathematical induction is used to decouple the control input from the tracking error and the compression mapping principle is utilized to prove that the tracking error is ultimately bounded under the influence of disturbances and DoS attacks. Finally, the main results are verified by the motor simulation. [ABSTRACT FROM AUTHOR]

In this paper, we consider n$$ n $$‐dimensional systems of differential equations applied to model a range of physical phenomena, where n$$ n $$ represents any positive integer. The defining characteristic that we focus on are the magnitudes of the inherent physical parameters, which are frequently of different orders of magnitude for various physical systems. This property, inherent to the given physical problems, can be exploited to reduce the problems into a set of much simpler subproblems, expanding our ability to gain physical insight in terms of the underlying simpler subproblems. These orders of magnitude motivate the choice of a specific class of transformations that can be chosen to reduce the number of required dynamic iterations in a sequence‐based approach to examining such systems. This is also useful for the development of proof techniques for proving properties of mathematical models, such as their well‐posedness. [ABSTRACT FROM AUTHOR]

The composite learning control with the heterogeneous estimator is proposed to deal with the multiple uncertainties of strict‐feedback nonlinear systems. The article applies the recorded data‐based neural learning and the disturbance observer (DOB) to learn the multiple uncertainties, including the nonlinear dynamics, the unknown control gain function (CGF), and the time‐varying disturbance. The lumped prediction error is constructed and included into the update law by neural approximation and disturbance observation. Furthermore, the asymmetric saturation nonlinearity (ASN) of the control input is represented by the smooth form model to ensure the input limitation, and a projection algorithm is adopted to avoid the singularity problem. The closed‐loop system stability is rigorously analyzed and the boundedness of the system tracking error is guaranteed. Through the tests of the third‐order nonlinear system and the autonomous underwater vehicle (AUV), it is observed that the proposed approach can improve the system tracking accuracy with the expected learning performance. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the parameter estimation problems of feedback nonlinear systems. Combining the multi‐innovation identification theory with the negative gradient search, we derive a multi‐innovation gradient‐based iterative algorithm. In order to reduce the computational burden and further improve the parameter estimation accuracy, a decomposition multi‐innovation gradient‐based iterative algorithm is proposed by using the decomposition technique. The key is to transform an original system into two subsystems and to estimate the parameters of each subsystem, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

An adaptive Dynamic Surface Controller (DSC) is designed for a two‐axis gimbal system with actuator dynamics in the presence of parametric uncertainties in [1]. A Lyapunov stability analysis is used to guarantee the convergence of the tracking error to the origin and boundedness of all closed‐loop signals. The main objective of this corrigendum is to point out several errors that occurred throughout the paper, resulting in the inaccuracy of the used dynamic model and ineffectiveness of the proposed controller. It should be noted that taking into account the corrections stated in this corrigendum, the main result of the original paper is still valid. [ABSTRACT FROM AUTHOR]

This paper, considers the channel estimation problem of the uplink low Earth orbit (LEO) satellite orthogonal frequency division multiplexing (OFDM) system. By exploiting the channel structured sparsity in the delay‐Doppler‐angle domain, the channel estimation problem is formulated as a block‐sparse signal recovery problem. The variance state propagation (VSP) framework is extended to solve such a problem. Considering that the delay, Doppler shift, and angle of departure (AoD) of each propagation path may not exactly fall on the presumed discrete grid points, an expectation maximization (EM) based parameter learning scheme in VSP to iteratively update the grid parameters is introduced. Simulation results demonstrate that the proposed approach achieves a substantial performance gain over the existing methods. [ABSTRACT FROM AUTHOR]

This article develops a novel framework for iterative learning control (ILC) design of discrete‐time systems with non‐uniform trial lengths by using the method of alternating projections. In contrast to existing results for the non‐uniform trial length problem, this article uses the Hilbert space setting and hence the linear discrete‐time system dynamics with non‐uniform trial lengths can be represented by multiple affine subspaces (or linear varieties). Motivated by the successive projection design between two closed convex sets, the considered ILC problem can be transformed into alternating projections between multiple sets, then the Hilbert space setting is used to establish key systems theoretic properties. Moreover, an optimal ILC design is developed for systems with non‐uniform trial lengths, which is also extended to the case of input constraints. A numerical case study is given to illustrate the applicability of the new design. [ABSTRACT FROM AUTHOR]

System identification is a critical task in various engineering applications such as motion control, signal processing and robotics. In this article, the identification of linear time‐varying (LTV) systems that perform tasks repetitively over a finite‐time interval is investigated. Traditional LTV system identification typically adopts recursive algorithms in the time domain, which are incapable of tracking drastic‐varying parameters and are subject to estimation lag and numerical instability. To address these issues, this article proposes the utilization of an iteration axis in addition to the time axis for estimating repetitive time‐varying parameters. Specifically, the proposed approach involves an estimation algorithm for the time‐varying parameters based on a recursive least squares (RLS) method along the iteration axis, as well as an update algorithm for the covariance matrix based on singular value decomposition (SVD) to enhance numerical stability. Additionally, a bias compensation method based on noise variance estimation is introduced for the sake of eliminating estimation error induced by measurement noise. Numerical comparisons with existing methods are conducted to demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Two challenging problems are addressed in this work for the convergence analysis of iterative learning control (ILC), that is, the rigorous assumption on repetitive conditions and the severe dependence on linear or nonlinear parametric models. Consequently, a data‐based analysis method of ILC is presented for a general multi‐input multi‐output nonaffine nonlinear system in the existence of multiple nonrepetitive uncertainties in initial states, external disturbances, reference trajectories, and plant models. An extended iterative dynamic relationship is constructed to extract the linear relationship of I/O dynamics for a nonaffine nonlinear system between two different iterations. The data‐based double dynamics analysis method is developed for analyzing that the tracking error is convergent and the I/O sequence is bounded. The tracking error is shown to converge to a finite bound related to nonrepetitive uncertainties and achieve a perfect convergence if nonrepetitive uncertainties are also iteratively convergent to zero. Notably, the presented analysis method merely uses the I/O data without relying on the plant model. Simulations validate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]

The problem of adaptive semi‐active control of transient structural vibration induced by unknown harmonic excitation is studied. The controller adaptation is attained by using a specially designed reinforcement learning algorithm that adjusts the parameters of a switching control policy to guarantee efficient dissipation of the structural energy. This algorithm relies on an efficient gradient‐based sequence that accelerates the learning protocol and results in suboptimal control. The performance of this method is examined through numerical experiments for a span structure that is equipped with a semi‐active device of controlled stiffness and damping parameters. The experiments cover a selection of control learning scenarios and comparisons to optimal open‐loop and heuristic state‐feedback control strategies. This study has confirmed that the developed method has high stabilizing performance, and the relatively low computational burden of the incorporated iterative learning algorithm facilitates its application to multi–degree‐of‐freedom structures. [ABSTRACT FROM AUTHOR]

In this work, we propose the construction of discrete‐time systems with two time scales in which infectious diseases dynamics are involved. We deal with two general situations. In the first, we consider that individuals affected by the disease move between generalized sites on a faster time scale than the dynamics of the disease itself. The second situation includes the dynamics of the disease acting faster together with another slower general process. Once the models have been built, conditions are established so that the analysis of the asymptotic behavior of their solutions can be carried out through reduced models. This is done using known reduction results for discrete‐time systems with two time scales. These results are applied in the analysis of two new models. The first of them illustrates the first proposed situation, being the local dynamics of the SIS‐type disease. Conditions are found for the eradication or global endemicity of the disease. In the second model, a case of co‐infection with a primary disease and an opportunistic disease is treated, the latter acting faster than the former. Conditions for eradication and endemicity of co‐infection are proposed. [ABSTRACT FROM AUTHOR]

Switching boundary is an integral part of the switched system, but the existing literature on switched systems does not focus on the system under switching boundary mismatch. In this article, the switching boundary mismatch is considered, and the control strategy based on observation algorithm is designed for the state dependent switched system. By implementing the proposed strategy, the system can realize robust exponential stability and track the desired trajectory. Since the switching boundary mismatch is unmeasurable, an observation algorithm is designed. Combining the observation algorithm and iterative learning control law, the new control strategy can be derived. According to the Lyapunov stability theory and mode dependent average dwell time method, the robust exponential stability conditions of the closed‐loop system based on linear matrix inequalities are given. The mode dependent average dwell time of each subsystem can be solved simultaneously. Finally, simulations have been taken to verify the designed strategy can render the closed‐loop system achieve robust exponential stability and can track the desired trajectory. [ABSTRACT FROM AUTHOR]

In this work, a novel adaptive event‐trigered iterative learning control (ILC) scheme is designed for discrete‐time nonlinear systems with time‐varying parameters and random initial states. The reference trajectory can be iteration‐invariant or iteration‐varying. The control input is updated when the event‐triggering condition is satisfied, which reduces the action time of controller and consequently the unnecessary waste of communication resource. An adaptive event‐triggering condition parameter is designed to improve the efficiency of the event generator. Moreover, the parameter updating law of adaptive iterative learning controller is designed based on the recursive least squares algorithm. With the proposed adaptive ILC with event‐triggered mechanism, the learning convergence along the iteration axis is guaranteed and communication burden is reduced. Finally, rigorous theoretical analysis under Lyapunov theory and illustrative examples are given to demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed performance fault‐tolerant attitude tracking control for flexible spacecraft under limited communication network, where the controller and the actuator are connected via wireless network. The hysteresis quantizer is employed to quantize the control signal, which can reduce the communication burden of the network onboard. First, a novel iterative learning observer is developed by combining with neural‐network approximation method to reconstruct the actuator faults and estimate the unmeasurable nonlinear rigid‐flexible dynamics simultaneously. Then, the signal quantization technique is introduced for control command quantization, and the concerned parametric quantization error is given. Based on the learning observer output, prescribed performance design procedure, and a quantization error compensation method, an active fault‐tolerant control strategy is developed for flexible spacecraft attitude tracking to deal with actuator faults, unmeasurable system nonlinearity, quantization errors, and external disturbances. The stability analysis of the closed‐loop system proves that the quantization errors, modal vibrations, disturbances can be effectively rejected, moreover, the convergence of the transformed performance states and angular velocity states can be guaranteed, which achieves the tracking problem with predetermined transient‐state and steady‐state performance requirements. Finally, a numerical simulation is conducted to illustrate the validness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper focuses on vehicle tracking control issues with consideration of energy efficiency, ride comfort and tracking performance. To address this issue, an economic model predictive control (EMPC) framework is proposed by taking average performance into account and introducing a time‐varying terminal set. We integrate an economic model predictive controller and a terminal controller with a feedback gain related to the time‐varying terminal set into the proposed EMPC framework. An acceleration‐dependent average constraint is designed and incorporated into the MPC optimization problem to facilitate the convergence of the actual acceleration to the desired one, which enables the vehicle to implement a smooth driving style that boosts energy economy. A tuning factor in the average constraint has the capacity to balance energy consumption and control performance. Under the premise of invariance constraints, a dynamic terminal set is constructed by solving a dynamic programming problem online, which enables an arbitrary location and scale of the terminal set at each iteration by taking the current and predicted tracking errors into account. This allows a reduction in the conservativeness of an economic model predictive controller in the sense of both the region of attraction and the cost bound. Recursive feasibility of the MPC optimization problem and the terminal set dynamic programming is ensured, and average performance is not worse than the performance with an optimal steady‐state operation. Moreover, the convergence of the closed‐loop tracking error to the optimal steady‐state is guaranteed by using the dissipativity theory. The effectiveness of the proposed algorithm is verified by numerical comparisons with different controller parameters and standard EMPC. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the surge speed tracking control of an autonomous surface vehicle (ASV) subject to fully unknown internal dynamic, external disturbance, and unknown control input gain. An adaptive anti‐disturbance sampling control method is proposed for an ASV without using any model parameters. Specifically, by utilizing real‐time and historical data, discrete‐time reduced‐order and full‐order concurrent learning extended state observers (CLESOs) are designed to estimate the unknown ASV model parameters and ensure estimation convergence without requiring persistent excitation. Then, a surge speed tracking control law is designed based on the discrete‐time full‐order CLESO. Through Lyapunov stability analysis, the closed‐loop system is proven to be stable, and the estimation and tracking errors are bounded. Simulation and hardware‐in‐loop experimental results validate the effectiveness of the proposed discrete‐time CLESOs for the surge speed tracking of an ASV with fully unknown dynamic model. [ABSTRACT FROM AUTHOR]

Developing a control strategy that can handle model uncertainties, external disturbances, and parameter fluctuations is essential for the desired operation of a nonlinear system. An attempt is made to develop such a control algorithm using the Learning Variable Structure Control technique. The proposed controllers have the advantages of both iterative learning control (ILC) and sliding mode control (SMC) such that they can compensate both structured and unstructured uncertainties. They are made comprised of modified‐twisting algorithm and super‐twisting algorithm respectively with the ILC technique. In this article, the performance of the two developed controllers is compared to a traditional SMC‐based ILC approach. Based on simulation results, the super‐twisting algorithm based ILC is found to be superior with reference to speed of convergence and tracking accuracy. This method is used to control the slip‐ratio of an electric vehicle as a specific application in this paper. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the identification problem of an output‐error nonlinear system with saturation and dead‐zone nonlinearity. Introducing a switching function and by means of the auxiliary model identification idea, an auxiliary model hierarchical least squares‐based iterative algorithm is proposed for estimating the parameters of the nonlinear system. Based on the hierarchical identification model, an auxiliary model hierarchical gradient‐based iterative algorithm is presented for the nonlinear system by utilizing the gradient search. In order to take full advantage of the system data, an auxiliary model hierarchical multi‐innovation gradient‐based iterative algorithm is derived for the nonlinear system according to the multi‐innovation identification theory. Finally, the numerical simulation results illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on product quality control issue of polystyrene polymerization reaction process. A novel tube‐based batch model predictive control (BMPC) strategy based on a data‐driven model is presented, which is inspired by the tube‐based robust model predictive control (MPC) strategy. First, the dynamic behavior of the polystyrene polymerization reaction process is captured with high accuracy by establishing a just‐in‐time learning (JITL) model. Then, the built JITL model is regarded as the nominal system, and a BMPC is designed on the basis of JITL model to obtain the nominal trajectory, which is an integrated control system framework composed of iterative learning control (ILC) and MPC. Meanwhile, another auxiliary MPC (AMPC) is designed to minimize the deviation between the actual trajectory and the nominal trajectory, and the actual tracking errors are limited to the tube invariant set centered on the nominal errors to restrain the performance deterioration of the control system caused by the modeling errors. Finally, the effectiveness of the proposed tube‐based BMPC method is verified by simulating an industrial batch polystyrene polymerization reaction process. The results indicate that the presented control algorithm not only enhances the tracking performance of the system but also provides a more robust system stability than existing methods. This paper provides a new solution to improve the product quality of polystyrene polymerization reaction process. [ABSTRACT FROM AUTHOR]

Summary: The design of tracking control problem and compensation of disturbance for switched neutral systems with multiple time‐delays and external disturbances are addressed in this paper. In this regard, a modified repetitive control technique based on the Matausek‐Micic modified Smith predictor approach is being implemented, which assures the exact tracking performance and disturbance attenuation with high precision in the considered system. To be specific, the integration of transfer function with modified Smith predictor block not only provides the accurate compensation of input time‐delays but also ensures the exact estimation and attenuation of external disturbances effectually. Furthermore, according to Lyapunov stability approach combined with average‐dwell‐time technique, a group of adequate conditions is derived in the form of matrix inequalities. Simultaneously, by solving the established matrix inequalities using available software the controller gain matrices are calculated. Ultimately, the simulation results of three numerical examples are presented to validate the efficiency and dominance of the suggested control procedure. [ABSTRACT FROM AUTHOR]

This paper studies the iterative learning control (ILC) for a class of second‐order nonlinear hyperbolic impulsive partial differential systems. Firstly, to follow the discontinuous desired output, a P‐type learning law is adopted, and sufficient conditions for the convergence of the tracking error is established under identified initial state value. The rigorous analysis is also given using the impulsive Gronwall inequality. Secondly, the tracking error of output trajectory is considered in systems with state initial values shifting based on an initial learning algorithm. These results of this paper show that the tracking error on the finite time interval can uniform converge to 0 as the iteration index goes to infinity if impulse number of the systems is only a finite numbers. Finally, two numerical simulation examples are given to verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]