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This article pays attention to the obstacle avoidance trajectory tracking control problem for uncertain Euler–Lagrange (EL) systems subject to velocity constraints and input saturation. The existing obstacle avoidance results do not consider velocity constraints under input saturation, which means that an EL system may not be able to obtain sufficient control inputs to avoid a collision with an obstacle if it has a high speed when approaching the obstacle. Therefore, the velocity constraints in the obstacle avoidance tracking control are considered in this paper. A novel velocity constraint function that depends on the distance between the system and the obstacle is proposed. Integral‐multiplicative Lyapunov‐barrier functions (LBFs) are constructed and incorporated into the backstepping procedure to design an adaptive fuzzy obstacle avoidance tracking control scheme. Moreover, an auxiliary dynamic system is designed by constructing a bounded nonlinear vector related to an auxiliary variable to compensate for the effects of saturation. Through the Lyapunov method and boundedness analysis for the barrier function, it is shown that the protocol achieves obstacle avoidance for the EL system without violating the velocity constraints inside the obstacle detection region, while also guaranteeing the ultimate uniform boundedness of all the closed‐loop signals. Numerical simulations are presented to demonstrate the efficacy of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this article, a neuroadaptive sliding mode control (NSMC) strategy based on recurrent neural network (RNN) for robustly and adaptively tracking the desired position and attitude of an uncertain tilting quadrotor unmanned aerial vehicle (TQUAV) with unknown controllers is presented. The main contribution of this article is the real‐time adjustment of unknown flight controllers using the approximation characteristics of RNN, in which the derived approximation errors of RNN are sufficiently estimated by adaptive control method that can reduce or eliminate the impact of error terms on the evolution of closed‐loop systems. Especially, Lyapunov stability analysis is greatly simplified compared to existing methods and does not require amplification or reduction. Finally, the superior performance of the NSMC strategy was verified by comparing simulation results. [ABSTRACT FROM AUTHOR]

This paper addresses neural‐network‐based adaptive prescribed‐time (PT) tracking for uncertain switched systems with unmatched nonlinearities. A continuously switched adaptive tuning mechanism for neural network learning is developed by applying the average dwell time (ADT). First, a neural‐network‐based PT tracking control design strategy using the ADT‐based adaptive tuning mechanism is established for switched nonlinear systems in strict‐feedback form. A novel adaptive dynamic surface controller is designed recursively using a practical finite‐time scaling function and continuously switched tuning parameters. The switched adaptive tuning laws for neural networks are structured to reduce the conservatism associated with common adaptive laws. Then, a filter‐based tuning approach is employed to ensure the continuity of switched adaptive parameters with ADT in the designed controller. The practical PT stability of the closed‐loop system is demonstrated based on the boundedness of the adaptive parameters. Building upon this foundation, the proposed PT design approach is extended to control switched pure‐feedback nonlinear systems, even in cases where control directions are unspecified. The unknown sign problem encountered with switched virtual and actual control coefficient functions is resolved in the PT control framework. It is shown that the PT performance bound of the tracking error can be reduced by selecting the design parameter of the scaling function. Finally, simulation results illustrate the merits of the proposed theoretical approach. [ABSTRACT FROM AUTHOR]

Connected and Autonomous Vehicles (CAVs) are shaping the future of transportation systems; however, their future development requires consideration of the upcoming challenges with cyber-physical attacks. Cooperative Adaptive Cruise Control (CACC) is one of the features used in CAVs. Using CACC increases the vulnerability of such systems to False Data Injection (FDI) attacks. To address this, this paper proposes a novel mitigation approach to compensate for FDI attacks and noise in real-time. In addition to FDI attacks, the current CACC algorithm suffers from input delay and disturbance. Here, we develop a nonlinear controller and an FDI attacks estimation technique to mitigate the effects of FDI attacks, noise, input delay and additive disturbance. Lyapunov–Krasovskii functionals are used in the Lyapunov-based stability analysis to ensure semi-global uniformly ultimately bounded tracking. In order to demonstrate the performance of the proposed approach, we validate it through simulation analysis. [ABSTRACT FROM AUTHOR]

This paper investigates a dual-channel event-triggered model-free adaptive iterative learning control problem for unknown nonlinear systems under periodic denial-of-service (DoS) attacks. Firstly, periodic DoS attacks on the measured output of the system are modelled using the Bernoulli distribution. Then, to minimise the utilisation of system bandwidth resources, a dual-channel event-triggered mechanism is designed for the sensor-to-controller and controller-to-actuator channels. Subsequently, a dual-channel event-triggered model-free adaptive iterative learning control algorithm is introduced. The convergence of the tracking error is proven in terms of mathematical expectation using Lyapunov stability theory. Finally, the effectiveness of the proposed algorithm is verified through two simulation cases. [ABSTRACT FROM AUTHOR]

In this article, an adaptive event-triggered bounded control strategy is proposed for a class of high-order uncertain switched nonlinear systems with full-state time-varying constraints under average dwell time switching rule. In order to achieve the boundedness of the control signal, the projection operator is introduced. At the same time, the high-order log-type time-varying barrier Lyapunov function method is chosen to ensure that the desired output and states of the systems do not violate the relevant constraints. In addition, the improved event-triggered control strategy effectively reduces the transmission burden and saves the resource cost. The stability analysis shows that the proposed scheme achieves the ultimate boundedness of all signals in the closed-loop systems and the states of the systems do not violate the corresponding constraints. It should be noted that the proposed controller has a known bound and this bound can be ensured by the rectification of the feedback control gain. Finally, the simulation results verify the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

Geometrically, a complex dynamical network (CDN) can be regarded as the interconnected system composed of the node subsystem (NS) and the link subsystem (LS) coupled with each other. Guided by this idea, in order to achieve the goal of each node following asymptotically its own reference target in a CDN, this paper investigates the model following adaptive control (MFAC) problem of NS via the dynamics of links, which implies that the LS plays the important dynamic auxiliary role in the MFAC realization of nodes. Meanwhile, we focus on the condition that the links state information is unavailable, due to sensor practical application and measurement cost constraints. To obviate this restriction, we construct the asymptotical state observer for the LS. Next, to achieve the control goal of this paper, an appropriate Lyapunov candidate function is selected, by which the MFAC scheme for NS is synthesized based on the state observer of LS. Finally, the simulation example is performed to demonstrate the theoretical results in this paper. [ABSTRACT FROM AUTHOR]

A novel dual‐channel dynamic event‐triggered model‐free adaptive control method with compensation is proposed for nonlinear networked control systems (NCSs) subjected to random packet loss. In order to tackle the issue of redundant data transmission, a dual‐channel triggering control framework is designed, where data transmission only occurs upon meeting the triggering conditions. Compared with the traditional static event‐triggered schemes with fixed triggering thresholds, the proposed method allows for dynamically adjustable triggering thresholds. This means that the number of data transmissions in the forward and feedback channels can be further reduced. In addition, considering the detrimental impact of random packet loss and output event‐triggered on the system, compensation is applied to the system's output data using the linear data model of the nonlinear system, which is directly derived from I/O data without incorporating any other mechanistic model information, thereby ensuring optimal control performance. In contrast to existing results, the proposed control strategy can enhance system performance while conserving network communication resources. The effectiveness and superiority of the proposed scheme have been validated through two simulations. [ABSTRACT FROM AUTHOR]

In this article, an adaptive switching controller is designed for the stabilization control and the tracking control of nonlinear systems suffering from full state constraints, unknown control directions, and system uncertainties. Featured with changing signs and unbounded increasing magnitudes, a switching control structure is proposed to deal with the unknown control direction problem. In order to explore and finally fix the correct control direction, an adaptive switching rule, a switching function, and a reference function are constructed. An integral Lyapunov function is adopted for the design of the control law, which ensures that full state constraints will not be violated. Using this method, the nonlinear system can be properly controlled without extra estimations or approximations. The boundedness of all the states and the stability of the system are analyzed and guaranteed. Finally, simulations are conducted to validate the proposed method, and the results illustrate its effectiveness. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a novel event‐triggered adaptive asymptotic tracking control (ATC) method for stochastic non‐linear systems with unknown hysteresis. Firstly, in order to reduce the depletion of network resources while optimizing the asymptotic tracking performance of the system, a switching threshold mechanism (STM)‐based event‐triggered control (ETC) strategy is adopted. Secondly, a first‐order filter is utilized to address the problem of the contradiction between event‐triggered mechanism (ETM) output and rate‐dependent hysteresis actuator input. By incorporating an enhanced backstepping technique and a bounded estimation method, it is rigorously demonstrate that the system achieves zero tracking error, effectively compensates for unknown hysteresis, and ensures that all closed‐loop signals remain bounded in probability. Meanwhile, the Zeno phenomenon is excluded. Finally, the effectiveness and superiority of the proposed control scheme are verified by the simulation results. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task. Hence, a novel Q‐learning algorithm for unknown continuous‐time (CT) affine‐in‐inputs nonlinear systems is proposed for generating an approximate solution to the HJI equation, which is valid in a local domain due to the use of a local approximator, that is, a Neural Network (NN) structure. The approach is model‐free and does not require the knowledge of system drift dynamics, and input and disturbance gains. The algorithm learns online from measurements of state variables in real time. To generate the local approximate solution of the HJI equation for the two‐player zero‐sum game problem for nonlinear systems, the proposed non‐iterative algorithm requires only a single critic NN instead of the commonly used triple NN approximator structure. A persistence of excitation condition is required to guarantee Uniformly Ultimately Boundedness (UUB) and convergence to the optimal solution. The effectiveness of the proposed Q‐learning approach for the two‐player zero‐sum game problem is demonstrated via simulations of a linear F‐16 aircraft plant and a highly complex nonlinear system. Proof of closed‐loop system stability is provided using Lyapunov Analysis, and convergence of the approximate solution to the true saddle‐point solution is guaranteed in a UUB‐sense. [ABSTRACT FROM AUTHOR]

The success of machining process automation hinges primarily on the effectiveness of the monitoring and adaptive control systems. A new digital twin–based process monitoring method and system in small batch machining is presented, and the cutting model is integrated into the monitoring method to improve the diagnosis accuracy. Model-based and signal-based monitoring indicators are developed, and indicators of the residual force component for the tool wear monitoring and energy ratio for the chatter detection are introduced to the digital twin–based monitoring. The critical monitoring algorithm is verified in two cases: tool wear monitoring and chatter detection. The results show that the method proposed can accurately evaluate the percentages of the residual useful life of the tool and the chatter. Moreover, a new process monitoring system in small batch machining is developed by integrating the advanced algorithm. This study can provide a critical reference for intelligent machining monitoring and control in industry. [ABSTRACT FROM AUTHOR]

Gas metal arc welding (GMAW) integrated wire arc additive manufacturing (WAAM) depicts non-uniform layers, spatter, thermal distortions, metal overflow, and mechanical anisotropy. The prime cause for these issues is improper thermal energy management due to the current controlled wire feeding mechanism. Here, an autonomous wire feed system (AWFS) has been designed and integrated into the GMAW-WAAM to eliminate such issues. It fine-tunes the wire feed speed (WFS) and maintains a steady flow of arc current. With this developed system, initially, twenty single beads are deposited using ER70S-6 feedstock under different conditions of welding voltage (U), travel speed (TS), and WFS. Later on, an optimum deposit condition has been formulated using response surface methodology-RSM (U ≈ 21.7 V, TS ≈ 8.6%, and WFS ≈ 2.7 m/min). Under this deposit condition, thin-layered parts are manufactured, where inter-layer dwell time (IDT) is only varied (from 2 to 4 min) to illustrate its significance over metallographic and mechanical performances. It is observed that with increasing IDTs, the morphological attributes of the deposit are improved (straight side wall with low surface waviness) with a reduction in grain size that further boosts the hardness and mechanical performances (increased strength and wear resistance). The occurrence of compressive residual stress could additionally support the enhancements in mechanical strength. In addition to the improvements in mechanical performances, the anisotropy in mechanical strength was also reduced (< 5%). Moreover, bulk texture analysis ensures similar fiber texture evolutions along different deposit sections with a least variation in the texture intensity, which point towards isotropicity in the as-fabricated part. [ABSTRACT FROM AUTHOR]

This study focuses on addressing the challenge of adaptive finite‐time control for nonstrict‐feedback nonlinear systems subject to input delay and saturation. Neural networks (NNs) are utilized to handle unknown nonlinear functions, and Padé approximation is employed to effectively manage input delay. To mitigate the issue of "explosion of complexity," the command filter method is applied. By leveraging command filter technology and backstepping technique, an adaptive finite‐time control scheme is developed using NN approximation. The proposed control scheme demonstrates that the closed‐loop signals achieve semi‐global practical finite‐time stable (SGPFS), ensuring that the tracking error converges within a finite time to a small region around the origin. The effectiveness of the proposed scheme is validated through two simulation examples. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive robust constraint controller for a continuous stirred tank reactor (CSTR) system based on a self‐organizing fuzzy neural network (SOFNN). Due to the high complexity of the chemical reactions, the CSTR system contains many strong nonlinearities and uncertainties. This is the first time to introduce the SOFNN into the adaptive controller design of the CSTR system, which improves the adaptability to dynamic system changes through the adjustment of the fuzzy network structure. Meanwhile, the time‐varying integral barrier Lyapunov functions (TVIBLFs) are employed to ensure the dimensionless reactant concentration and mixture temperature within a reasonable scope, which can improve the stability and safety of the CSTR system. Based on Lyapunov stability analysis, all the signals in a closed‐loop system are ultimately bounded. Simulation results substantiate the efficacy of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article presents a novel multi‐thread attracting manifold (MTAM) adaptive control scheme. The novel contribution of this new controller lies in how the attracting manifold design method is spread across multiple estimates of the truth while simultaneously weighting between said individual estimates to arrive at an adaptive composite estimate which outperforms single thread adaptive controllers. A notable benefit of this new formulation is that it allows for both the weighting of multiple threads and monotonic decrease of parameter error to aid the performance of transient error dynamics, without arbitrarily increasing adaptation gains. The benefits of the proposed controller over existing adaptive control schemes are analyzed through two aerospace flight control applications from the literature. [ABSTRACT FROM AUTHOR]

This article aims at a new partial least squares (PLS) control design and analysis in a data-driven framework for nonlinear multivariable processes whose mechanistic models are completely unknown. First, a general nonlinear autoregressive moving average with the exogenous input (NARMAX) model is used as the dynamic nonlinear PLS model. Then, by introducing a dynamic linearisation approach in each latent variable (LV) space, the unknown NARMAX-based PLS model is transformed to a linear dynamic PLS data model (dPLSDM), which can be improved in real time by estimating its unknown parameter using the latent input and output (I/O) data. Next, a data-driven latent variable adaptive control (DDLVAC) is proposed in each LV loop. By virtue of the dPLSDM, the multivariable nonlinear process is decoupled into multiple single-loop systems and the high dimensions of the process data are reduced such that the corresponding DDLVAC is simplified. Further, the DDLVAC only depends on the I/O data without requiring any model information of the original process. Theoretical analysis confirms the validity of the DDLVAC. The simulation study demonstrates the advantages of the DDLVAC such as less storage space, smaller computation burden, less control cost, as well as more robustness against uncertainties. [ABSTRACT FROM AUTHOR]

This article addresses a neural adaptive event-triggered tracking control for a class of strict-feedback nonlinear dynamics with incomplete measurements, which is novel on the research of Cyber-physical Systems. The incomplete measurement problem caused by packet loss, saturation, and other issues during data transmission can lead to the unavailability of system state variables, which can degrade system performance and even lead to instability. To solve these problems, a state estimator for data-losing case and two controllers for normal and data-losing cases are designed utilising event-triggered strategies which can reduce the burden of calculation and data transmission. Radial basis function neural networks are adopted to approximate the unknown nonlinear system functions. A strict stability analysis in probability shows that the control laws for the considered strict-feedback nonlinear system can guarantee all the closed-loop to be uniformly ultimately bounded in mean square. Two examples are performed to demonstrate the effectiveness of the provided control method. [ABSTRACT FROM AUTHOR]

This article focuses on the region-reaching control for a non-holonomic mobile robot system with two actuated wheels. The region-reaching control task consists of three basic control objectives: reaching an objective region, maintaining a rest state in the objective region, and stabilising at a desired posture. The constraints imposed on the motion of the mobile robot system are not integrable and have no inverse resolution, and thus increase the complexity of the controller design for non-holonomic systems. A system decomposition approach is proposed to convert the non-holonomic constraint control system to the cascade holonomic constraint control subsystems, and an adaptive control gain technique is presented to guarantee the stability of the non-holonomic system. Then, a novel torque controller is presented to implement the region-reaching control task. Specially, the objective region can be considered as a parking spot for a mobile robot. [ABSTRACT FROM AUTHOR]

A novel non-backstepping output feedback control approach, called as the dual-gain scaling approach, is proposed by combining the extended Lyapunov matrix inequalities with dual-domination idea. This approach provides partially decentralised adaptive output feedback controllers with three event-triggered mechanisms to achieve the global practical tracking of a family of uncertain large-scale nonlinear systems subject to unknown control coefficients and strongly interconnected nonlinearities with output-polynomial growth rate. The dual gains are embedded in the observers and controllers, which can effectively compensate for time-varying control coefficients and unknown constant growth rate multiplied by output-polynomial function. Three different threshold schemes including fixed threshold, relative threshold and time-varying threshold are introduced to save communication resources. The stability analysis shows that the solutions of the closed-loop system are globally uniformly bounded and the tracking error converges to a preset compact set after a finite time; also, there will be no zeno behaviour in the closed-loop response. [ABSTRACT FROM AUTHOR]

This paper studies the problem of global output-feedback tracking control for a class of uncertain nonlinear systems. The system studied in this paper has unknown time-varying control coefficients and unknown reference signal. Notably, the nonlinearities of the system are bounded by the lower triangular linear unmeasured states multiplying the unknown constant, the polynomial-of-output and the polynomial-of-input growth rates, which indicates the presence of serious uncertainties. Motivated by the closely related works, by combining the ideas of universal control and deadzone with backstepping technique, this paper presents a new adaptive tracking control scheme based on two dynamic high-gains in the new forms. The proposed control scheme ensures that the tracking error converges to a specified arbitrarily small interval range after a finite time while the state of the resulting closed-loop system is globally bounded. Finally, the effectiveness of the control scheme is verified by an illustrative example. [ABSTRACT FROM AUTHOR]

For the nonlinear systems with input delay, an adaptive fixed-time tracking control strategy based on multi-dimensional Taylor network (MTN) is proposed for the first time. First, to address the main challenge brought by input delay, an auxiliary system is constructed, which transforms the input delay systems into non-delay systems. Second, the MTNs are introduced into the backstepping design process, which reduce the design difficulty by approximating unknown nonlinear functions. Thus, a control scheme subject to low complexity is obtained. Third, aside from ensuring that the tracking error converges within a small neighbourhood of the origin within a fixed-time, the designed control scheme not only makes all signals in the closed-loop system remain bounded, but also avoids the problem of finite-time control relying on initial state. Finally, three simulations are given to prove the feasibility and superiority of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Continuum robots have great application value and broad prospects in various fields due to their dexterity and compliance. To fully exploit their advantages, it is crucial to develop an effective, accurate and robust control system for them. However, research on continuum robot control is still in its infancy and there are many problems remaining unsolved in this field. In particular, this paper focuses on the task-space behavior and the generic control of heterogeneous continuum robots. First, a controller is proposed to achieve the kinematic motion control and visual servoing of continuum robots with predefined task-space behavior. The predefined behavior is twofold: prescribed task-space error and predefined convergence time. Then, the proposed controller is integrated with a velocity-level kinematic mapping estimator to obtain a model-free control system, which is applicable to heterogeneous continuum robots. Furthermore, a re-adjustable performance function is proposed to ensure the effectiveness and robustness of the proposed control system in the presence of external disturbance. Finally, extensive simulations and experiments are performed based on heterogeneous continuum robots, including the cable-driven continuum robot, the parallel continuum robot, the concentric-tube robot, the flexible endoscope, and the pneumatic continuum robot. Our results demonstrate that the task-space error of heterogeneous continuum robots complies with the prescribed boundaries and converges to steady state in predefined time, which reveals the efficacy of the proposed control method. [ABSTRACT FROM AUTHOR]

This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient to model uncertainties, unmodeled dynamics, external disturbances, and actuator faults. The proposed approach synergistically combines a neural network for handling model uncertainties, unmodeled dynamics, and actuator faults, with a disturbance observer for compensating external disturbances and neural network estimation errors. The command-filtered backstepping technique avoids the explosion of complexity inherent to traditional backstepping, while integrating integral action into the design results in the elimination of the steady-state tracking error. Besides, a composite learning method optimizes the update laws for the neural network and disturbance observer weights, enhancing control performance. Despite the presence of uncertainties, the closed-loop system stability is guaranteed by the Lyapunov stability theorem. Simulation results demonstrate the proposed controller's ability to handle severe actuator faults, unmodeled dynamics, and measurement noise without requiring explicit fault detection and isolation schemes. [ABSTRACT FROM AUTHOR]

In recent years, virtual coupling (VC) has been becoming a hot research topic in railways. The coordinative control for the virtually coupled train set (VCTS) is a critical technology for realising VC. In this paper, distributed adaptive coordinative control of the VCTS with considering parametric uncertainty and practical constraints is studied. To begin with, a third-order nonlinear VCTS train-following model is formulated by considering the uncertain resistance in the railway external environment and the actuator delay in the traction/braking system of unit trains. After that, a novel barrier functions-based third-order sliding mode control method is proposed to realise all states tracking control as well as to satisfy the state constraints (i.e. spacing and speed constraints). Adaptive control technology is used to alleviate the parametric uncertainty. Furthermore, by combining these techniques, a novel distributed adaptive coordinative control strategy is designed to not only guarantee the stable operation of the VCTS but also effectively handle parametric uncertainty and state constraints. Finally, the effectiveness of the proposed method is validated in simulation analysis. [ABSTRACT FROM AUTHOR]

In this paper, a distributed tube-based model predictive control (TMPC) method is proposed to address the cooperative control of virtually coupled high-speed train (HSTs), which consists of an optimisation control part and a feedback control part. The novelty of this work lies in its pioneering consideration of the inconsistency between the train-to-train (T2T) communication cycle and the train operation control cycle in the cooperative control of virtually coupled trains. Firstly, for the optimisation control part, a distributed MPC scheme is designed based on the nominal train dynamics model to address the cooperative control of trains with different initial states, state constraints and control input constraints. By solving the optimisation control problem of the MPC, the optimal control input sequence and operating state sequence of the train for the next T2T communication cycle can be obtained. Secondly, the recursive feasibility and convergence of the MPC are analysed theoretically. Thirdly, for the feedback control part, an adaptive feedback control law is designed based on the actual train dynamics model, auxiliary dynamic system (ADS) and neural network techniques. In the train operation control cycle, the feedback control part utilises the optimal state sequence from the MPC as a reference for feedback control to deal with the external disturbance and train dynamics modelling inaccuracy. Fourthly, it is theoretically proved that the actual operating state of the train can converge to a small region around the optimal state of the MPC in a finite time. Finally, the effectiveness of the proposed distributed cooperative control scheme is verified by simulations. [ABSTRACT FROM AUTHOR]

Research on initial errors and constraint restrictions is one of the main research directions in the field of control of uncertain robotic systems. An adaptive iterative learning control (AILC) method based on radial basis function (RBF) neural network is proposed to address the trajectory tracking problem of the long-stroke hybrid robot system with random initial errors and full state constraints. The RBF neural network is used to approximate the unknown nonlinear terms, and the network weights are updated using an iterative learning law that incorporates a projection mechanism. Additionally, a robust learning strategy is used to compensate for both the approximation error of the neural network and the external disturbances that vary with each iteration. To relax the requirement of traditional iterative learning control (ILC) for identical initial condition, an equivalent error function is constructed based on the time-varying boundary layer. The tangent-type barrier Lyapunov function (BLF) is designed to ensure that the joint position and speed of the robot system are bounded within a predetermined range. Through stability analysis based on barrier composite energy function (BCEF), it can be proved that the boundedness of all signals in the closed-loop system and the tracking error of the robot system will converge to an adjustable residual set asymptotically. Finally, through simulation experiments conducted on the MATLAB platform, the results demonstrate that the method overcomes the random initial errors of the system effectively, ensures that the system satisfies the full-state constraints, and realizes high-precision trajectory tracking. [ABSTRACT FROM AUTHOR]

Interferometric scattering (iSCAT) microscopy enables high‐speed and label‐free detection of individual molecules and small nanoparticles. Here we apply point spread function engineering to provide adaptive control of iSCAT images using spatial light modulation. With this approach, we demonstrate improved dynamic spatial filtering, real‐time background subtraction, focus control, and signal modulation based on sample orientation. LAY DESCRIPTION: In this paper, we apply spatial light modulation to manipulate the illumination properties of an interferometric scattering microscopy. This technique, called iSCAT, can image individual molecules and nanoparticles without the need for fluorescent labels. We added a digital display device that can modulate the pattern and phase of the laser illumination. This allows us to control the light used to make images. By changing how we use the light, we can optimise the contrast of our images, make rapid changes in focus without mechanical movement, and explore the alignment of gold nano rods. [ABSTRACT FROM AUTHOR]

Global, 24/7, and all-weather Synthetic Aperture Radars (SARs) are optimal platforms for Ground Moving Target Indication (GMTI) missions, and the tether constraint provides a stable mechanic connection for such configurations. To fulfill the requirements of such missions, the Space Tether System (STS) must be deployed to horizontal positions to form the necessary along-track interference baseline, which is unstable relative to traditional vertical positions and has not received adequate focus. To deal with this problem, this study focuses on the deployment control of the STS to the unstable horizontal positions. Firstly, the properties of the STS at the horizontal position are analyzed, and a synthetic criterion of measurement error is defined based on the observation principle of the GMTI mission. Secondly, two deployment control strategies are proposed, and corresponding desired trajectories are generated by considering two occasions respectively. In the end, considering the instability of horizontal positions, an adaptive closed-loop controller is designed utilizing the backstepping method to address gravitational moment and other disturbances. Simulations demonstrate that the system can successfully attain the desired horizontal positions under both deployment strategies, and the designed controller can quickly track trajectories under initial state errors and external disturbances. • Two along-track deployment strategies are proposed for unstable horizontal positions. • A synthetic criterion of measurement error is defined to evaluate deployment accuracy. • A back-stepping adaptive controller is designed to suppress unstable tether oscillations. [ABSTRACT FROM AUTHOR]

Traditional vector control strategies cannot eliminate the disturbances existing in permanent magnet synchronous motor (PMSM) systems while maintaining their dynamic performances. To address this issue, this paper proposes an adaptive continuous twisting (ACT) control method to further improve the control accuracy and anti-interference ability of the PMSM system. Firstly, a continuous twisting controller is designed to generate a continuous signal, which is utilised in the speed loop. It can guarantee that the tracking error of the speed regulation system finite-time converges to zero. This not only enhances the robustness of PMSM system but also effectively weakens the control chattering. Secondly, on this basis, an ACT controller is constructed to handle the disturbances with unknown bounds in PMSM systems. Finally, simulation and experimental results show that the proposed control method can improve the performances of PMSM system. [ABSTRACT FROM AUTHOR]

Measurement error frequently distorts outcomes in real-world applications, affecting process results. This paper explores how measurement error affects the detection capabilities of a new adaptive control chart with variable sample size in identifying out-ofcontrol conditions. The analysis is based on a model incorporating linear covariates, focusing specifically on the variable sample size with the EWMA chart's ability to detect shifts in the mean. Our findings demonstrate that measurement error considerably influences the chart's performance concerning mean detection. Additionally, we show that the proposed variable sample size strategy outperforms traditional methods under these conditions. [ABSTRACT FROM AUTHOR]

Gimbal systems serve as critical mechanisms for carrying payloads and accurately aligning them with specific lines of sight. Designing controllers for these gimbal systems necessitates robustness against external disturbances and variations in system parameters. Various control methods have been explored in existing literature to address this challenge. In the scope of this study, we investigate the performance of four distinct control strategies—Proportional-Integral (PI) control, Self-Tuning PI control, Integral Action LinearQuadratic regulator (IA-LQR), and Relay Sliding Mode Control based on the Input-Output Model (RSMCIO)—as speed controllers for a gimbal system. We assess their efficacy through both simulation-based experiments and real-world applications, offering a comprehensive analysis of their outcomes. Beyond speed control, we extend our investigation to include cascade position control, employing a ProportionalDerivative (PD) control structure in the outer loop. Our study focuses on evaluating the performance of these diverse control structures when subjected to disturbances and variations in model parameters. To the best of our knowledge, this research marks the first attempt to apply the RSMC-IO approach to gimbal systems, enhancing our understanding of control strategies in this context and offering valuable insights into their practical implications. [ABSTRACT FROM AUTHOR]

Background and Objectives: The two-axis inertially stabilized platforms (ISPs) face various challenges such as system nonlinearity, parameter fluctuations, and disturbances which makes the design process more complex. To address these challenges effectively, the main objective of this paper is to realize the stabilization of ISPs by presenting a new robust model-free control scheme. Methods: In this study, a robust adaptive fuzzy control approach is proposed for two-axis ISPs. The proposed approach leverages the backstepping method as its foundational design mechanism, employing fuzzy systems to approximate unknown terms within the control framework. Furthermore, the control architecture incorporates a model-free disturbance observer, enhancing the system's robustness and performance. Additionally, novel adaptive rules are devised, and the uniform ultimate boundedness stability of the closed-loop system is rigorously validated using the Lyapunov theorem. Results: Using MATLAB/Simulink software, simulation results are obtained for the proposed control system and its performance is assessed in comparison with related research works across two scenarios. In the first scenario, where both the desired and initial attitude angles are set to zero, the proposed method demonstrates a substantial mean squared error (MSE) reduction: 96.2% for pitch and 86.7% for yaw compared to the backstepping method, and reductions of 75% for pitch and 33.3% for yaw compared to the backstepping sliding mode control. In the second scenario, which involves a 10-degree step input, similar improvements are observed alongside superior performance in terms of reduced overshoot and settling time. Specifically, the proposed method achieves a settling time for the pitch gimbal 56.6% faster than the backstepping method and 58% faster for the yaw gimbal. Moreover, the overshoot for the pitch angle is reduced by 53.5% compared to backstepping and 35.5% compared to backstepping sliding mode control, while for the yaw angle, reductions of 43.6% and 37.6% are achieved, respectively. Conclusion: Through comprehensive simulation studies, the efficacy of the proposed algorithm is demonstrated, showcasing its superior performance compared to conventional control methods. Specifically, the proposed method exhibits notable improvements in reducing maximum deviation from desired angles, mean squared errors, settling time, and overshoot, outperforming both backstepping and backstepping sliding mode control methods. [ABSTRACT FROM AUTHOR]

With intelligent manufacturing development, applying adaptive control technology in the machining process is an effective way to increase productivity and quality. However, adaptive control alone cannot control cutting forces effectively when cutting conditions have excessive change. In this study, a digital twin of the milling process is introduced to cutting force adaptive control for system robustness and efficiency. The cutting force is indirectly measured based on the feed drive current using a Kalman filter, and unknown parameters in the estimation model are identified. A virtual machining system model is established based on online data communication and geometric operation. In addition, the machining state is predicted and introduced into the adaptive control algorithm based on the integrated digital twin for cutting force constraint control. Finally, rough milling of an S-shape specimen is carried out as the cutting experiment to verify the credibility and efficiency of the digital twin-driven cutting force adaptive control. [ABSTRACT FROM AUTHOR]

Introduction. This study presents a robust control method for the path following problem of the PUMA560 robot. The technique is based on the Adaptive Fuzzy Type-2 Fast Terminal Sliding Mode Control (AFT2FTSMC) algorithm and is designed to handle actuator faults, uncertainties (such as payload change), and external disturbances. The aim of this study is to utilize the Fast Terminal Sliding Mode Control (FTSMC) approach in order to ensure effective compensation for faults and uncertainties, minimize tracking error, reduce the occurrence of chattering phenomena, and achieve rapid transient response. A novel adaptive fault tolerant Sliding Mode Control (SMC) approach is developed to address the challenges provided by uncertainties and actuator defects in real robotics tasks. Originality. The present work combined the AFT2FTSMC algorithm in order to give robust controllers for trajectory tracking of manipulator’s robot in presence parameters uncertainties, external disturbance, and faults. We use an adaptive fuzzy logic system to estimate the robot’s timevarying, nonlinear, and unfamiliar dynamics. A strong adaptive term is created to counteract actuator defects and approximation errors while also guaranteeing the convergence and stability of the entire robot control system. Novelty. The implemented controller effectively mitigates the chattering problem while maintaining the tracking precision and robustness of the system. The stability analysis has been conducted using the Lyapunov approach. Results. Numerical simulation and capability comparison with other control strategies show the effectiveness of the developed control algorithm. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of human exploration of the natural world, there has been a growing demand across various fields for robots capable of free movement in diverse environments. In this study, we address the issue of compliant control for a hexapod robot in diverse environments and propose a novel control method based on an adaptive impedance model for position control. Our approach enables the hexapod robot to stabilize foot force on complex terrains while preserving balance and body height. Specifically, we analyze the algorithm's parameters and stability by establishing the hexapod robot's structural and impedance control models. To tackle this challenge, we introduce an adaptive impedance control algorithm that estimates environmental parameters using Lyapunov's asymptotic stability theorem and achieves tracking of actual foot‐end forces to desired foot forces. Furthermore, to ensure body stability and height, we incorporate attitude feedback and body feedback. Experimental results from foot force control experiments conducted on a multilegged robot demonstrate that our proposed algorithm enhances the adaptability and robustness of the robot. This research holds significant implications for the stable control of hexapod robots in complex environments and has promising practical applications. [ABSTRACT FROM AUTHOR]

Despite various proposed control schemes for uncertain bilateral teleoperation systems under time delays, optimally restricting the system's overshoot has remained an overlooked issue in this realm. For this aim, we propose two novel control architectures based on robust L1 theory, entitled position‐based adaptive L1controller and transparent adaptive L1controller, with the former focusing on position synchronization and the latter concerning system transparency. Since developing L1‐based controllers for nonlinear telerobotic systems encompassing uncertainty and round‐trip delays puts significant theoretical challenges forward, the main contribution of this paper lies in advancing L1 theory within the field of delayed teleoperation control. To formulate the theories, the asymptotic stability of the closed‐loop system for each controller is first proved utilizing the Lyapunov method, followed by transformation, along with the L1 performance criterion, into linear matrix inequalities. Ultimately, the control gains are attained by solving a convex optimization problem. The superiority of the designed controllers over a benchmark transparent controller for teleoperators is demonstrated via simulation. Furthermore, experimental tests on a two‐degrees‐of‐freedom nonlinear telerobotic system validate the efficient performance of the proposed controllers. [ABSTRACT FROM AUTHOR]

The structural response under earthquake excitation can be simulated by shake table tests. However, the performance of the shake table is affected by the Control‐Structure Interaction (CSI) effect. In recent years, nonlinear control algorithms were developed to compensate for the CSI effect. In this study, a model reference adaptive control algorithm, named model reference adaptive hierarchical control (MRAHC) framework, is presented. MRAHC consists of a high (adaptive) and low (loop‐shaping) level controller. The high‐level (adaptive) controller develops the control algorithm on the system level, which directedly considers the inherent nonlinearity of the test specimen and the CSI effect. While the low‐level (loop‐shaping) controller develops the control algorithm to regulate the hydraulic system and make sure it can follow the reference signal generated by the high‐level (adaptive) controller. MRAHC offers many advantages including direct compensation to the structural nonlinearity and the ability to handle the CSI effect. In addition, it allows users to quantify the mass of the test specimens without measurement. To evaluate the performance of the MRAHC method, shake table tests with different upper structure masses were carried out. The performance of the MRAHC was compared with the direct loop‐shaping control method (LC) and the Proportional‐Integral‐Differentiation control method (PID). The results show that the MRAHC can achieve better acceleration tracking compared to the LC and PID control methods. Hence, the MRAHC can be used as an effective nonlinear controller for shake table tests. [ABSTRACT FROM AUTHOR]

Gas Metal Arc Welding (GMAW) is a critical process in manufacturing, known for its efficiency and versatility. The double-electrode GMAW (DE-GMAW) technique further enhances these attributes, offering superior welding speed and improved melting effects. However, controlling the DE-GMAW process effectively remains a complex challenge due to the nonlinear and dynamic nature of the system. The process involves intricate interactions between electrical, thermal, and mechanical phenomena, resulting in highly nonlinear behavior. Variations in material properties, environmental conditions, and external disturbances can adversely affect the welding process. Moreover, traditional control methods often fail to account for unmodeled dynamics and modeling errors, leading to performance degradation and potential instability. To address these challenges, this paper introduces a robust adaptive control scheme tailored for DE-GMAW systems, which combines online projection estimation identification and pole placement strategy at the same time to compensate for parameter uncertainties, external disturbances, and unmodeled dynamics. Simulation examples in welding process are carried out to demonstrate the effectiveness of the proposed robust adaptive control scheme. • A double-input double-output for the DE-GMAW system is put forward to consider the HM and the nonlinearity effects firstly, thereby better reflecting the engineering requirements. • A novel robust adaptive control combining pole assign self-tuning control and online projection estimation is first employed to deal with the modeling error of the nonlinear DE-GMAW. • Sufficient conditions are developed to demonstrate the nonlinear closed-loop system is bounded-input bounded-output stable. [ABSTRACT FROM AUTHOR]

For a multi-axis nonlinear milling system with piezoelectric actuators, a pseudo inverse dynamic surface active control scheme with adaptive fuzzy output-feedback is developed. The main contributions can be characterized as following: (1) to overcome the hysteresis non-linearities in the piezoelectric actuators, the new hysteresis pseudo inverse compensators are established to replace the conventional hysteresis direct-inverse model construction, which is a very challenging or even impossible work. The hysteresis pseudo inverse means that the hysteresis direct-inverse model is no longer essential any more, instead, a search algorithm for the actual control signal from the designed hysteretic temporary control laws are proposed to alleviate the hysteresis nonlinearities effectively; (2) to realized high-precision tracking control and mitigate chattering suppression in the micromilling systems, the fuzzy logic systems, the novel dynamic surface control algorithms and the modified high-gain states observer are designed to approximise the unknown continuous functions of the micromilling systems, design the high performance controller and estimate the immeasurable states in the control system, respectively; (3) the hardware-in-loop simulation platform is constructed and the experiments of tracking control and chattering suppression are implemented to validate the effectivity of the developed control scheme. [ABSTRACT FROM AUTHOR]

In petroleum drilling engineering, self-synchronous vibration screen with single-frequency actuation is difficult to implement the scheduled synchronous behavior caused by its invariance and instability of dynamic characteristics, which usually leads to reduction of the production efficiency. Hence, to solve the problem mentioned above, a dual-frequency counter-rotating synchronization control method among three exciters is presented by combining adjacent cross-coupling control (ACCC) structure with global sliding mode control (GSMC) theory. Firstly, motion differential equation of the vibration system in each direction is derived according to mathematical model of the tri-rotor vibration machine. Secondly, considering exponential reaching law and adaptive control algorithm, the adjacent cross-coupling controller and the vector controller of each induction motor are investigated to achieve the stable zero phase difference synchronization between double-frequency rotors. Subsequently, Runge-Kutta method is introduced to establish the electromechanical coupling control model and prove validity of the control theory. Meanwhile, it is also discussed that the rotors cannot obtain an ideal self-synchronization state since velocity of high-frequency motor is not strictly equal to twice that of low-frequency motor. Finally, based on the experimental test scheme, an experimental prototype related to controlled multi-rotor synchronization vibration system is designed independently, which further verifies the validity of theory and computer simulation. Research shows that the desirable synchronous state of the vibration system can be accurately controlled via the designed control strategy, and the control system can implement diversity of the dynamical characteristics, due to the existence of strong robustness. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of dynamic event-triggered predefined-time adaptive attitude control of quadrotor unmanned aerial vehicle (QUAV) suffering from unknown deception attacks. The command filter is utilized to avoid the "explosion of complexity" problem, while concurrently eliminating the effect of filtered error by constructing the fractional power error compensation signals. By using the Nussbaum gain technique, the unknown control coefficients generated by unknown deception attacks have been resisted. A dynamic event-triggered predefined-time attitude control scheme is proposed by introducing internal dynamic variables, which reduces the data transmissions and avoids the Zeno behavior. It is proved that the closed-loop system is practically predefined-time stable, and the attitude of QUAV is driven into a small region near the origin in a predefined time. Finally, a simulation example is provided to show the effectiveness and superiority of the developed predefined-time attitude control algorithm. • The proposed predefined-time control algorithm of QUAV presets the upper bound of settling time and merely need to adjust one design parameter. • The presented design approach based on predefined-time CFB framework can remove the effect of filtered error in a predefined time. • A DETM predefined-time control scheme of QUAV is proposed, which can adjust event thresholds to reduce triggered numbers and avoid Zeno behavior. [ABSTRACT FROM AUTHOR]

• Novel adaptive parameter ZNN for synchronizing complex chaotic system is proposed. • Well-designed activation function to ensure fast convergence in predefined time. • Adaptive parameters are promoted by fuzzy parameters to enhance noise resistance. • Convergence and robustness of this NAPZNN are presented by theoretical analysis. • FPGA implementation is achieved to further exhibit its engineering potentiality. This article proposes a novel adaptive-parameter zeroing neural network (NAP-ZNN) to control the synchronization of complex chaotic systems. Different from previous zeroing neural networks (ZNNs), the NAP-ZNN model incorporates adaptive-parameter, a unique blend of time-varying convergence factor and fuzzy control parameter, which is adjusted continuously according to the real-time synchronizing error, offering greater flexibility. The NAP-ZNN model aims to address the time-varying problem in complex-valued domain, which is scarcely explored, it rapidly impels two complex chaotic systems to synchronize within predetermined time and effectively mitigates noise interference. Moreover, the upper bound of the convergence time is theoretically calculated in noiseless environments, and the disturbance-resistant property is demonstrated under various noisy disturbances. The control performance of NAP-ZNN model has been separately compared to other general ZNNs (NAP-ZNN synchronizes at 0.097 s while three general ZNNs spend 0.43 s, 0.78 s and 0.84 s, and only NAP-ZNN complete synchronization within 0.1 s under noisy interferences while general ZNNs fail) and the traditional adaptive control method through two synchronization simulation (RMSE of NAP-ZNN under sinusoidal noise is 3.165 × 10-3 while traditional is 3.383 at t = 1 s). The numerical outcomes clearly illustrate the superior performance of the NAP-ZNN model in comparison to other synchronization strategies. The hardware implementation of the NAP-ZNN model has been successfully realized through the Field Programmable Gate Array (FPGA), and the chaos synchronization process has been vividly captured via an oscilloscope, further reinforcing the practicality and effectiveness of the NAP-ZNN model. [ABSTRACT FROM AUTHOR]

Autonomous vehicle steering control is sensitive to the vehicle driving speed and traditional model-based approaches are limited by the accuracy of the control model in various driving speed scenarios. To address these challenges, this study proposes a model-free control strategy based on deep reinforcement learning (DRL). In this strategy, the improved double deep Q-learning network (DDQN) with varied agents is employed for steering control to minimize the tracking errors across varying speeds. According to the kinematic characteristics of the vehicle, a dynamic action space is applied to enhance the tracking capability at high speeds. Furthermore, to ensure the output of the agent is more stable, a velocity adaptive reward function is designed by incorporating an action penalty factor. The performance of the proposed strategy is evaluated through simulation and experimental comparisons with other existing algorithms at a double-lane change maneuver. The results demonstrate that the DDQN-based strategy can effectively adapt to various vehicle speeds and perform the tracking task more accurately and stably. Finally, the feasibility of this strategy is verified using an actual prototype vehicle. [ABSTRACT FROM AUTHOR]

Electrified drive systems and eco-driving technologies play a crucial role in promoting energy conservation. Eco-driving for hybrid electric vehicles(HEVs) is an intricate problem involving intertwined speed planning and energy management. In this context, an ecological adaptive cruise control (eco-ACC) and powertrain energy management strategy considering Signal Phase Timing Message (SPaT) can enhance both performance and real-time implementation. Specifically, this study develops a novel co-optimization method based on Pontryagin's minimum principle (PMP) that combines car-following control rules with the SPaT for a parallel HEV. The methodology involves the following steps: firstly, the parallel HEV model is established; secondly, the safe following distance model is constructed and the car-following control rules are devised to ensure safe driving. Subsequently, the co-optimization method based on PMP is then presented to simultaneously optimize the eco-driving problem of an ego-vehicle by converting the inter-vehicle distance constraint of the lead-vehicle into the limitation of the speed of the ego-vehicle. Finally, simulations are conducted under different scenarios for both fused SPaT and non-fused SPaT strategy. The simulation results demonstrate a reduction in fuel consumption by 6.27 % and 5.69 % in two different scenarios, respectively, and a shorter driving time for the fused SPaT strategy compared to the non-fused SPaT strategy. • A novel car-following rules is proposed to ensure its safety. •An eco-ACC and energy management strategy is proposed with SPAT by combining the car-following control rules with the PMP. •The PMP is used to derive the explicitly optimal analytical solution of the eco-driving problem under signalized intersections. •The gearshift behavior of parallel HEV is considered by incorporating a single-parameter shift law. •Different methods are compared to validate the proposed method. [ABSTRACT FROM AUTHOR]

Hybrid energy storage plays a critical role in primary frequency regulation during large-scale renewable energy integration. Rational power distribution between multiple types of energy storage, as well as the use of a VSG control technique, are effective approaches to improving primary frequency regulation capability. To that end, this paper presents an adaptive Virtual Synchronous Generator (VSG) characteristics and state of charge (SOC) management technique for photovoltaic (PV) - hybrid energy storage system (HESS) primary frequency regulation. First, a power distribution mechanism for HESS on the direct current side is implemented. Second, a primary frequency control strategy is proposed based on adaptive rotational inertia and damping coefficient of VSG and SOC regulation of energy storage. Finally, a simulation analysis confirms the feasibility of the proposed primary frequency control strategy under transient conditions. The relevant results are presented as follows. Compared to traditional control strategies, the improved adaptive VSG parameter and energy storage SOC control strategy reduces the overshoot and adjustment time of VSG active power and frequency response by 68.57%, 23.94%, 19.05% and 9.80%, respectively. The decline in SOC of battery energy storage is decreased by 3.18%. The proposed control strategy also limits grid frequency fluctuation within ±0.2 Hz, with a recovery time of less than 0.5 s. The overall root mean square error of frequency f RMSE is a maximum of 0.02 Hz. The primary frequency regulation capability of the PV-HESS is enhanced, ensuring the operational safety of energy storage systems and effectively improving the stability while also improving grid stability and security. • A power distribution control strategy for hybrid energy storage system is introduced. • An adaptive VSG parameters and SOC control strategy for PV-HESS primary frequency regulation is proposed. • The PV and hybrid energy storage primary frequency regulation model is established. [ABSTRACT FROM AUTHOR]

The accuracy of fracture reduction robot is critical factor for clinical application. In this study, a neural network adaptive admittance control (NNAAC) system is proposed to improve the position/force tracking accuracy of the robot under large variable reduction forces and uncertain robot dynamic models. The NNAAC consists of an inner loop and an outer loop. The inner loop combines inverse dynamics control (IDC) and radial basis function neural network (RBFNN) to compensate for inaccuracies in the dynamic model, and an admittance parameter adaptive controller based on the RBFNN in the outer loop improves force tracking accuracy and stability. The performance of the NNAAC is evaluated by fracture model reduction experiments, comparing it with the inverse dynamics control and constant admittance control. The experimental results show that the NNAAC achieves better control effectiveness and high accuracy with a maximum reduction force of 190 N. Specifically, the proposed controller exhibits a maximum position error of less than 1 mm and a maximum orientation error of less than 0.5°. [ABSTRACT FROM AUTHOR]

• Evaluation objectives and influential factors for passenger transportation risk are constructed. • An adaptive optimization method for passenger flow control measures is proposed. • Simulation, data interpolation, and multi-objective optimization are integrated. • Optimal strategies under any passenger flow composition are obtained. Effective passenger flow control measures are essential for the safe operation of metro stations. Existing in-station control measures include adjusting the operation mode of escalators and setting up temporary fences. However, in practice, metro operators often adopt fixed operation modes during fixed periods, indicating that the current passenger flow control measures at metro stations are overly rigidified. Therefore, developing an adaptive control strategy to constantly balance the wildly fluctuating passenger flow and optimize the operation performance is a key issue in current research. In this study, transportation efficiency and congestion risk are selected as evaluation objectives for passenger transportation risk, and passenger flow feature, station structure, and passenger flow control measures are considered key influential factors. Subsequently, an adaptive optimization method integrating simulation and data interpolation is proposed. The software Legion is used to conduct 150 orthogonal simulations, and prediction models for passenger transportation risk are obtained by performing data interpolation on the simulation results. Finally, taking a certain metro station as a case study, the optimal passenger flow control strategy under any passenger flow composition is obtained by scenario acquisition, risk identification, and adaptive decision-making. The results show that setting up temporary fences can reduce the passenger density near the fare gates, while adjusting the running direction of escalators can reduce overcrowding on the platform. Under varying passenger flow composition, the optimal strategy for the current scenario can be obtained, controlling passenger transportation risk within an acceptable range and providing assistance for metro operators in decision-making. [ABSTRACT FROM AUTHOR]