This paper introduces a fully distributed model-free adaptive control method, addressing the tracking problem for multiple-high-speed trains (MHSTs) featuring sensor resolution and utilizing round-robin protocol. Sensor resolution is one of the basic characteristics of almost all sensors and an important indicator of sensor performance in practical applications, but it has not been given enough attention in the study of MHSTs. Since MHSTs have unknown dynamical properties, firstly, the dynamic linearisation technique is used to obtain the data relation description of MHSTs. Secondly, to address the issue of data transmission pressure caused by limited network bandwidth in Train-to-Train (T2T) direct communication, the round-robin protocol can be adopted for effective alleviation. On this basis, a distributed model-free adaptive control strategy is proposed to achieve the bounded formation tracking of MHSTs with sensor resolution under the round-robin protocol. Finally, the effectiveness of the proposed control scheme is verified by simulation examples.

This paper investigates a data-driven finite-time cooperative control problem for nonlinear multi-agent systems (MASs) and develops a distributed model-free adaptive prescribed-performance control strategy based on partial-form dynamic linearization (PFDL). First, a finite-time prescribed performance function is introduced to transform the distributed output errors, ensuring that the prescribed performance constraints are satisfied. Next, an improved dynamic linearization method is derived to convert the transformed error dynamics into an equivalent linear data-driven model, which facilitates controller synthesis. On this basis, a finite-time performance index is constructed and a distributed model-free adaptive cooperative control algorithm is proposed. Moreover, a dual-channel event-triggering mechanism with separate triggering rules for control inputs and measured outputs is designed to reduce the communication burden. Rigorous analysis establishes finite-time convergence of the distributed output errors and boundedness of the control inputs. Numerical simulations demonstrate the effectiveness and robustness of the proposed method.

In this article, a novel model-free adaptive fuzzy control (MFAFC) scheme is proposed to tackle the frequency fluctuation issue in complex interconnected power systems under sensor measurement and input saturation constraints. Communication between sensors and controllers is facilitated through a shared network, with the Round-Robin protocol employed to manage data transmission and prevent collisions.First, the Takagi-Sugeno (T-S) fuzzy system with a local nonlinear model is used to represent the multi-area interconnected nonlinear power system. Subsequently, the dynamic linearization technique is employed to describe the data relationships within each subsystem of the T-S fuzzy power system. Based on this data model, an model free adaptive fuzzy control algorithm is developed, and the convergence of tracking errors is thoroughly analyzed. To assess the control performance, a three-area interconnected power system is selected as the test case. Simulation results demonstrate that the MFAFC scheme effectively mitigates frequency fluctuations, showcasing its robust tracking performance and reliability.

This study addresses the challenge of precise control of the maglev system under the condition of rapid time-varying dynamics and large time delay. Maglev technology has been applied in many fields, such as maglev train and magnetic bearing, for its non-contact operation, low maintenance cost and high energy efficiency. However, the inherent nonlinear characteristics and delayed response characteristics of the magnetic levitation system make it very difficult to control. In practical scenarios, it is difficult to establish an accurate magnetic suspension system model, which limits the use of model-based control methods. PID control method is also difficult to guarantee the control performance when dealing with unmeasurable load disturbance and system delay scenarios. This study proposes a data-driven control solution, which integrates an optimized recursive identification algorithm and an adaptive delay compensation algorithm. Uniquely, the control solution achieves real-time iterative optimization with only a few input and output data, reducing the data storage resource requirements, avoiding offline training, and ensuring real-time performance and efficient resource utilization in practical applications. Practitioners can use this new method to enhance the performance and reliability of maglev applications, such as maglev trains, industrial processes and precision instruments, so as to achieve more robust and adaptive control in the face of rapid dynamic changes and large time delays. Note to Practitioners—This study addresses the challenge of precise control of the maglev system under the condition of rapid time-varying dynamics and large time delay. Maglev technology has been applied in many fields, such as maglev train and magnetic bearing, for its non-contact operation, low maintenance cost and high energy efficiency. However, the inherent nonlinear characteristics and delayed response characteristics of the meglev system make it very difficult to control. In practical scenarios, it is difficult to establish an accurate magnetic suspension system model, which limits the use of model-based control methods. PID control method is also difficult to guarantee the control performance when dealing with unbearable load disturbance and system delay scenarios. This study proposes a data-driven control solution, which integrates an optimized recursive identification algorithm and an adaptive delay compensation algorithm. Uniquely, the control solution achieves real-time iterative optimization with only a few input and output data, reducing the data storage resource requirements, avoiding offline training, and ensuring real-time performance and efficient resource utilization in practical applications. Practitioners can use this new method to enhance the performance and reliability of maglev applications, such as maglev trains, industrial processes and precision instruments, so as to achieve more robust and adaptive control in the Phase of rapid dynamic changes and large time delays.

The actuator faults and channel fading are unavoidable in the nonlinear continuous-time MIMO multiagent systems (MASs), which significantly complicate the formation control problem. To tackle these challenges, a model-free adaptive fault-tolerant formation control (MFAFTFC) scheme based on the sampled-data full-form dynamic linearization (SD-FFDL) technology is proposed, which integrates the data-based formation control algorithm, the fuzzy neural network algorithm, and the projection algorithm. The stability analysis of MFAFTFC scheme is strictly provided. Simulation comparison results with unmanned ground vehicle demonstrate the effectiveness of the proposed MFAFTFC scheme, that is, the proposed MFAFTFC scheme can achieve the formation objective for the nonlinear continuous-time MIMO MASs subjected to actuator faults and fading channels.

In this paper, an enhanced model-free adaptive control algorithm considering time delay is proposed for the path tracking problem of autonomous vehicles. First, a path tracking mechanism based on the preview-deviation-yaw angle is proposed, which transforms the path tracking problem into a control problem of the preview-deviation-yaw angle. A novel partial form dynamic linearization (PFDL) technique is then employed to transform the vehicle dynamic models into a discrete-time data model with a time-varying pseudogradient (PG), and the proposed controller (PFDL-EMFAC) is designed based on this data model. Moreover, a compensation mechanism is designed for the system time delay by combining the Smith predictor and tracking differentiator (TD). Notably, implementing the controller does not involve any model information; it is a purely data-driven control method. Furthermore, the convergence of the proposed controller is proven via mathematical analysis. The validity of the proposed controller was validated through CarSim-MATLAB cosimulation, and its applicability was verified via the Ankai HFF6668GEV1 autonomous driving platform on a test road in Hefei, China.

This paper investigates the issue of speed tracking and dynamic headway adjustment for multiple subway trains (MSTs) under data dropouts. First, the nonlinear subway train system is transformed into a disturbance-related full-form dynamic linearization (dFFDL) data model. Second, based on the obtained dFFDL data model and the asynchronous event-triggered mechanism, the model-free adaptive event-triggered predictive cooperative control (MFAETPCC) algorithm is proposed, utilizing only the input/output and disturbance data of MSTs to compensate for data dropouts, reduce the communication and computation burden, and ensure control performance. As a result, theoretical analysis reveals that the speed tracking errors of MSTs are bounded and the distances of adjacent trains are kept within a safe range. Finally, the effectiveness of the proposed MFAETPCC algorithm for MSTs is demonstrated by simulation results.

This paper aims to develop the RBFNN-based data-driven fast terminal sliding mode control for nonlinear multiagent systems (MASs) subjected to unknown uncertainties and external disturbances. First, a multiagent system with unknown uncertainties and external disturbances is transformed into a full-form dynamic linearization (FFDL) data model. Then, the RBFNN algorithm is utilized to compensate for the complex unknown nonlinear terms encountered by unknown uncertainties and external disturbances, and the data-driven fast terminal sliding mode control is designed to realize the finite-time consensus tracking and enhance the robustness of MASs. Finally, a simulation test applied to subway trains is conducted to demonstrate that the proposed RBFNN-based data-driven FTSMC algorithm is characterized by high tracking accuracy.

This article proposes a sampled-data fault-tolerant bipartite formation control (SD-FTBFC) algorithm that incorporates the sampling period and historical input and output data for nonlinear continuous-time multi-input-multioutput (MIMO) multiagent systems (MASs) subjected to unknown sensor faults. First, a sampled-data full-form dynamic linearization data model employing the principles of differential and integral mean value theorems is established to address the unknown nonlinearities of the continuous-time MASs. Afterward, a time-varying fault detection threshold is developed to ascertain the occurrence of sensor faults. Subsequently, the unknown sensor faults are approximated by the fuzzy neural network algorithm, and the uncertain parameters of MASs are tackled by the projection algorithm. A rigorous stability analysis for the proposed SD-FTBFC algorithm is thoroughly presented. Ultimately, the simulation outcomes utilizing multiple unmanned ground vehicles confirm the efficacy of the proposed SD-FTBFC algorithm.

In this paper, we consider the need for deployment in the long-distance safe longitudinal formation control task when the connected autonomous vehicle (CAV) platoon is subjected to malicious cyber attacks. To ensure the safe, orderly, stable and efficient driving performance of the vehicle platoon, a novel distributed data-driven control (DDDC) approach for a homogeneous connected autonomous vehicle platoon under false data injection (FDI) attacks is investigated. First, an FDI attacks detection and compensation mechanism is designed to detect whether the received position signals are under attack or not and compensate the attacked position signals. Then, a novel DDDC approach for the vehicle platoon longitudinal formation control is developed by using the compensation data from the designed attack compensation mechanism and a dynamic linearization data model. Theoretical analysis verifies that the proposed DDDC method can ensure the internal stability (IS) and string stability (SS) of the homogeneous platoon subjected to FDI attacks. Finally, the effectiveness and practicality of the proposed DDDC approach are validated through a group of comparative simulations subjected to random FDI attacks of equal frequency and magnitude.Note to Practitioners—This work aims to solve the vehicle platoon long-distance safe longitudinal formation control task subjected to malicious FDI attacks. FDI attacks can achieve their destructive purposes by processing intercepted information and injecting false data into the original information. Existing literature overly relies on a priori knowledge of network attacks, yet in practice it is difficult to capture the true intentions of attackers in advance. For multi-channel V2V communication networks, it is even more important to design a resilient and accurate distributed controller strategy for such unpredictable and specific network attacks. Therefore, this paper proposes a data-driven distributed longitudinal formation control strategy with attack detection and compensation mechanism. The proposed strategy is shown to be able to ensure the safe longitudinal formation control task for the homogeneous CAV platoon suffering from FDI attacks. In addition, the stability of the CAV platoon is then investigated while the attacked signals are detected and cleaned, and it is shown to guarantee the internal stability of a single vehicle and the string stability of the platoon.

In this article, a disturbance observer dynamic linearization (DL)-based model-free adaptive control (MFAC) scheme is proposed for discrete-time nonlinear systems with disturbances and uncertainties. The partial-form-dynamic-linearization-based disturbance observer (PDO) is constructed by applying the DL method to an unknown ideal disturbance observer. An adaptive updating algorithm of the observer gain is derived by minimizing a estimation criterion function. Then, the PDO-based MFAC scheme is formed and its bounded stability is rigorously analyzed using the contraction mapping principle. The proposed scheme is a purely data-driven control method, that is, both the PDO and control system are designed by using only the input/output data of underlying system. A numerical simulation and a vehicle turning experiment are given to verify the effectiveness of the proposed scheme.

This paper investigates a distributed data-driven event-triggered fault-tolerant control for a connected heterogeneous vehicle platoon with sensor faults under the vehicle-to-vehicle (V2V) communication network. First, a sensor fault diagnosis scheme based on the high-gain observer is designed to detect, estimate and compensate for the fault signal. Then, the measurement signals with sensor faults are recovered, and the reconstructed system can be modeled by the full-form dynamic linearization (FFDL) technique. To obtain reliable vehicle data communication with the efficient use of network resources, an event-triggered mechanism based on the formation error is established, and then a distributed data-driven controller can be designed to accomplish the platoon formation control task. Theoretical analysis demonstrates that the proposed distributed event-triggered fault-tolerant control method can realize the task of ensuring the safe formation control of the platoon system under some sensor faults. Finally, a simulation of a platoon with three faulty vehicles is made to verify the effectiveness and real-time performance of the proposed method.

This article investigates the issue of speed tracking and dynamic adjustment of headway for the repeatable multiple subway trains (MSTs) system in the case of actuator faults. First, the repeatable nonlinear subway train system is transformed into an iteration-related full-form dynamic linearization (IFFDL) data model. Then, the event-triggered cooperative model-free adaptive iterative learning control (ET-CMFAILC) scheme based on the IFFDL data model for MSTs is designed. The control scheme includes the following four parts: 1) the cooperative control algorithm is derived by the cost function to realize cooperation of MSTs; 2) the radial basis function neural network (RBFNN) algorithm along the iteration axis is constructed to compensate the effects of iteration-time-varying actuator faults; 3) the projection algorithm is employed to estimate unknown complex nonlinear terms; and 4) the asynchronous event-triggered mechanism operated along the time domain and iteration domain is applied to lessen the communication and computational burden. Theoretical analysis and simulation results show that the effectiveness of the proposed ET-CMFAILC scheme, which can ensure that the speed tracking errors of MSTs are bounded and the distances of adjacent subway trains are stabilized in the safe range.

In this article, an improved model-free adaptive control (iMFAC) is proposed for discrete-time multi-input multioutput (MIMO) nonlinear systems with an event-triggered transmission scheme and quantization (ETQ). First, an event-triggered scheme is designed, and the structure of the uniform quantizer with an encoding-decoding mechanism is given. With the concept of partial form dynamic linearization based on event-triggered and quantization (PFDL-ETQ), a linearized data model of the MIMO nonlinear system is constructed. Then, an improved model-free adaptive controller with the ETQ process is designed. By this design, the update of the pseudo partitioned Jacobean matrix (PPJM) estimates and control inputs occurs only when the trigger conditions are met, which reduces the network transmission burden and saves the computing resources. Theoretical analysis shows that the proposed iMFAC with the ETQ process can achieve a bounded convergence of tracking error. Finally, a numerical simulation and a biaxial gantry motor contour tracking control system simulation are given to illustrate the feasibility of the proposed iMFAC method with the ETQ process.

In this paper, a data-driven event-triggered cooperative control (DD-ETCC) scheme is proposed for the multiple subway trains to realize speed tracking and dynamic headway adjustment under switching topologies. Firstly, a nonlinear subway train system is transformed into a linearization data model, and the complex nonlinear terms caused by mechanical and aerodynamic resistance and operating environment conditions are estimated by projection algorithm. Then, the DD-ETCC scheme based on the linearization data model and the asynchronous event-triggered condition for multiple subway trains with switching topologies is designed. Theoretical analysis shows that the speed tracking errors of multiple subway trains are bounded, and the headway distances of consecutive trains are stabilized in a safe range with the proposed scheme. Finally, the effectiveness of the proposed DD-ETCC scheme of multiple trains is illustrated by subway train simulations.

This paper researches the cooperative control problem for multiple subway trains under the asynchronous data dropouts in both measurement channel and downlink channel. The compensation-based cooperative model free adaptive iterative learning control (cCMFAILC) for the multiple city subway trains (MCSTs) is proposed to avoid deterioration of the control performance due to data dropouts. First, the nonlinear subway train system is transformed into an equivalent dynamic linearization data model to describe the input-output dynamics of the subway train system. Next, the lost data is replaced by the corresponding data of the same time instant in the latest available iteration. And the cCMFAILC is designed to guarantee that the speed tracking errors of MCSTs are bounded along the iteration axis and the headway of neighboring subway trains is stabilized in a safe range. Finally, theoretical analysis and MCSTs simulations verify the validity of the proposed cCMFAILC scheme.

A sampled-data model-free adaptive control (SMFAC) strategy is proposed for continuous-time nonlinear nonaffine systems with input rate constraints. By using differential and integral mean value theorems as two basic mathematic tools, a sampled-data local dynamic linearization method is proposed at first to transform the continuous-time nonlinear nonaffine model into a sampled-data nonlinear affine I/O model, including a linear parametric term affined to the control input and a nonlinear uncertainty term. On this basis, we consequently propose an observer-based SMFAC (ObSMFAC) scheme, including a sampled-data parameter estimator to estimate the unknown partial derivatives and a sampled-data observer to estimate the residual nonlinear uncertainty, respectively. Note that the sampling period is incorporated explicitly in the proposed ObSMFAC which enhances the control performance by reducing its negative influence on the system stability. The constraint on the input rate is also considered in the control law as the transition condition of the input updating algorithms. The convergence of the proposed ObSMFAC is proved by using the contraction mapping principle. The simulation study demonstrates the theoretical results.

In this paper, an improved data-driven point-to-point iterative learning control is proposed for nonlinear repetitive systems where only the system outputs at the multiple intermediate prespecified points are considered. The entire finite time interval is divided into multiple time-subintervals according to the prespecified points. Then a new objective function is designed to generate optimal control inputs over a time-subinterval piecewisely. As a result, the control inputs are updated in a time-subinterval wise using additional input signals from the previous time-subintervals of the same iteration to help improving control performance. By removing the constraints on the unimportant intermediate points, the control system can be designed with additional freedom to achieve a better performance in tracking points of interest. Meanwhile, the proposed approach is data-driven and no process model is required for the control system design and analysis. Both a simulation with nonlinear batch reactor and an experiment with a permanent magnet linear motor servomechanism are provided to demonstrate the effectiveness of the proposed method.

Based on a nonlifted iterative dynamic linearization formulation, a novel data-driven higher order optimal iterative learning control (DDHOILC) is proposed for a class of nonlinear repetitive discrete-time systems. By using the historical data, additional tracking errors and control inputs in previous iterations are used to enhance the online control performance. From the online data, additional control inputs of previous time instants within the current iteration are utilized to improve transient response. The data-driven property of the proposed method implies that no model information except for the I/O data is utilized. The computational complexity is reduced by avoiding matrix inverse operation in the proposed DDHOILC approach due to the nonlifted linear formulation of the original model. The asymptotic convergence is proved rigorously. Furthermore, the convergence property is analyzed and evaluated via three performance indexes. By elaborately selecting the higher order factors, the higher order learning control law outperforms the lower order one in terms of convergence performance. Simulation results verify the effectiveness of the proposed approach.

Macroscopic fundamental diagram (MFD) that describes traffic flow in an urban road network can be used to design perimeter control method to regulate the traffic flow from a macroscopic level. Most of the perimeter control algorithms are regarded as a kind of model-based feedback control method, whose performance is hardly to improve in practice due to the model uncertainty. By noticing the repetitive nature of urban traffic flow, an iterative learning control (ILC) based perimeter control method is proposed for an urban region. Since the repetitive information of the controlled system is fully utilized, an improved tracking performance is guaranteed by theoretical analysis, and simulation results verify the effectiveness of the proposed perimeter control method.

In this paper, an enhanced data-driven optimal terminal iterative learning control (E-DDOTILC) is proposed for a class of nonlinear and nonaffine discrete-time systems. A dynamical linearization approach is first developed with iterative operation points to formulate the relationship of system output and input into a linear affine form. Then, an ILC law is constructed with a nonlinear learning gain, which is a function about the system partial derivative with respect to the time-varying control input. In addition, a parameter updating law is designed to estimate the unknown partial derivatives iteratively. The input signals of the proposed E-DDOTILC are time-varying and updated utilizing not only the terminal tracking error of the previous run but also the input signals of the previous time instants in the current iteration. The proposed approach is a data-driven control strategy and only the I/O data are required for the controller design and analysis. The monotonic convergence and effectiveness of the proposed approach is further verified by both the rigorous mathematical analysis and the simulation results.

In this work, a new iterative feedback tuning approach is proposed to tune ALINEA's controller gain automatically when there is not enough prior information available to select a proper feedback gain of ALINEA. It is a data-driven method and the ALINEA controller is auto-tuned only depending on the input and output data collected from closed-loop experiments. To mimic a real traffic environment, a simulator is built on the PARAMICS platform. The flow-based ALINEA controller is also considered to illustrate the good tuning performance of IFT comprehensively. The effectiveness of the proposed methods is verified through PARAMICS based simulations.

Most of the available results of adaptive iterative learning control (AILC) hitherto have considered the control systems with known linearly parameterized structures. A dynamical linearization approach is developed for a general nonlinear multiple input multiple output systems. And then a discrete-time adaptive ILC approach is presented to deal with the ILC problems of nonlinear MIMO systems with iteration-varying initial error and reference trajectory. The controller design and analysis is completely data-driven without using any modeling information of the plant, but the measured I/O data only. The almost perfect tracking performance is asymptotically guaranteed by rigirous mathematical analysis.

In this paper, an iterative learning control approach is developed for a class of uncertainty nonlinear discrete-time systems based on the identification of the controlled system. At first, the linearized model of the nonlinear system is proposed. And then using the identification method, we present an indirect iterative learning control scheme for the controlled system. Analysis shows that the scheme can guarantee the system convergence under some conditions.

In this paper, a data-driven model-free adaptive control (MFAC) approach is proposed based on a new dynamic linearization technique (DLT) with a novel concept called pseudo-partial derivative for a class of general multiple-input and multiple-output nonlinear discrete-time systems. The DLT includes compact form dynamic linearization, partial form dynamic linearization, and full form dynamic linearization. The main feature of the approach is that the controller design depends only on the measured input/output data of the controlled plant. Analysis and extensive simulations have shown that MFAC guarantees the bounded-input bounded-output stability and the tracking error convergence.