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This paper studies the bipartite containment tracking problem for a class of nonlinear multi-agent systems (MASs), where the interactions among agents can be both cooperative or antagonistic. Firstly, by the dynamic linearization method, we propose a novel model-free adaptive iterative learning control (MFAILC) to solve the bipartite containment problem of MASs. The designed controller only relies on the input and output data of the agent without requiring the model information of MASs. Secondly, we give the convergence condition that the containment error asymptotically converges to zero. The result shows that the output states of all followers will converge to the convex hull formed by the output states of leaders and the symmetric output states of leaders. Finally, the simulation verifies the effectiveness of the proposed method.

This study uses a three-axis Gimbal model to inertially stabilize a platform that can be used to feed smooth images from a camera. In this article, three-axis Gimbal performance analysis is presented. An inertial measurement unit responds to movement and a three-phase brushless DC motor with 14 poles and 12 coils is used to rule out vibrations and movement from the surroundings. The controller combines sliding-mode control and model-free-adaptive control to design a novel control method based on data, which can decrease the computational time and difficulty of a nonlinear system. Simulations on MATLAB prove the efficiency of the given method. The simulation results validate that the designed controller has improved position control than the traditional proportional integral derivative, model-free-adaptive control, and model-free-learning-adaptive control.

This paper presents a scheme for the feedforward–feedback longitudinal trajectory tracking control of buses. The scheme is specifically designed to address the periodic and repetitive nature of bus operations. First, the vehicle’s longitudinal dynamics are linearized along the iterative axis via full-form dynamic linearization (FFDL), and parameters such as the pseudo-gradient are estimated with data and a projection algorithm to grasp the dynamic characteristics of the system. To better handle complex real-world traffic conditions, we then propose the forward and backward structure. At the same time, the iterative axis design performance index is verified, and the forward partial control law, namely, model-free adaptive iterative learning control (MFAILC), is derived. In order to further enhance the robustness to disturbance and other factors, the control law of the feedback part is designed with active disturbance rejection control (ADRC). A key advantage of this control approach is its sole reliance on the data generated during vehicle operation, without the need for specific information about the controlled vehicle. This feature enables the method to be adaptable to different vehicle types and resilient to various disturbances. Finally, MATLAB simulations are used to verify the practicality of the proposed method.

For multi-input multi-output (MIMO) nonlinear discrete-time bipartite formation multiagent systems (BFMASs) performing trajectory tracking tasks with unknown dynamics, a dynamic event-triggered model-free adaptive iterative learning control (DET-MFAILC) algorithm is proposed to address periodic denial-of-service (DoS) attacks. First, using the pseudo-partial derivative, a compact format dynamic linearization (CFDL) method is employed to construct an equivalent CFDL data model for the MIMO multi-agent system. A DoS attack model and its corresponding compensation algorithm are developed, while a dynamic event-triggered condition is designed considering both the consensus error and the tracking error. Subsequently, the proposed DoS attack compensation algorithm and the dynamic event-triggered mechanism are integrated with the model-free adaptive iterative learning control algorithm to design a controller, which is further extended from fixed-topology systems to time-varying topology systems. The convergence of the control system is rigorously proven. Finally, simulation experiments are conducted on bipartite formation multi-agent systems (BFMASs) under fixed and time-varying communication topologies. The results demonstrate that the proposed algorithm effectively mitigates the impact of DoS attacks, reduces controller updates, conserves network resources, and ensures that both the tracking error and consensus error converge to an ideal range close to zero within a finite number of iterations while maintaining a good formation shape.

In this article, a novel hybrid control scheme is proposed for controlling the position of a three-phase brushless direct current (BLDC) motor. The hybrid controller consists of discrete time sliding mode control (SMC) with model free adaptive control (MFAC) to make a new data-driven control (DDC) strategy that is able to reduce the simulation time and complexity of a nonlinear system. The proposed hybrid algorithm is also suitable for controlling the speed variations of a BLDC motor, and is also applicable for the real time simulation of platforms such as a gimbal platform. The DDC method does not require any system model because it depends on data collected by the system about its Inputs/Outputs (IOS). However, the model-based control (MBC) method is difficult to apply from a practical point of view and is time-consuming because we need to linearize the system model. The above proposed method is verified by multiple simulations using MATLAB Simulink. It shows that the proposed controller has better performance, more precise tracking, and greater robustness compared with the classical proportional integral derivative (PID) controller, MFAC, and model free learning adaptive control (MFLAC).

Wire arc additive manufacturing (WAAM) is a Direct Energy Deposition (DED) technology, which utilize electrical arc as heat source to deposit metal material bead by bead to make up the final component. However, issues like the lack of assurance in accuracy, repeatability and stability hinder the further application in industry. Therefore, a Model Free Adaptive Iterative Learning Control (MFAILC) algorithm was developed to be applied in WAAM process in this study. The dynamic process of WAAM is modelled by adaptive neuro fuzzy inference system (ANFIS). Based on this ANFIS model, simulations are performed to demonstrate the effectiveness of MFAILC algorithm. Furthermore, experiments are conducted to investigate the tracking performance and robustness of the MFAILC controller. This work will help to improve the forming accuracy and automatic level of WAAM.

This article proposes a model free position-force control method for uncertain environmental constrained reconfigurable manipulators based on adaptive dynamic programming algorithm. Through the analysis of kinematic uncertainties, an adaptive estimation scheme is designed to obtain the approximate contacted torque. The contacted torque is generated due to the interaction of the manipulator’s end-effector with the uncertain environment. Then, the performance index function is defined by utilizing the joint position, contacted torque tracking errors and uncertain environmental factors. The presented neural network-based observer is utilized to learn the dynamic model. On the basis of policy iteration algorithm, the corresponding Hamiltonian–Jacobi–Bellman equation is addressing by employing the critic NN structure. Thus, the model free position-force control strategy is obtained. Based on Lyapunov stability theorem, the tracking error of the reconfigurable manipulator is proved to be ultimately uniformly bounded. Eventually, simulations and experiments are illustrated the effectiveness of the developed controller.

A hierarchical learning control framework (HLF) has been validated on two affordable control laboratories: an active temperature control system (ATCS) and an electrical rheostatic braking system (EBS). The proposed HLF is data-driven and model-free, while being applicable on general control tracking tasks which are omnipresent. At the lowermost level, L1, virtual state-feedback control is learned from input–output data, using a recently proposed virtual state-feedback reference tuning (VSFRT) principle. L1 ensures a linear reference model tracking (or matching) and thus, indirect closed-loop control system (CLCS) linearization. On top of L1, an experiment-driven model-free iterative learning control (EDMFILC) is then applied for learning reference input–controlled outputs pairs, coined as primitives. The primitives’ signals at the L2 level encode the CLCS dynamics, which are not explicitly used in the learning phase. Data reusability is applied to derive monotonic and safely guaranteed learning convergence. The learning primitives in the L2 level are finally used in the uppermost and final L3 level, where a decomposition/recomposition operation enables prediction of the optimal reference input assuring optimal tracking of a previously unseen trajectory, without relearning by repetitions, as it was in level L2. Hence, the HLF enables control systems to generalize their tracking behavior to new scenarios by extrapolating their current knowledge base. The proposed HLF framework endows the CLCSs with learning, memorization and generalization features which are specific to intelligent organisms. This may be considered as an advancement towards intelligent, generalizable and adaptive control systems.

Lower-body exoskeleton control that adapts to users and provides assistance-as-needed can increase user participation and motor learning and allow for more effective gait rehabilitation. Adaptive model-based control methods have previously been developed to consider a user’s interaction with an exoskeleton; however, the predefined dynamics models required are challenging to define accurately, due to the complex dynamics and nonlinearities of the human-exoskeleton interaction. Model-free deep reinforcement learning (DRL) approaches can provide accurate and robust control in robotics applications and have shown potential for lower-body exoskeletons. In this paper, we present a new model-free DRL method for end-to-end learning of desired gait patterns for over-ground gait rehabilitation with an exoskeleton. This control technique is the first to accurately track any gait pattern desired in physiotherapy without requiring a predefined dynamics model and is robust to varying post-stroke individuals’ baseline gait patterns and their interactions and perturbations. Simulated experiments of an exoskeleton paired to a musculoskeletal model show that the DRL method is robust to different post-stroke users and is able to accurately track desired gait pattern trajectories both seen and unseen in training.

Model-Free Adaptive Control (MFAC) is a control strategy that eliminates the need for prior knowledge of the system model by leveraging online data to learn the system dynamics and design controllers. This paper offers a comprehensive exploration of the significance of control theory in unmanned surface vessels (USVs), with a particular focus on data-driven approaches. It provides a comprehensive overview of various MFAC algorithms proposed for USVs in diverse scenarios, including neural network-based MFAC, reinforcement learning-based MFAC, and fuzzy logic-based MFAC. The objective of this review is to provide a profound understanding of the latest advancements in MFAC technologies and serve as a guiding resource for further developments in the field.