Adaptive Tracking Control Research Articles

Adaptive Predefined-Time Tracking Control for Vehicular Platoons With Finite-Time Global Prescribed Performance Independent of Initial Conditions

Adaptive Predefined-Time Tracking Control for Vehicular Platoons With Finite-Time Global Prescribed Performance Independent of Initial Conditions

Adaptive Approximation Tracking Control of a Continuum Robot With Uncertainty Disturbances.

Continuum robot has certain compliance and intrinsic safety, which makes it an excellent substitute for the traditional rigid robot in the various tasks, such as human-robot interaction or medical surgery. However, due to the complex nonlinearities which are induced by the compliance, parameter uncertainties, and unknown disturbances, good performance control scheme for the continuum robot is always a challenging task for the practical applications. Thus, to overcome this challenge of the uncertain dynamics and unknown external disturbances, this article develops a novel adaptive control scheme for a continuum robot using the function approximation technique (FAT). Specifically, for the proposed continuum robot, an adaptive FAT control (AFATC) strategy with no update laws is proposed to handle the uncertain parameters of the robot dynamics and external disturbances. The control law is expressed as a finite linear combination of the orthogonal basis functions by the FAT. The proposed AFATC scheme uses a fixed control structure, and the weight matrices are not updated in time. Then, the stability of the proposed controller is proved based on the Lyapunov function. Afterwards, the simulation results indicate the proposed AFATC scheme has good control performance compared with the regressor-free adaptive control (RFAC) method. Finally, the effectiveness of the proposed AFATC scheme is demonstrated in a group of real-time experiments.

Neural Network-based Robust Adaptive Synchronization and Tracking Control for Multi-Motor Driving Servo Systems

Neural Network-based Robust Adaptive Synchronization and Tracking Control for Multi-Motor Driving Servo Systems

Adaptive Neural Fixed-Time Tracking Control for High-Order Nonlinear Systems.

The problem of adaptive neural fixed-time tracking control for high-order systems is addressed in this article. In order to handle the difficulties from the uncertain nonlinearities within the original systems, the radial basis function neural networks (RBF NNs) are introduced to approximate the unknown nonlinear functions, and the adding a power integrator is applied to overcome the obstacle from high-order terms. It is proven that all signals in the closed-loop system are bounded and the output signal can eventually converge to a small neighborhood of the reference signal. Simulation results further verify the approaches developed.

Adaptive Event-Triggered Practical Tracking Control for a Class of Electrical Flexible-Joint Robotic Manipulators With Input Dead-Zone and Saturation

Adaptive Event-Triggered Practical Tracking Control for a Class of Electrical Flexible-Joint Robotic Manipulators With Input Dead-Zone and Saturation

Adaptive Dynamic Event-Triggered Asymptotic Tracking Control for Strict-Feedback Nonlinear Systems With a Self-Adjusting Performance Function

Adaptive Dynamic Event-Triggered Asymptotic Tracking Control for Strict-Feedback Nonlinear Systems With a Self-Adjusting Performance Function

Adaptive Event-Triggered Exponential Tracking Control of Parallel Robot Manipulators

Adaptive Event-Triggered Exponential Tracking Control of Parallel Robot Manipulators

Dynamic Event-Triggered Adaptive Fixed-Time Practical Tracking Control for Nonlinear Systems Through Funnel Function

Dynamic Event-Triggered Adaptive Fixed-Time Practical Tracking Control for Nonlinear Systems Through Funnel Function

Adaptive Output Feedback Trajectory Tracking Control of an Indoor Blimp: Controller Design and Experiment Validation

Adaptive Output Feedback Trajectory Tracking Control of an Indoor Blimp: Controller Design and Experiment Validation

Adaptive Anti-Disturbance Performance Guaranteed Formation Tracking Control for Quadrotor UAVs via Aperiodic Signal Updating

Adaptive Anti-Disturbance Performance Guaranteed Formation Tracking Control for Quadrotor UAVs via Aperiodic Signal Updating

Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems Under FDI Attacks and Input Saturation: A Flexible Transient Performance Approach.

The present study proposes an adaptive fuzzy tracking control strategy for switched nonlinear systems, capable of effectively addressing false data injection (FDI) attacks and input saturation, while achieving flexible prescribed performance control as well as semi-global uniform ultimate boundness for the resultant system. Compared to the previous work, the provided control strategy exhibits two notable strengths: 1) it introduces a novel modified fixed-time pregiven performance function to effectively balance input saturation and output constraint and 2) the detrimental impacts resulting from FDI attacks are successfully mitigated by implementing the fuzzy logic systems approximation technique in the backstepping procedure. A set of switching fuzzy observers are established to estimate the unobservable states while a first-order differential filter is utilized to handle the complexity explosion problem. Finally, the mass-spring-damper system is given to substantiate the developed approach.

Learning-Observer-Based Adaptive Tracking Control of Multiagent Systems Using Compensation Mechanism

This paper focuses on the adaptive consensus tracking problem for nonlinear multiagent systems (MASs) with unmeasurable state and input saturation. For unmeasurable state and nonlinearities, a learning-observer inspired by the idea of extended state observer (ESO) and the learning ability of neural network (NN) is established to observe the reconstructed system. In view of input saturation, a nonlinear auxiliary system with the same order is constructed to obtain the compensation signals. Different from the previous auxiliary system, the proposed method simultaneously compensates the change of nonlinearity caused by input saturation. Subsequently, a consensus tracking error is designed to ensure that the follower tracks the neighbor compensated output. Moreover, an adaptive consensus tracking control method based on learning-observer and compensation mechanism is proposed under the backstepping framework. The stability analysis is given by virtue of the Lyapunov theory. Finally, the effectiveness and superiority of the proposed control algorithm are verified through the simulation of unmanned air vehicles (UAVs) with one virtual leader and four followers.

Two High Performance Global Tracking Composite Adaptive Controllers for Fully Actuated Euler-Lagrange Systems

Two adaptive global tracking controllers for fully actuated Euler-Lagrange systems with verifiable performance improvement with respect to existing designs are reported in the paper. The controllers are shown to ensure global exponential convergence under a weak interval excitation condition. Besides, one of the proposed controllers has a simple adaptive PID-like structure and—in contrast with the classical solution—its construction avoids additional filtering. We adopt a composite adaptation architecture, invoke a novel parameterization of the system dynamics and use a high performance estimation scheme recently reported in the literature. The validity of our claims is illustrated with extensive simulations of a two degrees-of-freedom robot manipulator operating in a realistic noisy environment.

Adaptive Prescribed-Time Consensus Tracking Control Scheme of Nonlinear Multi-Agent Systems Under Deception Attacks

Adaptive Prescribed-Time Consensus Tracking Control Scheme of Nonlinear Multi-Agent Systems Under Deception Attacks

Adaptive Dynamic Event-Triggered Tracking Control for Uncertain Switched Nonlinear Systems Under State-Dependent Switching.

This article introduces an adaptive dynamic event-triggered technique for addressing the output tracking control problem of uncertain switched nonlinear systems with prescribed performance. First, a switching dynamic event-triggered mechanism (SDETM) is established to alleviate network burden and conserve computational resources. A notable aspect is the inclusion of asynchronous switching between the switching subsystems and controllers. Second, a state-dependent switching law ensuring a dwell-time constraint is designed, which avoids the frequent switching phenomenon within any finite time interval. Third, an SDETM and an adaptive dynamic event-triggered controller are developed to confine the output tracking error within predefined decaying boundaries, while ensuring that all the signals of the closed-loop switched system remain within bounded regions. Finally, the validity and applicability of the developed control scheme are demonstrated through a one-link manipulator example.

Barrier-Function Adaptive Finite-Time Trajectory Tracking Controls for Cyber Resilience in Smart Grids Under an Electricity Market Environment

Barrier-Function Adaptive Finite-Time Trajectory Tracking Controls for Cyber Resilience in Smart Grids Under an Electricity Market Environment

Neural Optimal Control for Constrained Visual Servoing via Learning From Demonstration

This paper proposes a novel optimal control scheme for constrained image based visual servoing of a robot manipulator. For a robot manipulator with an eye-on-hand configuration, visibility constraint is an essential requirement to avoid servo failure, while robot’s actuator limits must also be satisfied. To ensure this, the constraints are modelled implicitly via learning the task and defining safe regions using expert human demonstrations via mixture of Dynamic Movement Primitives (DMPs). The visual servoing problem is then formulated as a closed-loop optimal control problem using these constraint model where a desired target (possibly time-varying) is obtained by acting upon the feedback from the real-time visual sensors. The visual servo control loop consists of a single network adaptive critic optimal tracking control scheme whose weights are tuned using Lyapunov stability criteria. The stability and the performance of the proposed control scheme is shown theoretically via Lyapunov approach and also verified experimentally using a seven degree of freedom (DOF) Franka Emika and six DOF Universal Robot (UR) 10 manipulator. The approach is also demonstrated on a use case scenarios in mock-up convenience store and warehouse setup. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The applications of robots in busy warehouses, healthcare sectors and convenience store, has a big societal impact. The scenarios in these real-world problems often consist of a dynamic environment. Therefore, sensor-based localization and planning, along with satisfying robot and task constraints are needed. These naturally adds up to the programming costs in addition to the robot platform and actuation. However, modern robots need intuitive and easy programming for more pervasive in a practical societal application. In our proposed method, this sensor-based planning with environmental constraints is modelled implicitly using the Programming by Demonstration framework. The user needs to catch the robot arm by his hand and teach the task at hand, and the framework captures the task and robot constraints while generalizing to new goals. This modelling, along with cost optimal controller, generates real-time constraint aware robot manipulation trajectories for the demonstrated task in dynamic environments.

Distributed Adaptive Sampled-Data Security Tracking Control for Uncertain Heterogeneous Multi-Agents Systems Under DoS Attacks

Distributed Adaptive Sampled-Data Security Tracking Control for Uncertain Heterogeneous Multi-Agents Systems Under DoS Attacks

Adaptive Asymptotic Tracking Control for Underactuated Autonomous Underwater Vehicles With State Constraints

Adaptive Asymptotic Tracking Control for Underactuated Autonomous Underwater Vehicles With State Constraints

Adaptive Fixed-Time Performance Tracking Control for Unknown Nonlinear Pure-Feedback Systems Subject to Full-State Constraints and Actuator Faults

Adaptive Fixed-Time Performance Tracking Control for Unknown Nonlinear Pure-Feedback Systems Subject to Full-State Constraints and Actuator Faults