Model-free Adaptive Control Approach Research Articles

Data-Driven Containment Control for a Class of Nonlinear Multi-Agent Systems: A Model Free Adaptive Control Approach

This paper studies the containment control problem of heterogeneous multi-agent systems (MASs) with multiple leaders. The follower agent dynamics are assumed to be unknown and nonlinear. First, each follower is transformed into an incremental data description based on the dynamic linearization technique. Then, a distributed model-free adaptive containment control law is proposed such that all followers will be driven into the convex hull of the leaders. Furthermore, the algorithm is extended to the time-switching and dynamic leaders case. As a data-driven approach, the proposed controller design uses only the received input and output (I/O) data of these agents rather than agent mathematical models. Finally, to test the potential in real applications, three representative examples considering various environment factors, including external disturbances, are simulated to show the effectiveness and resilience of this method.

Resilience enhancement to loss of actuator effectiveness in a Model-Free Adaptive framework

Within the theoretical framework supporting the Model Free Adaptive Control approach, this paper investigates on the presence of possible loss of effectiveness affecting the actuator of a Single-Input Single-Output plant. In particular, stemming from a tracking controller proposed in a seminal paper and the related proofs, an identification algorithm is proposed for the estimation of the loss of effectiveness, with proved bounded estimation error, along with a condition for the detection of actuator faults. A fault-resilient tracking controller is finally proposed, based on the estimated loss of effectiveness, providing asymptotically vanishing tracking error. A comparative analysis by simulation on a benchmark system taken from the pertinent literature is also presented to validate the proposed development.

RETRACTED: Data–Driven Adaptive Fault–Tolerant Control for Floating Offshore Wind Turbines

Floating offshore wind turbines have increased in popularity owing to their adaptability for deep-water applications and high power generation efficiency. The control of floating offshore wind turbines, on the other hand, is very complex. The main challenges are the difficulty in precisely modelling floating offshore wind turbines and the higher failure rate of components. As a consequence, this study proposes a model-free adaptive fault-tolerant control system for blade root moment sensor failures. A model-free adaptive control approach is used to construct an individual pitch controller and a fault compensation to avoid mathematical modelling of floating offshore wind turbines. The proposed fault-tolerant control technique removes the need for fault detection and isolation by converting the fault dynamic compensation process into a real-time control issue for nonlinear systems. The fatigue, aerodynamics, structures, and turbulence code simulates and tests the proposed control strategy, and the results show that the proposed strategy can not only keep the wheel bearing load balanced but also reduce the movement of the floating platform and significantly reduce the bearing load of the floating offshore wind turbines. Furthermore, the output power is closer to the rated power, proving the strategy’s high fault tolerance in the face of repeated blade root moment sensor faults.

A Modified Model-Free Adaptive Control Method for Large-Scale Morphing Unmanned Vehicles

This paper investigates the attitude control problem for large-scale morphing unmanned vehicles. Considering the rapid time-varying and strong aerodynamic interference caused by large-scale morphing, a modified model-free control method utilizing only the system input and output is proposed. Firstly, a two-loop equivalent data model for the morphing unmanned vehicle is developed, which can better reflect the practical dynamics of morphing unmanned vehicles compared to the traditional compact form dynamic linearization data model. Based on the proposed data model, a modified model-free adaptive control (MMFAC) scheme is proposed, consisting of an external-loop and an inner-loop controller, so as to generate the required combined control torques. Additionally, in light of the aerodynamic uncertainties of the large-scale morphing unmanned vehicle, a rudder deflection actuator control scheme is designed by employing the model-free adaptive control approach. Finally, the boundedness of the closed-loop system and the convergence of tracking errors are guaranteed, based on the stability analysis. Additionally, numerical examples are presented to demonstrate the effectiveness and robustness of the proposed control scheme in the case of the effect of large-scale morphing.

An Enhanced Model Free Adaptive Control Approach for Functional Electrical Stimulation Assisted Knee Joint Regulation and Control.

Functional electrical stimulation has been widely used in the neurologically disabled population as a rehabilitation method because of its intrinsic and higher ability to activate paralyzed muscles. However, the nonlinear and time-varying nature of the muscle against exogenous electrical stimulus makes it very challenging to achieve optimal control solutions in real-time, that results in difficulty in achieving functional electrical stimulus-assisted limb movement control in the real-time rehabilitation process. Model-based control methods have been suggested in many functional electrical stimulations elicited limb movement applications. However, in the presence of uncertainties and dynamic variations during the process the model-based control methods are unable to give a robust performance. In this work, a model-free adaptable control approach is designed to regulate knee joint movement with electrical stimulus assistance without prior knowledge of the dynamics of the subjects. The model free adaptive control with a data-driven approach is provided with recursive feasibility, compliance with input constraints, and exponential stability. The experimental results obtained from both able-bodied participants and a participant with spinal cord injury validate the ability of the proposed controller to allocate electrical stimulus for regulating seated knee joint movement in the pre-defined trajectory.

A data-driven model-free adaptive controller with application to wind turbines

A data-driven controller is presented in this paper, which stems from the well known model-free adaptive control approach based on an equivalent linearized dynamical model of the plant. Inspired by the recent paper (Liu and Yang, 2019), the output tracking problem is here solved by a data-driven adaptive sliding-mode controller simultaneously ensuring prescribed performance constraints. To allow a rigorous stability analysis, the sliding variable, and the consequently derived controller, have been redesigned with respect to the inspiring paper. A proper setting of the gain of the discontinuous term is shown necessary to ensure closed loop stability. Validation of the technique has been extensively performed on the well assessed high-fidelity tool FAST (NREL) to solve the efficiency maximization problem using the proposed approach for a 5 MW wind turbine operating in the medium wind speed region.

Model free adaptive control of the under-actuated robot manipulator with the chaotic dynamics

Development of practical control approaches for the under-actuated chaotic systems such as the robot manipulators are challenging due to the unpredictable character of the chaotic dynamics, and the inevitable real-time application properties like delays, saturations, and uncertainties In this paper, we propose a model free digital adaptive control approach, which considers the time delay of the control signal, actuator saturation, and non-parametric uncertainties, for an under-actuated manipulator. We also develop a chaos control to learn the unbiased and smooth digital control policy inside the chaotic regions of the continuous time under-actuated manipulator. We perform real-time experiments in a dynamic environment with the proposed digital adaptive control. Then we compare the results of the learning and control with and without chaos control. We observe that the proposed model free adaptive control approach can accurately learn both the long-term predictor and unbiased control policy even in the chaotic regions of the under-actuated robot manipulator.

Apply Model-Free Adaptive Control Approach for Mobile Robot Path Following

The state of the art of mobile robot path planning is composed with global planner and local planner. For example, the global planner majorly establishes a suitable path from a knowing map based on the shortest path. And the local planner which computes the velocity command that includes obstacle clearance and progress toward the goal. However, due to the 2D laser ranger on a mobile service robot just detects the part of the meal table or misses it, this will cause robot often hits the meal table. In this work we apply adaptive control method to feed the goals for Dynamic Window Approach’s (DWA) computing. The experimental result shows that DWA will follow the pre-defined path closely and smoothly.

A recursive modified partial least square aided data-driven predictive control with application to continuous stirred tank heater

In this paper, a data-driven predictive control strategy for nonlinear system is proposed and testified on a continuous stirred tank heater (CSTH) benchmark. A recursive modified partial least square (RMPLS) algorithm is employed to regress the local linear model. The algorithm of locally weighted projection regression (LWPR) is then leveraged to build the predictive model, based on which a novel data-driven predictive control strategy is put forward. The proposed predictive controller has the ability to deal with changing working conditions, benefiting from the incremental learning ability of RMPLS and LWPR. The performance of the proposed control strategy is demonstrated with the CSTH while the superiority is illustrated by comparison with an existing model-free adaptive control approach.

Apply Model-Free Adaptive Control Approach for Mobile Robot Path Following

Apply Model-Free Adaptive Control Approach for Mobile Robot Path Following

Neural network-based event-triggered MFAC for nonlinear discrete-time processes

This paper is concerned with the event-triggered data-driven control problem for nonlinear discrete-time systems. An event-based data-driven model-free adaptive controller design algorithm together with constructing an adaptive event-trigger condition is developed. Different from the existing data-driven model-free adaptive control approach, an aperiodic neural network weight update law is introduced to estimate the controller parameters, and the event-trigger mechanism is activated only if the event-trigger error exceeds the threshold. Furthermore, by combining the equivalent-dynamic-linearization technique with the Lyapunov method, it is proved that both the closed-loop control system and the weight estimation error are ultimately bounded. Finally, two simulation examples are provided to demonstrate the effectiveness of the derived method.

Model Free Adaptive Control for Robotic Manipulator Trajectory Tracking

This paper considers the problem of trajectory tracking of robotic manipulator system by using model free adaptive control (MFAC). The dynamic linearization technique is first introduced, and then the controller can be designed only by I/O data of the robotic manipulator system via MFAC approach, not includes any explicit model information. With some given conditions for controller parameters, the stability of MFAC for robotic manipulator system can be given. It is shown that the tracking error of robotic manipulator can converge to zero and the better tracking performance can be obtained. Simulation result for two-link robot manipulator further given to valid the effective of the proposed method.

Freeway Density Control Via Model-free Adaptive Ramp Metering Approach

By introducing a new dynamical linearization technology, this paper presents a model-free adaptive control approach for density control of freeway traffic flow via ramp metering, which is consisted with a control input learning law and a parameter updating law. The design and analysis only depends on the I/O data of the freeway traffic system. Furthermore, the control input learning law is extended to a higher-order form by incorporating more control information of previous sampling instants for improving the control performance. Both convergence analysis and simulation results illustrate the validity of the presented methods.