Adaptive Control Law Research Articles

Adaptive Leader-Following Consensus of Multiagent Systems With Unknown Disturbances and Switching Topologies

This brief investigates adaptive consensus of a second-order multi-agent system (MAS) with unknown nonlinear dynamics by using the relative position and velocity measurements. Firstly, for a leader-follower MAS, unknown nonlinear dynamics of a self-active leader and all followers are constructed and described by linearly parameterized models. Secondly, based on relative position and velocity measurements, a fully distributed adaptive control law (ACL) is designed, then a novel control protocol by using the ACL is proposed for each follower. Thirdly, with the help of a common Lyapunov function method, the asymptotical stability of the derived dynamical tracking system and the parameter convergence are analyzed. Finally, the correctness of the adaptive protocol is verified by some numerical simulations.

Self-Correcting Quadratic Programming-Based Robot Control

Quadratic programming (QP)-based controllers allow many robotic systems, such as humanoids, to successfully undertake complex motions and interactions. However, these approaches rely heavily on adequately capturing the underlying model of the environment and the robot’s dynamics. This assumption, nevertheless, is rarely satisfied, and we usually turn to well-tuned end-effector PD controllers to compensate for model mismatches. In this article, we propose to augment traditional QP-based controllers with a learned residual inverse dynamics (IDs) model and an adaptive control law that adjusts the QP online to account for model uncertainties and unforeseen disturbances. In particular, we propose: 1) learning a residual IDs model using the Gaussian Process and linearizing it so that it can be incorporated inside the QP-control optimization procedure and 2) a novel combination of adaptive control and QP-based methods to avoid the manual tuning of end-effector PID controllers and faster convergence in learning the residual dynamics model. In simulation, we extensively evaluate our method in several robotic scenarios ranging from a 7-degrees of freedom (DoFs) manipulator tracking a trajectory to a humanoid robot performing a waving motion for which the model used by the controller and the one used in the simulated world do not match (unmodeled dynamics). Finally, we also validate our approach in physical robotic scenarios where a 7-DoFs robotic arm performs tasks where the model of the environment (mass, friction coefficients, etc.) is not fully known.

Velocity Observer-Based Event-Triggered Adaptive Fuzzy Attitude Takeover Control of Spacecraft With Quantized Quaternion

In this paper, the spacecraft attitude takeover control (ATC) problem with limited communication and the unavailable angular velocity using cellular satellites is addressed. First, a dynamic uniform quantizer (DUQ) is proposed to quantize unit quaternion between sensor cellular satellites and controller cellular satellite. Second, an adaptive fuzzy observer is proposed to estimate the unavailable angular velocity. Further, between controller cellular satellite and actuator cellular satellites, an event-triggered mechanism (ETM) is provided to lighten the communication burden. By combing DUQ, adaptive fuzzy observer, the adaptive fuzzy control law and the ETM is established. The stability of the ATC systems with the proposed control method is ensured. Finally, the simulation results illustrate the effectiveness of the developed control laws.

Reinforcement learning‐based adaptive consensus control for a class of non‐affine nonlinear multiagent systems with semi‐Markov switching topographies

AbstractA reinforcement learning‐based adaptive consensus control method has been proposed for the non‐affine nonlinear multiagent systems with semi‐Markov switching topographies in this paper. The considered system contains the nonlinear features, multiple source disturbances and non‐affine inputs, which makes it difficult to design the consensus controller. Firstly, to overcome the adverse influence caused by the multiple disturbances existing in the nonlinear system, a reinforcement learning based adaptive disturbance observer has been designed. Secondly, to handle the unknown nonlinear terms and the non‐affine features of the control inputs, an auxiliary loop has been introduced and a reinforcement learning based adaptive control law has been designed. Finally, aiming at the semi‐Markov switching topographies possessed by the multiagent systems, a novel adaptive consensus controller integrating the reinforcement learning approximator, the adaptive disturbance observer and the consensus control law, has been proposed. Through the Lyapunov direct method, it has been proven that the tracking errors are ultimately uniformly bounded. Moreover, within an acceptable margin of error, the state of all the followers can proved to be consistent with the leader. Several numerical experiments are conducted to illustrate the feasibility and effectiveness of the proposed reinforcement learning‐based leader‐following consensus control law.

Behavior learning based distributed tracking control for human-in-the-loop multi-agent systems

In this study, the distributed tracking problem for human-in-the-loop multi-agent systems (HiTL MASs) has been investigated. First, we construct an HiTL MAS model with a non-autonomous leader which can receive the control signal from a human operator and generate the desired trajectory. The human control signal is assumed to be generated by a leader’s state feedback control law with an unknown gain matrix that represents the control behavior of the human operator. Then, we propose a fully distributed adaptive control method that enables all followers to simultaneously track the human-controlled leader and online learn the unknown human operator’s feedback gain matrix. Furthermore, the parameter estimation error is also discussed, and all followers will learn the true value of the human operator’s feedback gain matrix when the state of the leader satisfies the persistent excitation (PE) condition. Moreover, a novel distributed adaptive control law is developed for each follower to remove the PE condition by utilizing the concurrent learning (CL) technique. Finally, simulated examples demonstrating the effectiveness of the proposed methodologies are presented.

Robust control for affine nonlinear system with unknown time‐varying uncertainty under reinforcement learning framework

AbstractThis paper investigates the adaptive robust control problem based on reinforcement learning for an affine nonlinear system with unknown time‐varying uncertainty. Inspired by the ability to estimate uncertainty of neural network, a novel policy iteration algorithm is proposed which alternates between the value evaluation, uncertainty estimation, and policy update steps until the adaptive robust control law is obtained. Especially during the step of uncertainty estimation, the unknown time‐varying uncertainty is approximated by a radial basis function neural network and introduce it into the reinforcement learning framework. By designing an appropriate utility function, the algorithm improves both convergence rate and final approximate error comparing with existing reinforcement learning algorithm. The Lyapunov stability theorem provides theoretical demonstrations of the stability and convergence. Furthermore, the uniformly ultimately bounded stability of the affine nonlinear system is demonstrated with unknown time‐varying uncertainty. Finally, the performance of the proposed algorithm is demonstrated through a torsion pendulum system.

Constrained control using novel nonlinear mapping for underactuated unmanned surface vehicles with unknown sideslip angle

This paper addresses the problem of guidance and control for underactuated unmanned surface vehicles (USVs) with state constraints and input saturation, in support of enabling an underactuated USV to follow a parameterized curved path in the case of unknown sideslip angle and cross-tracking error constraint. First, a cross-tracking error constraint line-of-sight (LOS) guidance law with sideslip angle compensation is originally designed to guide an underactuated USV to convergence to the desired path within a time-varying cross-tracking error constraint. Second, a novel nonlinear mapping (NM) function is first constructed to map the heading and surge control subsystems with state constraints to unconstrained nonlinear systems, transforming the constrained control problem into the unconstrained control problem. Subsequently, adaptive fuzzy control laws are designed to achieve the control objectives for the USV using the new unconstrained nonlinear systems with unknown disturbance and input saturation. Then, a series of theoretical analyses using input-to-state stability theories are presented to prove the boundness of the tracking errors for the underactuated USV during path following. Finally, numerical results obtained using a physics-based simulation model are shown to reveal the effectiveness of the guidance and control algorithms.

Model-Free Adaptive Disturbance Rejection Control of An RSV With Hardware-in-The-Loop Experiments

This paper reports a model-free adaptive disturbance rejection control method for the surge velocity tracking of a robotic surface vehicle (RSV) in the presence of unknown external disturbances, internal dynamics, and control coefficient. Specifically, reduced-order and full-order filtering adaptive extended state observers (FAESOs) are proposed at first, for estimating the unknown control coefficient, and the lumped disturbance consisting of the unknown external disturbances and internal dynamics, simultaneously. Based on the FAESO, a model-free adaptive disturbance rejection surge velocity tracking control law is derived. A key advantage of the proposed method is that it results in uniform control performance within large variation range of control coefficient regardless of the fully unknown model. The efficacy of the developed model-free adaptive disturbance rejection control method for the surge velocity tracking of RSV is substantiated by comparing simulations and hardware-in-the-loop experiments.

Design of optimal-robust control of step motors with partially unknown parameters and disturbances

The paper describes the design of a novel control law for step motor under participially unknown parameters and bounded external disturbances. The unknown parameters and disturbances are related to the motor torque, the rotor moment of inertia, the viscous friction, the resistance and the load torque. The design of a novel control law is based on feedback linearization and linear robust control. The gain of a linear control law is calculated by solving the linear matrix inequality and some optimization problems. The simulations illustrate the efficiency of the proposed scheme in comparison with classical PID-controller and adaptive control law.

Adaptive control schemes based on characteristic model for servo motor drives

AbstractIn the paper, two adaptive control schemes based on characteristic model (CM) method are proposed to achieve great dynamic performance for servo motor drive applications. Two second‐order CMs of permanent magnet synchronous motors (PMSMs) are established based on the difference equation method. An adaptive controller consisting of four control laws is designed based on CMs. The proposed controller implements an improved maintaining/tracking control law that considers load torque ripple, plus a golden‐section adaptive control law, a logic differential control law, and a logic integral control law. In addition, two different servo systems using adaptive controllers are established, both of which are simple to implement and can achieve accurate position‐tracking or speed‐tracking in a servo drive system. The feasibility and effectiveness of the proposed adaptive control schemes are experimentally tested and verified on a servo motor drive platform.

Rotation tracking control strategy of underwater flexible telescopic manipulator based on neural network compensation for water environment disturbance

The underwater flexible telescopic manipulator (UFTM) can be installed on a submersible to achieve underwater operation, which is conducive to the exploration and development of deep-sea resources. However, the motion of the UFTM is affected by underwater disturbance torque, which may lead to a decrease in operational accuracy. In addition, the flexible components in the UFTM, including flexible deformation and torsional deformation, will exacerbate fluctuations in the rotation angle underwater environment disturbance torque. In this paper, a control method using neural networks to identify underwater disturbance torques is proposed to improve the rotational tracking accuracy of the UFTM. Firstly, based on the assumed modal method (AMM) and Lagrange equation, the UFTM dynamic model considering two-dimensional deformation and underwater disturbance torque is derived. Then, based on the UFTM dynamic model and Lyapunov theorem, the adaptive law and control law of neural networks are designed. Finally, simulation and control experiments prove the effectiveness of the neural network compensation for underwater disturbance torque control strategy. Compared with the classical PID strategy and the sliding mode control, the control strategy of neural network compensation for underwater disturbance can effectively improve the tracking control accuracy of the UFTM.

Position and Attitude Tracking Finite-Time Adaptive Control for a VTOL Aircraft using Global Fast Terminal Sliding Mode Control

In this work, the position and attitude tracking finite-time adaptive control problem of a type of vertical take-off and landing (VTOL) aircraft system is studied. Here, the dynamic of the VTOL aircraft is subjected to external disturbances and unknown nonlinearities. Firstly, radial basis function neural networks are introduced to approximate these unknown nonlinearities, and adaptive weight update laws are proposed to replace unknown ideal weights. Secondly, for the errors generated in the approximation process and the external disturbances of the aircraft system, adaptive parameter update laws are presented. After that, based on the designed global fast terminal sliding mode control functions and adaptive update laws, we present the position tracking control laws and the roll angle control law. Then, based on this, the adaptive global fast terminal sliding control laws for the given aircraft system are finally obtained. Meanwhile, the stability of the aircraft control system is proven by using Lyapunov stability theory and designed adaptive control laws. It is not only ensured that the outputs of the aircraft system can track the given trajectories, but also ensured that the tracking errors can converge to approximately zero within a finite time. Finally, the validity of the designed adaptive control laws is verified through three numerical examples. It can be obtained that the finite-time tracking problems of the given aircraft system can be achieved at 18.8766 s and 14.6340 s under the given parameters. The results are consistent with the theoretical analysis. In addition, under the control laws proposed in this work, the aircraft system can achieve tracking after 9.443 s and 9.674 s and the tracking errors are basically close to zero, which is significantly superior to other control methods considered in this work.

Backstepping-Based Six Degrees of Freedom Adaptive Control for Spacecraft Tracking a Noncooperative Target

In this paper, the six degrees of freedom adaptive tracking control for chaser spacecraft with uncertainty and disturbance to approach a noncooperative target are studied. The system equations with coupled relative translation and relative rotation are modeled. Considering the unknown inertia uncertainties and state-dependant disturbances, a backstepping-based adaptive tracking control law is developed to achieve high-precision proximity operations between chaser and noncooperative target. The unknown inertia and disturbance are estimated by adaptive update laws. The properties of the developed control law are discussed through the comparison of simulation results.

Improved Adaptive NDI Flight Control Law Design Based on Real-Time Aerodynamic Identification in Frequency Domain

The traditional aircraft controller design is usually based on the off-line aerodynamic model. Due to the deviation of the off-line aerodynamic model, the flight quality is difficult to meet the requirements when the aircraft is flying in the real atmosphere. To solve this problem, this paper proposes a frequency domain identification-based improved adaptive nonlinear dynamic inversion (NDI) control method (FDI-ANDI). In this paper, an online recursive aerodynamic parameter identification method in the frequency domain is first designed, and then an adaptive dynamic inversion control method based on the online aerodynamic parameter identification results is established. Finally, aiming at the problem of the slow response speed of the NDI controller, an improved adaptive dynamic inversion control law is designed by using the method of series lead correction. Compared with the traditional control method, the adaptive dynamic inversion method based on online aerodynamic identification has stronger robustness and a faster response speed in the face of model uncertainty. The final simulation analysis shows that the method has a better control effect than the traditional control method.

Control-oriented meta-learning

Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory tracking performance, provided that any uncertain dynamics terms are linearly parameterizable with known nonlinear features. However, it is often difficult to specify such features a priori, such as for aerodynamic disturbances on rotorcraft or interaction forces between a manipulator arm and various objects. In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal parametric model of these nonlinear features. Our key insight is that we can better prepare the controller for deployment with control-oriented meta-learning of features in closed-loop simulation, rather than regression-oriented meta-learning of features to fit input-output data. Specifically, we meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. With both fully actuated and underactuated nonlinear planar rotorcraft subject to wind, we demonstrate that our adaptive controller outperforms other controllers trained with regression-oriented meta-learning when deployed in closed-loop for trajectory tracking control.

Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System

Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System

A Physiological Control for Artificial Heart Pump based on Adaptive Regulation of Heart Rate and the improved BAS Algorithm Optimization

In order to solve the problem that the traditional artificial heart pump has poor dynamic bionic performance and cannot respond to blood perfusion in real time according to the patient’s state, this paper proposes a new physiological control method based on adaptive adjustment of heart rate. Firstly, a coupled model of the cardiovascular circulatory system and artificial heart pump was established, and the correctness of the model is verified by hemodynamic simulation. Secondly, according to the model-free adaptive control theory, the artificial heart pump physiological control strategy for adaptive regulation of heart rate is designed, including pseudo partial derivative estimation algorithm and adaptive control law. Then, the parameters of the adaptive controller are optimized by the improved beetle antennae search algorithm. Finally, the auxiliary effect and dynamic performance of the adaptive control strategy are studied by numerical methods. The results show that the physiological control method based on adaptive regulation of heart rate realizes the adaptive modulation of heart rate and blood flow, which can meet the needs of blood perfusion in different states and help to promote the recovery of damaged heart.

Direct approach-based synchronization of fully quaternion-valued neural networks with inertial term and time-varying delay

In this article, the global exponential and asymptotic synchronization for a type of quaternion-valued inertial delayed neural networks (QIDNNs) are explored by introducing a direct analysis approach. Above all, without reducing the order or decomposing the models of QIDNNs, a compact quaternion-valued control scheme is developed to ensure synchronization. In addition, two new Lyapunov functionals are constructed directly and several compact criteria represented by matrix inequalities are derived by means of the theory of quaternion and inequality technique. Furthermore, a quaternion-valued adaptive control law is proposed to automatically determine the control costs, and the synchronization is guaranteed by the aid of Barbalat Lemma. Lastly, the derived results are affirmed via a numerical example.

Neural network-based adaptive controller design for robotic manipulator subject to varying loads and unknown dead-zone

In this article, aiming at handling the trajectory tracking issue of industrial manipulator system (IMS) with modeling uncertainty, varying loads (VL) and unknown dead-zone characteristic, a compensation-based adaptive switching controller synthesis is proposed. In this scheme, the dynamic model of the IMS under VL is regarded as a switched system (SS) with a specified modal set. The nonlinear term related to plant model in each subsystem is approximated by radial basis function neural network (RBFNN) so as to avoid the reliance of the controller on the accurate model, and the unknown dead-zone is estimated and compensated by NN, from which the corresponding NN robust compensation term is developed to eliminate the potential perturbations and estimated errors. The designed controller with switching mechanism effectively solves the problem of degradation of the tracking accuracy caused by VL. Finally, the uniform ultimate boundedness of error signals is analyzed by the average dwell time (ADT) approach, multi-Lyapunov function method and the synthesized adaptive control law, and the effectiveness of the developed scheme is verified by simulation.

Trajectory‐tracking control of an unmanned surface vehicle based on characteristic modelling approach: Implementation and field testing

AbstractIn this study, a practical adaptive control scheme is proposed for the trajectory tracking of an unmanned surface vehicle via the characteristic modelling approach. Therefore, accurate tracking control can be achieved in the presence of unknown time‐varying model parameters and environmental disturbances. The control scheme comprises a trajectory guidance module based on the virtual target approach and a tracking control module designed by characteristic modelling theory. Firstly, the ideal control commands of the yaw speed and surge speed are generated using the position errors between the vehicle and the virtual target. Then, a second‐order characteristic model for the heading and surge speed channel is developed. The parameters of the model are updated by a real‐time parameter identification algorithm. Based on this model, an integrated adaptive control law is designed which consists of golden‐section control, feed‐forward control and integral control. Finally, the development processes of the vehicle platform and the control algorithms are described, and the results of simulation and field experiments are presented and discussed.