Adaptive Control Framework Research Articles

An adaptive control framework for a class of nonlinear time-delay systems

This paper aims to formulate an adaptive control framework capable of producing desirable tracking performance for uncertain nonlinear systems with an unknown delay in the control input and a nonlinear control coefficient matrix. Since the control framework inherits common components of the classical model-reference adaptive controller (MRAC), it is easy to implement with a low barrier to entry. In addition, the work shows that the deviation of the state of the proposed control scheme from an ideal dynamics system is bounded given that the delays are within a certain margin. Simulation results are presented to illustrate the theoretical contribution and demonstrate the efficacy of the proposed controller in comparison with an existing competing controller. The limitation of this control framework is also discussed with a suggestion for overcoming the limitation toward the end of the paper.

An Adaptive Control Framework for Dynamically Reconfigurable Battery Systems Based on Deep Reinforcement Learning

This article presents an adaptive control framework for dynamically reconfigurable battery (DRB) systems based on the deep reinforcement learning method. The proposed adaptive control framework relies on deep Q-network to learn the DRB system operations. By utilizing its model-free nature, the proposed framework can significantly reduce the complexity of building experiences or expert models for DRB systems as well as improve battery operating time by ensuring cell balancing. Extensive simulation and experimental study has been carried out with data gathered from a real-world DRB testbed, and the results show the effectiveness and efficiency of the proposed control framework.

Adaptive neural network asymptotic path-following control of underactuated ships with stochastic sea loads

This paper investigates the high-precision path following of underactuated ships suffering from stochastic sea loads, where a novel adaptive neural network asymptotic control framework is fabricated. Based on the nonlinear 3-degree-of-freedom (3-DOF) motion model of the underactuated ship, the stochastic ship motion model is established by involving both unknown deterministic and stochastic disturbances, which is chosen as the control objective. To transform path following to tracking control, a novel virtual ship guidance approach is fabricated with generating smooth reference signals. Through the control scheme, the radial-basis-function neural networks (RBF NNs) are employed to approximate the total marine uncertainties. By introducing integral-bounded functions to control laws and adaptive laws, the asymptotic convergence of positional tracking errors is guaranteed with the aid of adaptive backstepping control. To ensure the computational simplicity, the adaptive laws are fabricated with the minimum learning parameters (MLPs). Via the important inequality of the neural basis function, the “algebraic loop” problem is successfully solved, such that the control logicality is guaranteed. Finally, the numerical experiments validate the high-precision tracking performance of the proposed scheme.

Adaptive QoS-aware multipath congestion control for live streaming

Nowadays, the popularization of live streaming bring challenges in providing customized Quality of Service (QoS) and satisfying diverse Quality of Experience (QoE) requirements. Multipath TCP (MPTCP), designed for bandwidth and reliability in aggregating transmission, has the potential to improve live streaming performance with multiple simultaneously transmitting paths. However, the existing multipath congestion control algorithms (CCAs) of MPTCP fail to adapt to diverse network environments and different QoS requirements. We study the problem with extensive experiments to observe the limitations of current multipath CCAs. To tackle these problems and support stable and customized live streaming, we propose ACCeSS, an adaptive QoS-aware multipath congestion control framework. ACCeSS is able to promptly adapt to network changes and QoS requirements with a novel control policy optimization phase. In order to adjust and stimulate improvement on the preferred performance metric, ACCeSS exploits Random Forest Regressing (RFR) method to perform QoS-specific utility function optimization. Besides, ACCeSS is implemented and deployed in a multipath live streaming system. We compare it with other multipath CCAs in the Linux kernel and evaluate their performance in both emulated and real-world networks. It is revealed that ACCeSS outperforms classic multipath CCAs and the state-of-the-art learning-based multipath CCA, with better environment adaptive capability of QoS and higher QoE for live streaming.

Experimental Validation of Adaptive Augmented LQI Control for a 2 DoF Helicopter

This paper presents a novel adaptive augmented linear quadratic integral (LQI) controller for a 2 degrees of freedom (DoF) helicopter to improve the closed-loop performance of the baseline controller even when the system contains unmodeled dynamics and exogenous disturbances. Although the LQI controller is widely used in aircraft control applications for its excellent reference-tracking capability and inherent robustness, the performance of the baseline LQI controller degrades when the aircraft model has unmodeled dynamics and external disturbances, which are inevitable in aircraft applications. Hence, to deal with the parameter uncertainty and unmeasured disturbances, we present an adaptive control framework using a model reference adaptive control (MRAC) scheme combined with the baseline LQI controller. The key aspect of the proposed control scheme is that the robustness of the closed-loop control system to deal with the matched uncertainty is enhanced by tracking error augmentation with the control law. Moreover, this work proves the asymptotic stability of the closed-loop system using the inverse Lyapunov function and Barbalat’s lemma. The efficacy of the adaptive augmented LQI is experimentally validated through hardware in loop (HIL) implementation on a laboratory scale 2 DoF helicopter workstation for several real-time test cases. Experimental results accentuate that the adaptive augmented control scheme can significantly improve not only the reference-tracking capability but also the robustness of the closed-loop system.

Adaptive Control of Wire-Borne Underactuated Brachiating Robots Using Control Lyapunov and Barrier Functions

Executing safe brachiation maneuvers with a cable-suspended underactuated robot is a challenging problem due to the complications induced by the cable dynamics. We present and experimentally implement an online adaptive controller for a wire-borne brachiating robot swinging on a vibrating cable. An adaptive function approximation approach is proposed to estimate the unknown dynamics of the flexible cable as an external force applied to the robot. Robust control Lyapunov and barrier functions are designed and incorporated into quadratic programs to synthesize a unified adaptive control framework, which formally guarantees the stability and safety of the brachiating robot in the presence of dynamic uncertainties, actuator constraints, and obstacles in the environment. The stability analysis and derivation of the adaptation law are carried out through a Lyapunov analysis. We demonstrate and validate the proposed control framework using extensive hardware experiments with an underactuated brachiating robot operating on a flexible cable. Simulation results, hardware experiments, and comparisons with a baseline controller show that the proposed quadratic programming-based controller achieves reliable tracking performance and disturbance estimation in the presence of model uncertainties, actuator limits, and safety constraints.

A framework of adaptive fuzzy control and optimization for nonlinear systems with output constraints

This paper presents a framework for adaptive fuzzy control and optimization of nonlinear systems subject to uncertainties and disturbances. The barrier Lyapunov function (BLF) technique was adopted to determine output constraints. To enhance the tracking performance of the system, fuzzy logic systems (FLSs) were introduced to approximate nonlinear terms. Subsequently, a combination of Bayesian optimization and particle swarm optimization (BO-PSO) was employed for gains optimization to further improve the control performance. Furthermore, the multilayer neural networks (MNNs) were applied as surrogate models of nonlinear systems with interval parameters to improve the computational efficiency of the optimization process. Finally, two simulations were conducted to demonstrate the effectiveness of the proposed framework.

A Fast Optimal Speed Planning System in Arterial Roads for Intelligent and Connected Vehicles

Speed planning system is generally equipped for intelligent and connected vehicles (ICVs). Under the circumstances of autonomous driving, an energy-optimal speed trajectory is usually desired, particularly on urban arterial roads with complex traffic conditions involved. However, the existing speed planning solutions in the literature have not dealt with the problem of time consuming. Also, the ego vehicle could not perfectly track a precalculated speed reference because of the dynamically varying traffic. Thus, optimal speed planning cannot always be guaranteed. In this article, a fast optimal speed planning system for complex urban driving situations is established through an adaptive hierarchical control framework. In the planning layer, dynamic programming (DP) and the interior-point optimizer are jointly used to compute the global speed trajectory with access to signal phase and timing (SPaT) information. The computational burden is greatly alleviated based on a weighted orientation graph assumption and problem decomposition. The following layer utilizes the Informer, which is a transformer-based model, to predict preceding vehicle speed. Then, a target-switching model-predictive controller (MPC) is adopted for global speed trajectory following and adaption. The proposed approach significantly reduces speed planning computation time compared to previous solutions. Simulation results based on real road traffic scenes manifest that 22.0% of energy is saved compared with human driving.

Adaptive neural control for nonlinear switched systems with improved MDADT and its applications

In this paper, a new framework of the robust adaptive neural control for nonlinear switched stochastic systems is established in the presence of external disturbances and system uncertainties. In the existing works, the design of robust adaptive control laws for nonlinear switched systems mainly relies on the average dwell time method, while the design and analysis based on the model-dependent average dwell time (MDADT) method remains a challenge. An improved MDADT method is developed for the first time, which greatly relaxes the requirements of Lyapunov functions of any two subsystems. Benefiting from the improved MDADT, a switched disturbance observer for discontinuous disturbances is proposed, which realizes the real-time gain adjustment. For known and unknown piecewise continuous nonlinear functions, a processing method based on the tracking differentiator and the neural network is proposed, which skillfully guarantees the continuity of the control law. The theoretical proof shows that the semiglobal uniform ultimate boundedness of all closed-loop signals can be guaranteed under switching signals with MDADT property, and simulation results of the longitudinal maneuvering control at high angle of attack are given to further illustrate the effectiveness of the proposed framework.

Adaptive control of a wheelchair mounted robotic arm with neuromorphically integrated velocity readings and online-learning.

Wheelchair-mounted robotic arms support people with upper extremity disabilities with various activities of daily living (ADL). However, the associated cost and the power consumption of responsive and adaptive assistive robotic arms contribute to the fact that such systems are in limited use. Neuromorphic spiking neural networks can be used for a real-time machine learning-driven control of robots, providing an energy efficient framework for adaptive control. In this work, we demonstrate a neuromorphic adaptive control of a wheelchair-mounted robotic arm deployed on Intel’s Loihi chip. Our algorithm design uses neuromorphically represented and integrated velocity readings to derive the arm’s current state. The proposed controller provides the robotic arm with adaptive signals, guiding its motion while accounting for kinematic changes in real-time. We pilot-tested the device with an able-bodied participant to evaluate its accuracy while performing ADL-related trajectories. We further demonstrated the capacity of the controller to compensate for unexpected inertia-generating payloads using online learning. Videotaped recordings of ADL tasks performed by the robot were viewed by caregivers; data summarizing their feedback on the user experience and the potential benefit of the system is reported.

Fractional-Order Multivariable Adaptive Control Based on a Nonlinear Scalar Update Law

This paper proposes a new fractional-order model reference adaptive control (FOMRAC) framework for a fractional-order multivariable system with parameter uncertainty. The designed FOMRAC scheme depends on a fractional-order nonlinear scalar update law. Specifically, the scalar update law does not change as the input–output dimension changes. The main advantage of the proposed adaptive controller is that only one parameter online update is needed such that the computational burden in the existing FOMRAC can be relieved. Furthermore, we show that all signals in this adaptive scheme are bounded and the mean value of the squared norm of the error converges to zero. Two illustrative numerical examples are presented to demonstrate the efficiency of the proposed control scheme.

Iterative Learning Control for Output Tracking of Nonlinear Systems With Unavailable State Information.

This work presents a novel design framework of adaptive iterative learning control (ILC) approach for a class of uncertain nonlinear systems. By using the closed-loop reference model that can be viewed as an observer, the proposed adaptive ILC approach can be adapted to deal with the output tracking problem of nonlinear systems with unavailable system states. In the systems considered, two classes of uncertainties are taken into account, including parametric input disturbances and input distribution uncertainties. To facilitate the controller design and convergence analysis, the composite energy function (CEF) methodology is employed. The design framework in this brief is novel and widely applicable, which extends the CEF-based ILC approach to output tracking control of nonlinear systems without requiring full knowledge of state information and complicated observer design process. To show the effectiveness of the proposed design framework and control algorithms, two numerical examples are illustrated.

Learning-Based Multi-Robot Formation Control With Obstacle Avoidance

Multi-robot formation control has been intensively studied in recent years. In practical applications, the multi-robot system’s ability to independently change the formation to avoid collision among the robots or with obstacles is critical. In this study, a multi-robot adaptive formation control framework based on deep reinforcement learning is proposed. The framework consists of two layers, namely the execution layer and the decision-making layer. The execution layer enables the robot to approach its target position and avoid collision with other robots and obstacles through a deep network trained by a reinforcement learning method. The decision-making layer organizes all robots into a formation through a new leader–follower configuration and provides target positions to the leader and followers. The leader’s target position is kept unchanged, while the follower’s target position is changed according to the situation it encounters. In addition, to operate more effectively in environments with different levels of complexity, a hybrid switching control strategy is proposed. The simulation results demonstrate that our proposed formation control framework enables the robots to adjust formation independently to pass through obstacle areas and can be generalized to different scenarios with unknown obstacles and varying number of robots.

An Adaptive Control Framework for Underactuated Switched Euler–Lagrange Systems

The control of underactuated Euler–Lagrange systems with uncertain and switched parameters is an important problem whose solution has many applications. The problem is challenging as standard adaptive control techniques do not extend to this class of systems due to structural constraints that lead to parameterization difficulties. This note proposes an adaptive switched control framework that handles the uncertainty and switched dynamics without imposing structural constraints. A case study inspired by autonomous vessel operations is used to show the effectiveness of the proposed approach.

Adaptive control and management of multiple nano‐grids in an islanded dc microgrid system

AbstractThis paper presents an adaptive control framework for the flexible and effective management and control of clustered DC nano‐grids (NGs) in an islanded DC microgrid system. It is assumed that each NG contains a photovoltaic (PV) system, a battery energy storage system (BESS), local loads, and a gateway (GW) module. Each NG has a hierarchical control system consisting of a decision‐making module and low‐level controllers. The decision‐making module ensures various desirable features including plug‐and‐play operation of NGs, maximum utilization of PV power generations, and avoiding state of charge (SoC) violation of batteries. Moreover, an adaptive model predictive control (AMPC) strategy is proposed to regulate the voltage of the NG local DC buses in the presence of non‐linear loads. This approach improves the performance of the NG voltage control system and reduces the current ripples of BESSs, thereby enhancing the lifetime of the batteries. In addition, a smart switching consensus‐based control strategy is designed that provides flexible power sharing among the NGs to balance the SoC of BESSs in which the BESSs altogether imitate the behaviour of a single cloud energy storage system (ESS). Finally, the performance of the proposed control system is verified by simulating the DC microgrid in MATLAB/Simulink.

Data-Driven-Based Event-Triggered Control for Nonlinear CPSs Against Jamming Attacks.

This brief considers the security control problem for nonlinear cyber-physical systems (CPSs) against jamming attacks. First, a novel event-based model-free adaptive control (MFAC) framework is established. Second, a multistep predictive compensation algorithm (PCA) is developed to make compensation for the lost data caused by jamming attacks, even consecutive attacks. Then, an event-triggering mechanism with the dead-zone operator is introduced in the adaptive controller, which can effectively save communication resources and reduce the calculation burden of the controller without affecting the control performance of systems. Moreover, the boundedness of the tracking error is ensured in the mean-square sense, and only the input/output (I/O) data are used in the whole design process. Finally, simulation comparisons are provided to show the effectiveness of our method.

Novel Compliant Control of a Pneumatic Artificial Muscle Driven by Hydrogen Pressure Under a Varying Environment

In this article, a pneumatic artificial muscle (PAM) based on a metal hydride (MH) is considered for a compact compliant actuator. It is suitable for broad applications of the human–robot interaction (HRI). To address the problem of the HRI represented by a varying environment, a compliant control is introduced. In fact, the bottlenecks of improving the performance in the compliant control of the PAM actuator are: an inherent nonlinear dynamics of a PAM, the parametric and nonlinear uncertainties influenced by a varying environment, and an additional high dimension introduced by an MH employed as a driving force for the PAM. We propose a learning-based adaptive robust control (LARC) framework to tackle these challenges. A Bayesian learning technique deals with the parameter adaptation for the adaptive control. The effectiveness of the LARC has been examined in extensive experiments of tracking control.

A novel event-triggered adaptive tracking control framework for a manipulator with aperiodic neural network estimation

PurposeThe purpose of this study is developing the minimum parameter learning law for the weight updating, which reduces the updating of neural network (NN) weight only at triggering instants and makes a trade-off between the estimation accuracy and triggering frequency such that the computing complexity can be decreased. Besides that, a novel “soft” method is first constructed for the control updating at the triggered instants, to reduce the chattering effect of discontinued renewal of control. Addressing to the proposed control and updating method, a novel dead-zone condition with variable boundary about the triggered control signal is derived to ensure the positivity of adjacent execution intervals.Design/methodology/approachIn this paper, to achieve the motion tracking of manipulator with uncertainty of system dynamics and the communication constraints in the control-execution channel, an adaptive event-triggered controller with NN identification is constructed to improve the transmission efficiency of control on the premise of the guaranteed performance. In the proposed method, the NN with intermittent updating is proposed to perform the uncertain approximation with the saved computation, and the triggered mechanism is constructed to regulate the transportation of the signal in the channel of controller-to-actuator.FindingsAccording to the impulsive Lyapunov function, it can be proved that all the signals are semi-global uniformly ultimately bounded, and the positivity of adjacent execution intervals is also guaranteed by the proposed method. In addition, the chattering effect of control updating at the jumping instants can be relieved by the proposed “soft” mechanism, such that the control accuracy and stability can be guaranteed. Experiments on the JACO2 real manipulator are carried out to verify the effectiveness of the proposed scheme.Originality/valueTo the best of the author’s knowledge, this study is firstly to propose a “soft” method to reduce the chattering effect caused by discontinuous updating. Addressing to the updating method designed above, a novel dead-zone condition with variable threshold and boundary is first constructed to ensure the positivity of execution intervals.

Data-driven model-free adaptive sliding mode control for electromagnetic linear actuator

A data-driven model-free adaptive sliding mode control (MFASMC) method is proposed to tackle the problems of inaccurate model and time-varying parameters of a micro electro-magnetic linear actuator system, considering the dependence of existing model-based control methods on the system dynamics model and the impact of unmodeled dynamics on the control performance. Firstly, the pseudo-gradient concept in the model-free adaptive control (MFAC) framework is used to transform the electromagnetic linear actuator dynamics model, which is difficult to obtain parameters accurately, into a full-format dynamic linearized data model, the dependence of the controller on the electromagnetic linear actuator model is reduced. To compensate for the effects of unknown perturbations, an improved discrete sliding mode exponential convergence law is introduced to derive a new composite control algorithm based on the pseudo partial derivative estimator, which improves the robustness of the control system. Then, the convergence of the control error and the stability of the system are demonstrated by theoretical analysis. The results show that the proposed MFASMC reduces the 10 mm step response time of the electromagnetic linear actuator by 46.3% and 25.3% compared with proportional-integral-derivative control (PID) and MFAC. The phase lag time and root-mean-square error of MFASMC under sinusoidal conditions are 0.8 ms, 0.178 mm respectively, and the steady-state error does not exceed 0.05 mm. The experimental and simulation results keep the error within 5%, the proposed control algorithm has good trajectory tracking response speed and control accuracy, and has good robustness to system uncertainty and external force disturbance.

Adaptive consensus control for output-constrained nonlinear multi-agent systems with actuator faults

This paper addresses the problem of leader-follower consensus fault-tolerant control for a class of nonlinear multi-agent systems with output constraints. Specifically, a new nonlinear state transformation function is proposed to deal with the asymmetric constraint on output. Moreover, by integrating backstepping and radial basis function neural network approaches, an adaptive consensus control framework is developed with a single parameter estimator, which mitigates the computation of control algorithm in comparison with conventional adaptive approximation based control techniques. Then an adaptive compensation method is proposed to eliminate the effect of actuator failure. Under the proposed control scheme, all the closed-loop signals of the systems are bounded and the consensus tracking error converges to an adjustable small neighborhood of zero. To evaluate the developed control algorithm, a group of four networked two-stage chemical reactors is used to illustrate the effectiveness of the theoretic results obtained.