Robust Adaptive Control Method Research Articles

An adaptive robust nonsingular fast terminal sliding mode controller based on wavelet neural network for a 2-DOF robotic arm

The paper proposes a new robust adaptive control method for a two-link robot manipulator to enhance high tracking control performance despite the presence of dynamic uncertainties and unknown disturbances. The paper examines a Non-Singular Fast Time Sliding Mode (NFTSM) controller based on Wavelet Neural Network (WNN). The Wavelet Network is employed to approximate the upper bound of uncertainties and disturbances. In addition, a compensation term is added to the NFTSM control to attenuate the effect of uncertainties, including unavoidable approximation errors and unknown disturbances. Therefore, to ensure high tracking accuracy, chattering phenomenon reduction, and fast response against approximation errors and uncertainties. The parameters of the controller are tuned online by an adaptive learning algorithm, and online adaptive control laws are determined by the Lyapunov stability theorem. Due to these techniques, the suggested control drives the system to the desired performance where tracking errors converge to zero within a finite time. Simulations performed on a two-link robotic arm demonstrate a higher performance of the proposed adaptive robust NFTSMC based on WNN methodology compared to some advanced control strategies.

Robust adaptive finite-time attitude tracking control of a 3D pendulum with external disturbance: numerical simulations and hardware experiments

The three-degree-of-freedom (3D) pendulum has been used as a benchmark in the field of nonlinear dynamics and control. Nonetheless, the attitude control of 3D pendulum is still an open problem presently since some issues remain not well addressed. In this paper, a robust adaptive finite-time attitude control method is proposed for the attitude tracking control of a 3D pendulum with external disturbance. First, a baseline finite-time attitude controller is designed based on the adding a power integrator technique. Then, a finite-time disturbance observer is designed to exactly estimate the unknown external disturbance. Finally, a robust adaptive finite-time attitude controller is constructed by integrating the baseline finite-time attitude controller with the finite-time disturbance observer. The proposed robust adaptive finite-time attitude controller can guarantee the global finite-time stability of the whole closed-loop system even in the presence of external disturbance owing to the feedforward dynamic compensation. Numerical simulations and hardware experiments are both performed to illustrate the effectiveness and superiority of the proposed control method.

Robust Adaptive Contact Force Control of Pantograph–Catenary System: An Accelerated Output Feedback Approach

The pantograph-catenary system plays the crucial role in high-speed electric trains for electrical power collection, where continuity and smoothness of the contact between the pantograph and the catenary is one of the key factors ensuring high-quality power collection essential for safe and reliable train operation. In this article, a nonlinear and highly coupled model for the pantograph-catenary system is established, and an accelerated robust adaptive output feedback control method based on a high-gain observer is proposed to solve the contact force tracking control problem of the pantograph-catenary system using output information only. It is shown that, with the proposed method, the tracking error of the contact force is ensured to be ultimately uniformly bounded with an accelerated preassignable decay rate, resulting in smooth contact force and good-quality current collection. Both theoretical analysis and numerical simulation confirm the effectiveness and benefits of the method.

Robust adaptive anti-windup wheel slip tracking control for intelligent vehicle with fast terminal sliding mode observer

Aiming at the requirement of the intelligent vehicle for the fast and stable tracking control of the wheel slip, a novel robust adaptive anti-windup wheel slip tracking control method with fast terminal sliding mode observer is proposed. First, a fast terminal sliding mode observer based on equivalent control on the sliding surface is proposed to estimate the states of the wheel slip dynamic system to lay the foundation for the full state feedback control law design. Second, a robust adaptive anti-windup wheel slip tracking control law with lumped uncertainty observer and additional anti-windup dynamics is derived based on Lyapunov-based method. The lumped uncertainty observer utilizes the nonlinear mapping ability of the radius basis function neural network to estimate and compensate the lumped uncertainty of the system, and the unknown optimal weight vector of the radius basis function neural network is updated by adaptive law. The additional anti-windup dynamics is used to suppress the effect of the input saturation on the stability of the system. Finally, the performance of the proposed method is verified through simulations of various maneuvers on vehicle dynamics simulation software.

Robust adaptive repetitive control for a class of nonlinear periodically time-varying systems

This paper presents a robust adaptive repetitive control (RARC) method for a class of periodically time-varying nonlinear systems with aperiodic uncertainties. A σ modification is introduced in the learning algorithm of RARC, in order to guarantee robustness of the system undertaken. The closed-loop type learning algorithm is examined and it is shown that the realisability cannot be assured when the σ modification is applied. To avoid the causality contradiction, an open-loop type learning algorithm with switching σ modification is proposed to guarantee robustness and achieve the asymptotic convergence of the tracking error, when the disturbances disappear. Extension to the RARC for robotic manipulators is given and the numerical simulation is carried out to verify the effectiveness of the learning control scheme.

A robust adaptive control method for reactive power of doubly-fed induction wind generator

Some of the existing control strategies for reactive power of doubly-fed wind generators are only based on the simplified transfer function model without considering the existence of disturbance and some require real-time adjustment of the algorithm to meet different operating conditions. In order to overcome the above problems, a robust adaptive control method is introduced into the field of reactive power control of doubly-fed induction wind generator in this paper. An adaptive reactive power control strategy for the grid-side converter of doubly-fed wind generator is proposed, and the effectiveness of the algorithm is verified through simulation verification.

Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

A new model-free adaptive robust control method has been proposed for the robotic exoskeleton, and the proposed control scheme depends only on the input and output data, which is different from model-based control algorithms that require exact dynamic model knowledge of the robotic exoskeleton. The dependence of the control algorithm on the prior knowledge of the robotic exoskeleton dynamics model is reduced, and the influence of the system uncertainties are compensated by using the model-free adaptive sliding mode controller based on data-driven methodology and neural network estimator, which improves the robustness of the system. Finally, real-time experimental results show that the control scheme proposed in this paper achieves better control performances with good robustness with respect to system uncertainties and external wind disturbances compared with the model-free adaptive control scheme and model-free sliding mode adaptive control scheme.

Observer based adaptive robust Feedback-linearization control for VVER-1000 nuclear reactors with bounded axial power distribution based on the validated multipoint kinetics reactor model

In nuclear reactor, spatial oscillations in neutron flux distribution resulting from xenon reactivity feedback are a matter of concern. If the oscillations in power distribution are not controlled, power density and rate of change of power at some locations in the reactor core may exceed their respective limits causing the nuclear power plant instability. Therefore, during the design stages of any Pressurized Water Nuclear Reactor (P.W.R), it is necessary to identify the existence of xenon oscillation and to design suitable control strategy for regulating the spatial power distribution. In each nuclear power plant, Load-following control is one of the most important techniques for nuclear reactor regulation. A novel nonlinear controller called observer-based adaptive robust feedback-linearization controller for VVER-1000 nuclear reactors is presented in this paper. This novel control strategy is then applied to the Axial power distribution control during load following operation in the VVER-1000 nuclear reactors.The reactor core is simulated based on the validated four nodes kinetics reactor model and three groups of delayed neutron precursor’s concentration based on the Skinner-Cohen model. Considering the limitations of the xenon concentrations and delayed neutrons precursor’s densities physical measurement, an adaptive sliding mode observer is designed to estimate their values and finally adaptive robust feedback-linearization based on the adaptive sliding mode observer and nodal kinetics reactor model is presented to AO control during load following operation for nuclear reactors. The stability analysis is given by means Lyapunov approach, thus the designed control system is guaranteed to be stable within a large range.Simulation results show that robust control and state estimation with adaptive robust feedback-linearization and adaptive Sliding mode methodologies can be achieved in nuclear plant systems with diverse applications including control and estimation in the presence of model uncertainties and external disturbances. One significant finding to emerge from this paper is that the Observer based Adaptive robust feedback-linearization control method provides a robust, high-performance and automatic control mechanism at all the power ranges of operation for P.W.R control system.

RBF Neural Network Adaptive Robust Sliding Mode Control Method of Artillery Ammunition Transfer Arm

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network for a kind of value controlled asymmetric cylinder electro-hydraulic servo system of an ammunition transfer arm in the presence of uncertain nonlinearity and parameter uncertainty. On the premise of setting the expected trajectory, using RBF neural networks to approximate unknown parameters, by setting appropriate neural network parameters and adaptive terms, the change trend of position parameter was estimated. The stability of close loop system is verified by the Lyapounov theory. Compared with the simulation results of PID control method and expected trajectory, RBF Neural Net sliding mode control method has smaller system tracking error, faster response and better tracking performance.

Adaptive Robust Triple-Step Control for Compensating Cogging Torque and Model Uncertainty in a DC Motor

Eliminating the influence of cogging torque and model uncertainty on the tracking control of a dc motor when its speed varies nonperiodically is a challenge. In this paper, an adaptive robust triple-step control method is proposed for compensating cogging torque and model uncertainty. First, a new presentation of the cogging torque and a simplified model of the friction torque are presented to facilitate the online estimation of the unknown model parameters. The load torque, motor disturbance, and model errors are considered as model uncertainty. Based on these considerations, a control-oriented model that contains unknown parameters and model uncertainty is obtained. Second, benefitting from the new presentation, an adaptive algorithm is employed to identify the unknown parameters online. The model uncertainty is estimated by an extended state observer. Third, the model-based triple-step nonlinear method is extended to a system with both parameter uncertainty and model uncertainty, and an adaptive robust triple-step nonlinear controller is derived. The robust stability of the closed-loop system is proven in the framework of Lyapunov theory. Finally, the effectiveness and the satisfactory control performance of this controller are evaluated through comparative experiments on a J60LYS05 motor.

Robust adaptive fault-tolerant control for time delay uncertain nonlinear systems with time-varying performance bounds

In this paper, a robust adaptive fault tolerant controller guaranteeing with time-varying performance bounds is designed for a class of time delay uncertain nonlinear systems subject to actuator failures and external disturbance. The influence of time delay on the system is mitigated and the system performance can be guaranteed by introducing a positive nonlinear control gain function and the generalised restricted potential function. A new method with more design degrees of freedom is developed to ensure the norm of the system state within a-priori, user-defined time varying performance bounds. Using the online estimation information provided by adaptive mechanism, a robust adaptive fault-tolerant control method guaranteeing time varying performance bounds is proposed. It is shown that all the signals of the resulting closed-loop system are bounded and the system state less than a-priori, user-defined performance bounds. Finally, simulation results are given to demonstrate the efficacy of the proposed fault-tolerant control method.

Event-Triggered Adaptive Control for Uncertain Constrained Nonlinear Systems With Its Application

This article is devoted to the event-triggered adaptive control design for uncertain nonlinear systems with full state constraints. A robust adaptive control method enabling the codesign of event-triggering mechanism is proposed, in which the communication burden between controllers and actuators is reduced, and both the physical limitation of the plant with uncertainties and the measurement errors introduced by event-triggering mechanisms can be simultaneously addressed. In addition, a priori knowledge of the signs of unknown virtual control coefficients is not required in the presented controller design methods. Furthermore, Lyapunov stability analysis guarantees that all states in the closed-loop nonlinear system are bounded, the state constraints are not violated, and the tracking errors are driven to a compact set. Finally, a designed example is given to illustrate the effectiveness and advantages of the presented design approaches.

Robust adaptive steering control for unmanned surface vehicle with unknown control direction and input saturation

SummaryA robust adaptive steering control method is proposed to solve the control problem of the unmanned surface vehicle (USV) with uncertainties, unknown control direction, and input saturation. In the controller design process, the adaptive fuzzy system is incorporated into dynamic surface control (DSC) to approximate the uncertainty term induced by external environmental disturbances and model parameters. Then, the Nussbaum function is used to eliminate the requirement for a priori knowledge of the control direction. Besides, to handle the input saturation, the adaptive fuzzy DSC is extended by a second‐order nonlinear filter and antisaturation auxiliary function to compensate for the magnitude and rate saturation of the rudder. All signals of the closed‐loop system are proven to be uniformly ultimately bounded (UUB) by Lyapunov theorem and the Lemma of Nussbaum gain, and the course error can converge to a small neighborhood of zero through choosing design parameters appropriately. Finally, simulation results and comprehensive comparisons are shown for the USV course system, which is demonstrative of the proposed controller's effectiveness and robustness.

Tracking control of MIMO nonlinear systems under full state constraints: A Single-parameter adaptation approach free from feasibility conditions

This paper investigates the tracking control problem of a class of multi-input multi-output (MIMO) nonlinear systems under asymmetric full-state constraints. By integrating a nonlinear transformed function (NTF) with the upper bound estimation technique, we develop a robust adaptive dynamic surface control (DSC) method that exhibits several attractive features: 1) both symmetric and asymmetric state constraints are directly addressed without the need for converting the state constraints into new constraints on tracking errors (as used in the Barrier Lyapunov Function (BLF) in most existing works). Such control scheme (with its structure unchanged) is also applicable to the systems free from state constraints; 2) the undesirable feasibility conditions on virtual control laws commonly involved in most existing works are completely eliminated; and 3) the utilization of NTF-based DSC and upper bound estimation technique not only avoids the explosion of complexity problem in traditional backstepping design, but also allows a structurally simple and computationally inexpensive control scheme to be developed, in which only one single parameter updating is needed, significantly reducing the design complexity and online computation. The effectiveness of the proposed scheme is verified via simulation.

Addressing Control Implementation Issues in Robotic Systems Using Adaptive Control

SummaryThis paper addresses three control implementation issues for trajectory tracking of robotic manipulators: unmodeled dynamics, unknown input saturation and peaking effects during the transient phase. A model-free first-order robust-adaptive control method is used to deal with the unmodeled dynamics. Robust optimality and stability of the controller are proved using the 𝓗∞ technique and the game-algebraic Riccati equation. An intuitive approach is devised to incorporate the unknown input saturation by modifying the speed of the desired trajectory. The trajectory scaling is performed by using only the state errors. Furthermore, two different techniques are utilized to suppress peaking during the transient response of the trajectory tracking. The first method adds an extra term in the input while the second method uses variable gain to improve the transient response. A systematic procedure for finding the controller parameters is formulated using features, such as rise time and settling time. A three-degree-of-freedom robot manipulator is used for the validation of the proposed controller in simulations and experiments.

Path-tracking of autonomous vehicles using a novel adaptive robust exponential-like-sliding-mode fuzzy type-2 neural network controller

Abstract Structured and unstructured uncertainties together with external disturbances may pose considerable challenges in realizing desired path-tacking and lane keeping of autonomous vehicles. This paper presents a novel robust adaptive indirect control method based on an exponential-like-sliding-mode fuzzy type-2 neural network approach for enhanced path-tracking performance of road autonomous vehicles subject to parametric uncertainties related to vehicle nominal cornering stiffness, road-tire adhesion coefficient, inertial parameters, and forward speed. A hierarchical controller is designed and the stability of the closed-loop system is ensured along with deriving the adaptation laws by employing the Lyapunov stability theorem. The conventional reaching law related to the sliding mode degrades from the system stability and introduces an inherent chattering of the controller input. The convergence law for the sliding surface is adjusted based on a variable exponential sliding manifold to eliminate possible chattering in the buffeting switch zone near the origin. The proposed exponential-like sliding surface guarantees the swift and smooth global asymptotic convergence of the tracking error toward zero. Furthermore, an adaptive look-ahead path-tracking error term is introduced as an auxiliary error criterion to improve the vehicle path-tracking performance. The effectiveness of the proposed controller is verified through Matlab/Simulink–CarSim co-simulations and comparisons with selected reported control methods for two different road maneuvers. The results suggested substantially improved tracking performance by the proposed controller with robustness to withstand against the perturbed parameters and external disturbances.

MRAC-Based Voltage Controller for Three-Phase CVCF Inverters to Attenuate Parameter Uncertainties Under Critical Load Conditions

This paper investigates a robust model reference adaptive control (MRAC) method for a three-phase constant-voltage constant-frequency (CVCF) inverter with an output LC filter. The proposed MRAC method is designed to stabilize the error dynamics of the system by a feedback control term in the steady state and attenuate the parameter uncertainties of the system by an updated MRAC term. Unlike the conventional proportional–derivative control (PDC) scheme, the proposed MRAC scheme ensures the fast convergence of the output errors to the exponential trajectories predefined by the reference models. Furthermore, the adaptive state-feedback mechanism can guarantee the fast dynamic response in the transient state without using load current sensors or observers. The asymptotic stability is mathematically proven by a Lyapunov theory. The feasibility of the proposed controller is confirmed through extensive experimental studies on a prototype three-phase CVCF inverter with a TI TMS320LF28335 DSP. Finally, comparative experimental results of three control methods (i.e., conventional PDC, feedback linearization control, and proposed MRAC) are provided to validate the superior performance of the proposed method such as fast transient response, low total harmonic distortion, and robustness to parameter uncertainties under critical load conditions (i.e., abrupt load changes, unbalanced loads, and distorted nonlinear loads).

A robust-adaptive linearizing control method for sensorless high precision control of induction motor

Even if there exists remarkable applications of induction machines in variable speed drives and also in speed sensorless control in the low–high speed region, open/closed loop estimators in the literature utilized on induction machine sensorless position control vary regarding to their accuracies, sensitivity, and robustness with respect to the variation of model parameter. The deterioration of dynamic performance depends on the lack of estimation techniques which provide trustable information on the flux or speed/position over a wide speed range. An effective estimator should handle the high number of parameter and model uncertainties inherent to induction machines and also torque ripple, the compensation of which is crucial for a satisfactory decoupling and linearizing control to provide the accuracy and precision requirements of demanding motion control in the field of robotics/unmanned vehicle. In this study, to address all of the above-mentioned problems, robust-adaptive linearizing schemes for the sensorless position control of induction machines based on high-order sliding modes and robust differentiators to improve performance were designed. The control schemes based on direct vector control and direct torque control are capable of torque ripple attenuation taking both space and current harmonics into account. The simulation results comprise both the estimation and sensorless speed control of induction machines over a wide operation range, especially at low and zero speed, all of which are promising and indicate significant superiority over existing solutions in the literature for the high precision, direct-drive, speed/position sensorless control of squirrel-cage induction machines.

Precise motion control of an electromagnetic valve actuator with adaptive robust compensation of combustion force

Electromagnetic valve actuators (EMVA) can achieve independent fully flexible control of valve and thus reduce the fuel consumption significantly. In order to improve the performance of the engine, precise motion control of EMVA is requird. Most of the researches published so far focus on the compensations of nonlinearities such as frictions and parameters variations. However, the influence of the varying combustion force on the exhaust valve needs to be considered. An adaptive robust control (ARC) method was developed in this paper to compensate the major nonlinearities, including parameters variations and combustion force variations. The parameters of EMVA system were tuned online via certain adaptation law. Combustion force variations were compensated by robust control law. In addition, current saturation was caused by large combustion force, which was then solved by proposing an anti-windup compensation strategy. The effectiveness of the proposed ARC method was verified by simulations and experiments, and the results show that ARC method can adapt to parameters variations and large changes in combustion force.

Adaptive Anti-Windup Control System Design for ASPR-based Adaptive Output Feedback Control

ASPR-based adaptive output feedback control is considered as one of the simple and robust adaptive control method. However, in the case where there is an input saturation, windup phenomena might occur in the ASPR- based adaptive output feedback control and it may degrade the control performance significantly. In this paper, we propose an anti-windup control strategy for the ASPR-based adaptive output feedback control system. An adaptive anti-windup control based on a model recovery anti-windup control technique is proposed to improve the control performance of ASPR-based adaptive output feedback control systems with an input saturation. The stability of the obtained adaptive anti-windup control system will also be analyzed.