Robust Adaptive Control Algorithm Research Articles

Adaptive Disturbance Suppression Method for Servo Systems Based on State Equalizer.

Disturbances in the aviation environment can compromise the stability of the aviation optoelectronic stabilization platform. Traditional methods, such as the proportional integral adaptive robust (PI + ARC) control algorithm, face a challenge: once high-frequency disturbances are introduced, their effectiveness is constrained by the control system's bandwidth, preventing further stability enhancement. A state equalizer speed closed-loop control algorithm is proposed, which combines proportional integral adaptive robustness with state equalizer (PI + ARC + State equalizer) control algorithm. This new control structure can suppress high-frequency disturbances caused by mechanical resonance, improve the bandwidth of the control system, and further achieve fast convergence and stability of the PI + ARC algorithm. Experimental results indicate that, in comparison to the control algorithm of PI + ARC, the inclusion of a state equalizer speed closed-loop compensation in the model significantly increases the closed-loop bandwidth by 47.6%, significantly enhances the control system's resistance to disturbances, and exhibits robustness in the face of variations in the model parameters and feedback sensors of the control object. In summary, integrating a state equalizer speed closed-loop with PI + ARC significantly enhances the suppression of high-frequency disturbances and the performance of control systems.

Adaptive Disturbance Suppression Method for Servo Systems Based on State Equalizer

Disturbances in the aviation environment can compromise the stability of the aviation optoelectronic stabilization platform. Traditional methods, such as the proportional integral adaptive robust (PI + ARC) control algorithm, face a challenge: once high-frequency disturbances are introduced, their effectiveness is constrained by the control system’s bandwidth, preventing further stability enhancement. A state equalizer speed closed-loop control algorithm is proposed, which combines proportional integral adaptive robustness with state equalizer (PI + ARC + State equalizer) control algorithm. This new control structure can suppress high-frequency disturbances caused by mechanical resonance, improve the bandwidth of the control system, and further achieve fast convergence and stability of the PI + ARC algorithm. Experimental results indicate that, in comparison to the control algorithm of PI + ARC, the inclusion of a state equalizer speed closed-loop compensation in the model significantly increases the closed-loop bandwidth by 47.6%, significantly enhances the control system’s resistance to disturbances, and exhibits robustness in the face of variations in the model parameters and feedback sensors of the control object. In summary, integrating a state equalizer speed closed-loop with PI + ARC significantly enhances the suppression of high-frequency disturbances and the performance of control systems.

Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system.

A robust model-free adaptive iterative learning control (R-MFAILC) algorithm is proposed in this work to address the issue of laterally controlling an autonomous bus. First, according to the periodic repetitive working characteristics of autonomous buses, a novel dynamic linearized method used in the iterative domain is utilized, and a time-varying data model with a pseudo gradient (PG) is given. Then, the R-MFAILC controller is designed with a proposed adaptive attenuation factor. The proposed algorithm's advantage lies in the R-MFAILC controller, which solely utilizes the input and output data of the regulated entity. Moreover, the R-MFAILC controller has strong robustness and can handle the nonlinear measurement disturbances of the system. In simulations based on the Truck-Sim simulation platform, the effectiveness of the proposed algorithm is verified. A rigorous mathematical analysis is employed to demonstrate the stability and convergence of the proposed algorithm.

Fuzzy adaptive control of coal gangue sorting parallel robot with variable load

ABSTRACT Delta parallel robot is widely used in the high-speed sorting industry with a light load and constant load. Its motion control is based on kinematic planning, without considering the influence of load and disturbance. The gangue sorting robot has the characteristics of a large load and random mass change, so traditional kinematics planning can not meet the control requirements. A fuzzy adaptive robust control algorithm is proposed to solve the problems of unstable motion and jitter in the coal gangue sorting robot. By establishing the mathematical model of the fuzzy system, the uncertain terms of the dynamic parameters caused by the random changes in the load are approximated. At the same time, to reduce the number of fuzzy rules and the amount of fuzzy calculation, the uncertain terms are decomposed into velocity terms and acceleration terms and then approximated, respectively. The Lyapunov analysis of the system is carried out, and the convergence of the system is proved. The simulation results show that the fuzzy adaptive robust control algorithm has a smaller error and faster convergence compared with the computational torque control algorithm when the system is sorting gangue within 8 kg, which improves the stability of gangue grasping.

A backstepping sliding mode control algorithm of electro-hydraulic position servo system of valve-controlled symmetric cylinder based on extended state observer

Electro-hydraulic position servo system of valve-controlled symmetrical cylinder (EPSSVCS) is a very important and widely used electro-hydraulic servo system, the performance of which is often affected by friction nonlinearity and external random disturbances together with unmodeled dynamic factors such as parameter uncertainty in practical work. To improve the tracking performance of the system, a backstepping sliding mode control algorithm (BSMC) based on an extended state observer was presented in this paper. Firstly, a nonlinear mathematical model of the EPSSVCS was established which takes into account the composite disturbance induced by the systematic friction nonlinearity, external disturbances and unmodeled dynamic factors. Then, an extended state observer (ESO) was designed which can effectively estimate the velocity, acceleration, and composite disturbance of the valve-controlled cylinder online. Furthermore, a kind of BSMC was presented based on the online ESO estimates and the displacement feedback signals. The control law was given and the system stability was proved. The analysis results showed that the output signal of the system can effectively track the input signal under the influence of typical external disturbances and unmodeled dynamic factors, which illustrated the effectiveness of the established nonlinear mathematical model and the stability of the designed control system. Moreover, by taking the parameters of the electro-hydraulic servo system of a certain type of CNC machine tool as a real example, the proposed algorithm (BSMC) was compared with the conventional PID control algorithm, the backstepping sliding mode control algorithm (BSC), and the adaptive robust control algorithm (ARC), and the results verified the superiority of the BSMC. This work may provide a useful reference for the research in the field of related control systems.

Event‐triggered adaptive consensus control of nonlinear multi‐agent systems with unknown control directions and actuator saturation

AbstractThis article addresses the event‐triggered adaptive consensus control of nonlinear multi‐agent systems with unknown control direction and actuator saturation. A new robust adaptive control algorithm based on an event‐triggered mechanism is designed. The smooth Lipschitz function approximates the saturated nonlinear function, while the Nussbaum function handles unknown control directions and residual terms. The event‐triggered mechanism is designed to determine the time of communication, significantly reducing the communication burden. An additional estimator is utilized to deal with unknown parameters involved in neighbor dynamics and prevent information exchange to consistency errors between connected subsystems. The results show that all the signals of the closed‐loop system are uniformly bounded, and the consensus tracking error converges to a bounded set. Meanwhile, Zeno's behavior is eliminated. Simulation results confirm the superiority of the proposed method.

A robust variable-λ least logarithmic hyperbolic cosine function adaptive filtering-based control algorithm for grid-connected solar PV system

This paper implements a robust variable-λ least logarithmic hyperbolic cosine function adaptive filtering (VLlncosh)-based control algorithm for a grid-connected solar photovoltaic system (GCPVS) which includes DSTATCOM features while integrating PV power to the grid at enhanced power quality (PQ). In this paper, a single-stage topology of GCPVS is considered, which can reduce the complexity, cost, and maintenance associated with DC-DC converter employed in two-stage topology. In GCPVS, the nonlinear loads, which deteriorate the quality of the power, are attached to the point of common coupling (PCC), and maximum extracted PV power is allocated between these loads and the grid. The power transfer at the grid takes place at unity power factor (UPF). The proposed VLlncosh algorithm estimates the fundamental weight components of the distorted load current for grid reference currents generation with a variable learning parameter to ensure fast initial convergence and more steady-state accuracy. The control algorithm operates GCPVS such that it provides compensation to the load current harmonics, unbalanced load current, and load reactive power while complying with IEEE-519–2022 standard. The VLlncosh-based control algorithm is implemented for GCPVS modeled in MATLAB/Simulink to evaluate its performance through simulation results. Further, the control algorithm is validated using the experimental prototype setup of GCPVS in the laboratory. The comparative assessment of the VLlncosh algorithm is provided to confirm its superior performance over LMS, sign-error LMS, and LMF algorithms.

High precision control of direct-drive permanent magnet linear motor with adaptive friction compensation

To improve the control precision and stability of Direct-Drive Permanent Magnet Linear Motor (DPMLM) with frictional nonlinearity and uncertain disturbances at a low speed, an adaptive robust integral sign error control algorithm based on friction compensation of dual nonlinear observer is proposed in this study. An improved LuGre friction model is established to describe the friction phenomena of the system. The dual nonlinear observer is designed to observe the internal frictional state of the model. A parameter adaptive law is designed to perform parameter estimation of structured uncertainty. Robust integral of the sign of the error (RISE) term is designed to overcome the frictional nonlinear disturbances. The friction nonlinearity and parameter uncertainty are compensated by feedforward compensation. The bounded stability of the proposed controller is proved by Lyapunov stability theory. As suggested by the experimental results, RMSE index decreased by 10.8% and the control precision was improved effectively by the proposed control algorithm at a low speed.

Neural network-based direct robust adaptive non-fragile fault-tolerant control of amorphous flattened air-ground wireless self-assembly system

PurposeIn this paper, the authors take an amorphous flattened air-ground wireless self-assembling network system as the research object and focus on solving the wireless self-assembling network topology instability problem caused by unknown control communication faults during the operation of this system.Design/methodology/approachIn the paper, the authors propose a neural network-based direct robust adaptive non-fragile fault-tolerant control algorithm suitable for the air-ground integrated wireless ad hoc network integrated system.FindingsThe simulation results show that the system eventually tends to be asymptotically stable, and the estimation error asymptotically tends to zero with the feedback adjustment of the designed controller. The system as a whole has good fault tolerance performance and autonomous learning approximation performance. The experimental results show that the wireless self-assembled network topology has good stability performance and can change flexibly and adaptively with scene changes. The stability performance of the wireless self-assembled network topology is improved by 66.7% at maximum.Research limitations/implicationsThe research results may lack generalisability because of the chosen research approach. Therefore, researchers are encouraged to test the proposed propositions further.Originality/valueThis paper designs a direct, robust, non-fragile adaptive neural network fault-tolerant controller based on the Lyapunov stability principle and neural network learning capability. By directly optimizing the feedback matrix K to approximate the robust fault-tolerant correction factor, the neural network adaptive adjustment factor enables the system as a whole to resist unknown control and communication failures during operation, thus achieving the goal of stable wireless self-assembled network topology.

Disturbance observer-based adaptive neural control for underactuated surface vehicle with constraint of input saturation

In this paper, a robust adaptive neural control algorithm is provided to solve the problem of path-following control for underactuated surface vehicle (USV) subject to input saturation, in the presence of model uncertainties and unknown marine environmental disturbance.In the algorithm, through the application of neural network (NN), an adaptive disturbance observer (ADO) is proposed to observe the unknown disturbance, which does not need the precise information of the upper bound of the disturbance, and the model uncertainty can be appropriated simultaneously. Specially, since the ADO and control law share the same set of NN, the number of adaptive parameters of the controller can be greatly reduced. After that, dynamic surface control (DSC) method is used to avoid repeated derivative in the process of back-stepping, which solve the “calculation explosion” problem. An auxiliary system is designed to compensate errors caused by input saturation, which solve the input saturation problem of actuators. Through Lyapunov stability analysis, it is proved that all signals of the closed-loop system are semi-globally ultimately uniformly bounded (SGUUB). Finally, some experiments are conducted to demonstrate the effectiveness of the algorithm.

An adaptive robust path-tracking control algorithm of multi-articulated and redundantly actuated virtual track train

Multi-articulated and redundantly actuated virtual track trains (VTTs) are inevitably exposed to unknown exogenous disturbances and various operating conditions. To address the complex over-actuated tracking-control problem, this study proposes a decoupling control scheme for an all-wheel independent drive and all-wheel active steering VTT composed of multiple modules, wherein the adaptive robust path-tracking and longitudinal speed control algorithms of the VTT are decoupled. An appropriate control-oriented nonlinear dynamic model for VTT path-tracking control is formulated using Lagrange’s equations. To compensate for the model uncertainties and environmental disturbances without priors and parameter estimations, a robust control algorithm using radial basis function neural network and sliding mode is proposed. The asymptotic stability of the algorithm is proved, and its adaptation law is derived using the Lyapunov theory. Moreover, a novel traction/braking control algorithm based on the wheel speed distribution model and double closed-loop control algorithm is suggested. Using a multi-body dynamic model of the VTT with seven modules, simulations are performed to evaluate the effectiveness and superiority of the proposed path-tracking algorithm. The obtained results indicate that the proposed algorithm could realise the expectable performance of the VTT path tracking and longitudinal speed control. Moreover, the proposed control algorithm has good scalability for multi-articulated and redundantly actuated VTT.

Harmonic and synchronous compound control of a dual-valve controlled single-rod electro-hydraulic actuator

This article presents a novel harmonic and synchronous compound control scheme based on the dual-valve parallel structure for controlling a single-rod electro-hydraulic actuator with high-precision control and large-flow requirements. Harmonic and synchronous compound control consists of an adaptive robust control layer and a control law allocation layer. In adaptive robust control layer, an adaptive robust control algorithm is designed to deal with the system nonlinearities and uncertainties via backstepping method. Meanwhile, dynamic surface control technique and “lumped flow” are introduced to solve the differential explosion problem and the multiple input-single output control problem. In control law allocation layer, the harmonic and synchronous compound strategy allocates the control law to two valves reasonably based on system flow and valve frequency characteristics. Furthermore, since the effect of the proportional valve dead-zone can be compensated effectively by combining the estimation of the dead-zone flow by adaptive term, the estimation error compensation by robust term, and the allocation method of control law allocation layer, improved tracking performance can be achieved. Finally, both simulations and experiments are demonstrated to illustrate the advantages of the proposed control scheme.

Intervehicle Security-Based Robust Neural Formation Control for Multiple USVs via APS Guidance

This paper focuses on the intervehicle security-based robust formation control of unmanned surface vehicles (USVs) to implement the formation switch mission. In the scheme, a novel adaptive potential ship (APS)-based guidance principle is developed to prevent intervehicle collisions, which is common and threatening when maneuvering a formation switch. By employing the artificial potential field (APF), the APS can program the real-time attitude reference for USVs by using the security intervehicle distance while achieving the path-following task. As for the control part, a robust adaptive formation control algorithm is proposed to effectively stabilize the USVs to the APS via the fusion of the disturbance observer (DOB) and by using the robust neural damping technique. Regarding the merits of the improved design of the DOB, the weight compression of the neural networks can effectively simplify the structure of the DOB and enhance the observation accuracy of the external disturbance. This can facilitate the avoidance of intervehicle collisions and guarantee the application of the theoretical algorithm in engineering practice. Considerable effort has been made to obtain the semiglobally uniform ultimate bounded (SGUUB) stability via theoretical analysis. Finally, with the sailing scene in a narrow channel, the simulated experiment is illustrated to verify the security performance of the proposed strategy.

Adaptive Robust Control for Nonlinear Mechanical Systems With Inequality Constraints and Uncertainties

The inequality constraints, system nonlinearities, parameter uncertainties, and external disturbances are always unavoidable in practical mechanical systems. This article proposes an adaptive robust control (ARC) algorithm from the view of servo constraint following to tackle the control problem of mechanical systems subject to the above factors. For the inequality constraints, a creative diffeomorphism which could convert the two-sided bounded state variables to the unbounded ones is explored, which could render the transformed nonlinear system free from inequality constraints. For the system uncertainties, a leakage-type ARC algorithm is developed, which could render the system the practical stability. The permanent magnet linear motor (PMLM) system is utilized as a typical application to verify the proposed state transformation and ARC approach. Numerical simulations show that the displacement of the PMLM system could well track the desired trajectory without violating the given bound line.

Event-triggered robust adaptive control for unmanned surface vehicle in presence of deception attacks

This article focuses on a double layer virtual ship guidance–based adaptive event–triggered path-following control for unmanned surface vehicle under the unknown actuator gain and deception attacks. In the guidance unit, the adaptive virtual ship is developed to derive the smooth reference paths for the controlled objective. That can effectively remove the abrupt phenomenon of actuators. Associated with the double layer virtual ship guidance strategy, a robust adaptive control algorithm is proposed for the unmanned surface vehicle by fusing the event-triggered rule and the adaptive compensated technique. In the algorithm, the deception attacks caused by the communication channel were compensated by constructing the attack adaptive parameters. That is critical to improve the accuracy and stabilize of the closed-loop control system. Besides, the transmission burden from the controller to the actuator was reduced for merits of the input-based event-triggered mechanism. Finally, the theoretical stability was proofed by the Lyapunov theorem, and the well performance was obtained through numerical simulation in presence of the time-varying disturbances.

Adaptive robust control algorithm for enhanced path-tracking performance of automated driving in critical scenarios

Adaptive robust control algorithm for enhanced path-tracking performance of automated driving in critical scenarios

Adaptive Robust Path Tracking Control for Autonomous Vehicles Considering Multi-Dimensional System Uncertainty

As the bottom layer of the autonomous vehicle, path tracking control is a crucial element that provides accurate control command to the X-by-wire chassis and guarantees the vehicle safety. To overcome the deterioration of control performance for autonomous vehicle path-tracking controllers caused by modeling errors and parameter perturbation, an adaptive robust control framework is proposed in this paper. Firstly, the 2-DOF vehicle dynamic model is established and the non-singular fast terminal sliding mode control algorithm is adopted to formulate the control law. The unmeasured model disturbance and parameter perturbation is regarded as the system uncertainty. To enhance the control accuracy, the radial basis forward neural network is introduced to estimate such uncertainty in real time. Then, the dynamic model of an active front steering system is established. The model reference control algorithm is applied for the steering torque control considering model uncertainty brought by the dissipation of manufacturing and mechanical wear. Finally, the Simulink–CarSim co-simulation platform is used and the proposed control framework is validated in two test scenarios. The simulation results demonstrate the proposed adaptive robust control algorithm has satisfactory control performance and good robustness against the system uncertainty.

Robust Adaptive Neural Control for Wing-Sail-Assisted Vehicle via the Multiport Event-Triggered Approach.

This article presents a robust adaptive neural control algorithm for the wing-sail-assisted vehicle to track the desired waypoint-based route, where the event-triggered mechanism is with the multiport form. The main features of the proposed algorithm are three-fold: 1) the communication burden, in the channel from the sensor to the controller as well as the actuator, has been reduced for the merits of the multiport event-triggered approach. The feedback error signals and the control input will be updated only on the event-triggered time point; 2) for the wing-sail-assisted vehicle, the thrust force is provided by devices with the propeller and the sail. From this consideration, the proper sail force compensation is derived on the basis of information about the current heading angle and the wind direction. The corresponding control law can guarantee the energy-saving for the propeller; and 3) in the algorithm, the system uncertainties are remodeled by the neural-network approximator. Furthermore, by fusion of the robust neural damping and dynamic surface control (DSC) techniques, the corresponding gain-related adaptive law is developed to address constraints of the gain uncertainty and the environmental disturbances. Through the Lyapunov theorem, all signals of the closed-loop control system have been proved to be with the semiglobal uniform ultimate bounded (SGUUB) stability, including the triggered time point and the intermediate triggered interval. Finally, the numerical simulation and the practical experiment are illustrated to verify the effectiveness of the proposed strategy.

Stability and Fast Transient Performance Oriented Motion Control of a Direct-Drive System With Modeling Uncertainties, Velocity, and Input Constraints

Guaranteeing the stability and fast transient performance of a direct-drive system simultaneously, which is practically subject to various modeling uncertainties, velocity, and input constraints, is both theoretically and technically challenging. To this end, an online time-optimal trajectory optimization-based saturated adaptive robust control algorithm is proposed in this article. The structure of the proposed control approach consists of two levels. In the lower level, a saturated adaptive robust controller (SARC) is synthesized. Meanwhile, an online time-optimal trajectory optimization algorithm is integrated in the upper level. Typically, the input constraint is divided into the feedforward constraint and feedback constraint, respectively. In this way, the modeling uncertainties and feedback constraint are handled by the SARC in the lower level, while the online time-optimal trajectory optimization algorithm in the upper level deals with the feedforward and velocity constraints. The stability analysis of the overall two-level control framework is presented and a minimum-time transient response is also theoretically guaranteed. Comparative experiments on a linear motor justify the theoretical results and superiorities of the proposed two-level control approach.

Adaptive robust force control of an underactuated walking lower limb hydraulic exoskeleton

This paper presents an adaptive robust interaction force control algorithm for an underactuated walking lower limb hydraulic exoskeleton. In order to deal with the problem of passive joints (less of control inputs), holonomic constraints from the wearer are considered in system dynamics, which help transform the underactuated exoskeleton dynamics into fully actuated dynamics in Cartesian space. Based on the established dynamic model, adaptive robust interaction force controllers are presented both for the phase of double leg support and single leg support to effectively deal with multiple walking phases, negative effect of high-order nonlinearities of hydraulic systems as well as various model uncertainties and external disturbances. An additional torque allocation method is proposed to deal with the over-actuated characteristic in double leg support phase. Comparative simulations are carried out. The results demonstrate that the proposed force controller can achieve precision and strong robust interaction force control performance to load change and variation of the human trajectory.