Adaptive Dynamic Control Research Articles

Adaptive 3D Visual Servoing of a Scara Robot Manipulator with Unknown Dynamic and Vision System Parameters

In the present work, we develop an adaptive dynamic controller based on monocular vision for the tracking of objects with a three-degrees of freedom (DOF) Scara robot manipulator. The main characteristic of the proposed control scheme is that it considers the robot dynamics, the depth of the moving object, and the mounting of the fixed camera to be unknown. The design of the control algorithm is based on an adaptive kinematic visual servo controller whose objective is the tracking of moving objects even with uncertainties in the parameters of the camera and its mounting. The design also includes a dynamic controller in cascade with the former one whose objective is to compensate the dynamics of the manipulator by generating the final control actions to the robot even with uncertainties in the parameters of its dynamic model. Using Lyapunov’s theory, we analyze the two proposed adaptive controllers for stability properties, and, through simulations, the performance of the complete control scheme is shown.

Learning-based CO2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation

There have been increasing concerns over the air quality inside buildings as high levels of bio-effluents can cause nausea, dizziness, headaches, and fatigue to the people working in those spaces. First published in 2004 as Standard 62.1, ASHRAE Standard 62.2-2019 requires highly occupied spaces to implement heating, ventilation, and air conditioning (HVAC) that can dilute contaminants produced by occupants. In this regard, occupant-centric ventilation control has been regarded as an effective practice to maintain a satisfactory indoor air quality (IAQ) when dealing with highly variable occupancy environments. However, few established models in current literature and practice consider dynamic occupancy behavior and adaptive IAQ control. To address this gap, a dynamic indoor CO2 model is constructed using machine learning algorithms to forecast CO2 concentrations across a range of forecasting horizons. Herein, we tuned and compared six state-of-the-art learning algorithms—including Support Vector Machine, AdaBoost, Random Forest, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. The algorithms’ performances are validated using CO2 and historical meteorological data collected from a campus classroom with a variable occupancy rate. Simulation results showed that Multilayer Perceptron can strongly predict the volatile CO2 behavior and also outperforms other algorithms in terms of accuracy. Furthermore, a control strategy capable of modeling and detecting dynamic patterns of CO2 level is utilized to modulate the ventilation rate in real-time and also reduce the energy consumption. The proposed controller reduced the HVAC fan’s energy consumption by 51.4% and provided ventilation as needed per the ASHRAE standards.

Adaptive bidirectional dynamic response control of offshore wind turbines with time‐varying structural properties

Wind, wave and seismic loading induced excessive vibration of offshore wind turbines (OWTs) impairs the wind turbine energy output and escalates its fatigue damage. The present study proposes a three-dimensional adaptive pendulum tuned mass damper (3D-APTMD) to control the bidirectional dynamic responses of monopile OWTs exposed to combined wind, wave, seismic loading, and time-varying structural properties. In terms of the proposed adaptive tuning algorithm, the natural frequency and damping property of the 3D-APTMD are tuned in real time to match the wind turbine time-varying dominant frequency caused by environmental and/or structural property variations. Performance of the developed adaptive damper is examined on a monopile 5-MW (megawatt) baseline wind turbine subjected to representative wind, wave, and seismic loading. Time-varying stiffness of the tower and foundation is introduced to simulate damage evolution during service life. A three-dimensional pendulum tuned mass damper (3D-PTMD) and dual linear TMDs (2TMD) are used for comparison. Research results show that when damage occurs, 3D-PTMD and 2TMD become partially off-tuned and less effective. In comparison, the 3D-APTMD which is tuned in real time maintains its effectiveness. It can improve the reduction effect by around 25% and 30% when compared with the 3D-PTMD and 2TMD, respectively. Therefore, the proposed 3D-APTMD is promising for real application to protect OWTs, high-rise buildings, chimneys, and other types of slender structures suffering from excessive bidirectional vibration, and environmental, operational, and/or structural property variations.

A novel optimal control design for unknown nonlinear systems based on adaptive dynamic programming and nonlinear model predictive control

AbstractThis paper presents a novel adaptive optimal control algorithm by combining adaptive dynamic programming with nonlinear model predictive control for unknown continuous‐time affine nonlinear systems. The adaptive optimal control design is realized by the model‐critic‐actor architecture. Model neural network, critic neural network and actor neural network are constructed to approximate the system dynamics, the cost function and the optimal control law respectively. The random initialization of neural networks usually influences the control performance, so three neural networks are initialized properly to obtain the suitable initial values so that the control performance is improved. Especially, actor neural network is initialized to approximate the near‐optimal control law which is obtained from nonlinear model predictive control. The convergence of the proposed algorithm is proved by the Lyapunov theory. Finally, simulation results are provided to illustrate the effectiveness of the proposed algorithm.

Finite-time Dynamic Surface Fault-tolerant Control for Hypersonic Vehicle with Mismatched Disturbances

This paper studies the tracking control problem of hypersonic vehicle in the process of external disturbance, parameters uncertainties and actuator faults. Firstly, a non-triangular velocity and altitude nonlinear subsystem with mismatched disturbances and actuator faults are established based on the feedback linearization model. Secondly, adaptive dynamic surface fault-tolerant controllers are designed for velocity and altitude subsystems by combining nonlinear filter, adaptive control and back-stepping method. Finally, the Lyapunov stability theory and numercial simulation are carried out to verify the availability of the proposed control strategy.

Nonlinear Disturbance Observer-Based Fuzzy Adaptive Finite-Time Dynamic Surface Formation Control of Tractor–Trailer Wheeled Mobile Robots

Nonlinear Disturbance Observer-Based Fuzzy Adaptive Finite-Time Dynamic Surface Formation Control of Tractor–Trailer Wheeled Mobile Robots

Precise Trajectory Tracking Control of Ship Towing Systems via a Dynamical Tracking Target

This paper proposes a novel control strategy to address the precise trajectory tracking control problem of a ship towing system. At first, the kinematics and dynamics models of a ship towing system are established by introducing a passive steering angle and using its structure relationship. Then, by using the motion law derived from its nonholonomic constraints, the relative curvature of the target trajectory curve is applied to design a dynamical tracking target. By applying the sliding mode control and inverse dynamic adaptive control methods, two appropriate robust torque controllers are designed via the dynamical tracking target, so that both the tugboat and the towed ship are able to track the desired path precisely. As we show, the proposed strategy has excellent agreement with the numerical simulation results.

Modelling and neural adaptive vibration control for three-dimensional Timoshenko beam with output restrictions and external disturbances

In this paper, the dynamic modelling and adaptive boundary control of a three-dimensional Timoshenko beam are presented for vibration suppression. In order to ensure that no modal information is left out, partial differential equations (PDEs) are employed to describe the nonlinear dynamic characteristics of the system by applying the extended Hamilton’s principle. Based on the established model, barrier Lyapunov functions (BLFs) are utilised for dealing with system output restrictions. For practical consideration, parameter uncertainties and external disturbances are also taken into account. Under the proposed neural adaptive control scheme, undesirable elastic vibration of the three-dimensional Timoshenko beam is restricted and constrained within given bounds even subject to external perturbations, without needing to know any system parameters. The system stability is proved by the Lyapunov’s direct method. The control performance is verified via using numerical simulations.

Adaptive model-based dynamic event-triggered output feedback control of a robotic manipulator with disturbance

This paper focuses on the stable tracking control of the manipulator with constrained communication, unmeasurable velocity, and nonlinear uncertainties. An NN observer-depended output feedback scheme in the discrete-time domain is developed by virtue of the model-based dynamic event-triggered backstepping technique in the channel of sensor to controller. For generalizing the zero-order-holder (ZOH) implementation, a plant model is built to approximate the triggered states in the time flow, and according to which, the control law is fabricated. Based on model-based error events, we construct a dead-zone triggered condition with a dynamically adjustable threshold, making the threshold evolve with the system performance, to achieve flexible communication scheduling and avoid the accumulation of triggers in small tracking errors. The internal and external nonlinear uncertainties are online compensated by the neural network, and the aperiodic adaptive law is derived in the sense of control stability to save the computation. Finally, the conditions for semi-global ultimate uniform bounded (SGUUB) of all variables are given via impulse Lyapunov analysis, and a positive lower bound in the time interval between consecutive executions to guarantee the Zeno free behavior is obtained. Simulations are conducted on a three-link manipulator to illustrate the effectiveness of our method.

Intelligent flow control algorithm for microservice system

In microservice systems, availability can be ensured through a variety of measures, such as fault tolerance and flow limiting, which are collectively called the flow control. In the current mainstream system design, the flow control rules are usually fixed and set manually, which cannot be dynamically adjusted according to the flow shape. The performance of the system is thus not fully explored. To mitigate this problem, an adaptive dynamic flow control algorithm is proposed. Based on the system's monitoring data and current flow, the algorithm calculates the flow-limiting threshold in real time, and then it implements fine-grained service adaptive flow control to improve the resource utilization. Experimental results show that the performance of the adaptive automatic flow control is better than that of the traditional static method on resource utilization.

Data-driven active flutter control of airfoil with input constraints based on adaptive dynamic programming method

The stable flight region can be extended by adding control flap at the wing trailing edge and combined with active control technology. We studied the active flutter control by considering the input constraints. By designing the data-driven optimal controller, the limit cycle oscillations of a typical two-dimensional airfoil wing can be suppressed with single trailing edge control surface. The traditional control methods always need a precise mathematical model of the system, which put high requirements on system modeling. In this study, a novel data-driven optimal controller is proposed by using the input–output data and without depending on the nonlinear system dynamic model. This model-free approach avoids the effects of modeling errors and system uncertainty. When the data-driven controller is applied, the limit cycle oscillation phenomenon of the airfoil wing is eliminated within several seconds. It can be seen from the numerical simulation result that the data-driven adaptive dynamic programming control method possess superiority and feasibility.

Dynamic Neural Network-Based Adaptive Tracking Control for an Autonomous Underwater Vehicle Subject to Modeling and Parametric Uncertainties

This research presents a way to improve the autonomous maneuvering capability of a four-degrees-of-freedom (4DOF) autonomous underwater vehicle (AUV) to perform trajectory tracking tasks in a disturbed underwater environment. This study considers four second-order input-affine nonlinear equations for the translational (x,y,z) and rotational (heading) dynamics of a real AUV subject to hydrodynamic parameter uncertainties (added mass and damping coefficients), unknown damping dynamics, and external disturbances. We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain. Several numerical simulations validate the satisfactory performance of the proposed DNCS tracking reference trajectories in comparison with a conventional feedback controller with no adaptive compensation. Some graphics showing dynamic approximation of the lumped disturbance as well as estimation of the parametric uncertainty are depicted, validating effective operation of the proposed DNCS when the system is almost completely unknown.

Adaptive dynamic optimal control for triggered networked switched systems under dual‐ended denial‐of‐service attacks

AbstractNetworked switched systems (NSSs) have unique advantages, but the unreliability of networks, such as suffering from attacks, will reduce system performance. Besides, the required control energy to stabilize systems is not considered in existing switching signal design methods, which is worthy of improvement. In this paper, an energy function‐based resilient triggered switching law (EFB‐RTSL) is proposed for NSSs under dual‐ended denial‐of‐service (DoS) attacks and asynchronous switching. For the data communication under DoS attacks, a resilient switch‐triggering mechanism is designed that aims at reducing the unnecessary data transmission and the risk of switching signal being attacked. For the discontinuity of switching signal induced by DoS attacks, a combined structure of attacks‐triggered decision switch and buffer is constructed. Moreover, an integrated model is built to characterize the dual‐ended DoS attacks, and the maximum tolerance is given. The coupling between DoS attacking, synchronous, and asynchronous switching is clarified, which provides convenient conditions for stability analysis. Subsequently, the gains of subcontrollers are computed by a dynamic iterative algorithm. By utilizing the average dwell time and Lyapunov–Krasovskii functional technique, sufficient criteria are derived to ensure the stability of triggered NSSs when suffering from dual‐ended DoS attacks. At last, the effectiveness of proposed method is verified by a numerical simulation.

Visual Tracking Control of Deformable Objects With a FAT-Based Controller

Automatic manipulation of deformable objects is a challenging problem. Improper operations, such as excessive stretching and collision, are easy to cause damages to the deformable objects. Thus, not only the final configuration but also the trajectory of the deformation is supposed to be controlled during the process of interaction. In this article, a method to control the trajectory of the deformation in the unknown environment is proposed. We design an adaptive dynamic controller that adaptively estimates the deformation Jacobian matrix (DJM) online based on function approximation techniques (FAT), which approximates nonlinear functions with an arbitrary small error, avoiding modeling for compliant objects. Besides, we introduce a virtual force to improve the manipulability of the method. The stability of the proposed adaptive algorithm and the boundedness of internal signals are proved by Lyapunov theory whenever the approximation error is nonnegligible or negligible. Experiment results validate the efficiency of the algorithm proposed.

An adaptive dynamic inversion control method based on improved piecewise constant for flight control system

To overcome the lack of robustness of the nonlinear dynamic inversion (NDI) control, a simple and practical adaptive NDI control method based on an improved piecewise constant is proposed in this paper to enhance its robustness to disturbances and improve the accuracy of response tracking. Firstly, reasonable assumptions and analyses are made for the system with the influence of disturbance. Secondly, an improved piecewise constant adaptive NDI control method suitable for general flight control systems is proposed. The stability of the control system with disturbance and the error convergence range of the improved piecewise constant adaptive control are proved and analyzed theoretically. Finally, taking into account the fighter actual control requirements, the angular rates control strategy is given, and the proposed method is applied to the angular rates flight controller design. Matlab simulations are carried out under the disturbance of the actuator failure and the sudden change of the center of gravity, and the robustness and dynamic performance of the controller designed based on the present method is compared and verified. The results illustrate that our present method has stronger robustness and higher control accuracy.

Adaptive Fuzzy Event-Triggered Command-Filtered Control for Nonlinear Time-Delay Systems

This article focuses on an adaptive fuzzy dynamic event-triggered tracking control for nonlinear time-delay systems with unmodeled dynamics via a command filter method. Fuzzy logic systems are utilized to address the unknown nonlinear functions. The upper bound of the approximation error is allowed to be unknown and can be compensated by skillfully introducing a hyperbolic tangent function to the design of the adaptive laws. Meanwhile, without requiring any assumptions, time delays can be handled by appropriately incorporating the delayed nonlinear functions into the Lyapunov–Krasovskii functional. Then, a dynamic event-triggered control mechanism is designed to dynamically adjust the threshold parameter. Finally, a new adaptive controller is constructed such that all states of the closed-loop system are bounded. The system output is demonstrated to follow the desired signal. Two examples are given to illustrate the validity of the presented method.

Research on Adaptive Relationship Between Trust and Privacy in Cloud Service

To prevent privacy leakage, cloud services need to take corresponding methods, so participants often face the dilemma of service utility and privacy protection. In this paper, we propose a dynamic adaptive access control model based on trust permission and privacy protection to solve the problem of privacy disclosure and utility in the cloud service. Firstly, we add the concept of obligation and purpose into access control and establish the privacy information tree and privacy policy tree. Secondly, we establish a new trust evaluation and give the corresponding weight algorithm. Thirdly, we quantify the privacy information with the normal space and correlation coefficient method. Further, we propose a tradeoff relationship model between trust permission and privacy protection, each participant can select the corresponding parameters according to the actual requirement and personal preference. Experimental analysis and comparison results verify the feasibility, effectiveness, and superiority of our method. Finally, we summarize the work of this paper and point out the future development direction.

Molecular Dynamics Characteristics and Model of Vehicle-Following Behavior

The vehicle-following behavior is a self-organizing behavior that restores dynamic balance under the stimulation of external environmental factors. In fact, there are asymmetric problems in the process of acceleration and deceleration of drivers. The existing traditional models ignored the differences between acceleration and deceleration of vehicles. In order to solve this problem, the vehicles driving on the road are compared to interacting molecules. Vehicle-following characteristics are studied, and the molecular following model is established based on molecular dynamics. The model parameters under different conditions are calibrated considering the required safety distance by the vehicle and the reaction time of the driver. With the help of the vehicle running track graphs, speed, and acceleration graphs, the numerical simulations of the molecular following model and the classical optimal speed vehicle-following model are carried out. The results of the comparative analysis show that the acceleration in the process of acceleration and deceleration is not constant but more sensitive to the deceleration of the preceding vehicle than to the acceleration and more sensitive to the acceleration/deceleration of the short-distance vehicle than to the acceleration/deceleration of the long-distance vehicle. Therefore, the molecular following model can better describe the vehicle-following behavior, and the research results can provide a theoretical basis and a technical reference for the analysis of traffic flow dynamic characteristics and adaptive cruise control technology.

Dynamical analysis and adaptive fuzzy control for the fractional-order financial risk chaotic system

In this paper, a fractional-order model of a financial risk dynamical system is proposed and the complex behavior of such a system is presented. The basic dynamical behavior of this financial risk dynamic system, such as chaotic attractor, Lyapunov exponents, and bifurcation analysis, is investigated. We find that numerical results display periodic behavior and chaotic behavior of the system. The results of theoretical models and numerical simulation are helpful for better understanding of other similar nonlinear financial risk dynamic systems. Furthermore, the adaptive fuzzy control for the fractional-order financial risk chaotic system is investigated on the fractional Lyapunov stability criterion. Finally, numerical simulation is given to confirm the effectiveness of the proposed method.

Fixed-Time Event-Triggered Output Consensus Tracking of High-Order Multiagent Systems Under Directed Interaction Graphs.

This article investigates the problem of fixed-time event-triggered output consensus tracking for high-order multiagent systems (MASs) under directed interaction graphs. First, a fixed-time event-triggered distributed observer and triggering functions are proposed. Next, fixed-time convergence of the presented distributed observer is proved by the Lyapunov function approach, and an analysis is conducted to show the proposed distributed observer excludes zeno behavior. Then, an event-triggered adaptive dynamic surface fixed-time controller is designed to stabilize the tracking error system. Finally, simulation results are given to show the effectiveness and superiority of the consensus scheme developed. The contribution of this article is to present a novel event-triggered fixed-time distributed observer and a novel fixed-time controller, which can reduce frequency of communication and control update, avoid continuous monitor, exclude zeno behavior, eliminate the effect of mismatched disturbance caused by observation error, and achieve practical fixed-time output consensus tracking of high-order MAS under directed interaction graphs.