Adaptive Dynamic Control Research Articles

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

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

Decentralized Adaptive Quantized Dynamic Surface Control for a Class of Flexible Hypersonic Flight Vehicles with Input Quantization

A control strategy for a certain class of hypersonic flight aircraft dynamic models with unknown parameters is proposed in this article. The strategy is adaptive dynamic surface input quantization control. To address the issues in conventional inversion control, a first-order low-pass filter and an adaptive parameter minimum learning law are introduced in the control system design process. This method has the following features: (1) it solves the problem of repeated differentiation of the virtual control law in the conventional back-stepping method, greatly simplifying the control law structure; (2) by using the norm of the neural network weight vector as the adaptive adjustment parameter instead of updating each element online, the number of adaptive adjustment parameters is significantly reduced, improving the execution efficiency of the controller; (3) the introduced hysteresis quantizer overcomes the disadvantage of the quantization accuracy deterioration when the input value is too low in the logarithm quantizer, improving the accuracy of the quantizer. Stability analysis has shown that all signals in the closed-loop system are semi-globally uniformly bounded, and simulation results have verified the effectiveness of the proposed adaptive quantized control scheme.

Network-Based Adaptive Multievent-Triggered Fuzzy Dynamic Positioning Controller Design for Unmanned Surface Vehicles Against Denial-of-Service Attacks

Network-Based Adaptive Multievent-Triggered Fuzzy Dynamic Positioning Controller Design for Unmanned Surface Vehicles Against Denial-of-Service Attacks

Adaptive dynamic programming discrete-time LQR optimal control on electromagnetic levitation system with a $$H_\infty $$ Kalman filter

Adaptive dynamic programming discrete-time LQR optimal control on electromagnetic levitation system with a $$H_\infty $$ Kalman filter

Optimal Statistical Estimation and Dynamic Adaptive Control of Airline Seat Protection Levels for Several Nested Fare Classes under Parametric Uncertainty of Customer Demand Models

Assigning seats in the same compartment to different fare classes of passengers is a major problem of airline seat allocation. Airlines sell the same seat at different prices according to the time at which the reservation is made and other conditions. Thus the same seat can be sold at different prices. The problem is to find an optimal policy that maximizes total expected revenue. To solve the above problem, this paper presents the novel computational approach to optimization and dynamic adaptive prediction of airline seat protection levels for multiple nested fare classes of single-leg flights under parametric uncertainty. It is assumed that time T (before the flight is scheduled to depart) is divided into h periods, namely a full fare period and h-1 discounted fare periods. The fare structure is given. An airplane has a seat capacity of N. For the sake of simplicity, but without loss of generality, we consider (for illustration) the case of nonstop flight with two fare classes (business and economy). The proposed airline's inventory management policy is based on the use of the proposed computational models. These models emphasize pivotal quantities and ancillary statistics relevant for obtaining statistical predictive limits for anticipated quantities under parametric uncertainty and are applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. The proposed technique is based on a probability transformation and pivotal quantity averaging. It is conceptually simple and easy to use. Finally, we give illustrative examples, where the proposed analytical methodology is illustrated in terms of the two-parameter exponential distribution. Applications to other log-location-scale distributions could follow directly.

Finite-time quantized dynamic event-triggered control for cluster synchronization of Markovian jump complex dynamic networks with time-varying delays and actuator faults

In this article, by employing the quantized dynamic event-triggered (ET) and adaptive fault-tolerant control mechanisms, the finite-time cluster synchronization (FTCS) criterion of Markov jump complex dynamic networks (MJCDNs) with actuator faults is established. A kind of novel adaptive controller is designed and appropriate Lyapunov functions are applied to eliminate the effectiveness of actuator faults. Further on historical ET schemes, a more universal dynamic ET control mechanism for MJCDNs is considered in this article, which can reduce the network load of MJCDNs more effectively. Meanwhile, the Zeno behavior is ruled out availably. Finally, the numerical simulation example is presented to verify the validity of the theory.

Event-triggered synchronization of a two-layer heterogeneous neural network via hybrid control

Multi-layer network contains multiple subsystems and different types of interaction, which is more practical than traditional single-layer network. On this observation, this paper explores point-to-point quasi-synchronization in a two-layer heterogeneous neural network system. The sufficient conditions for global exponential synchronization in the driving layer are given a coupling term. Based on the global exponential synchronization in the driver layer, this paper further studies the global point-to-point quasi-synchronization between the driver layer and the response layer of the two-layer neural network under the hybrid control and event-triggered control. Under this hybrid control strategies, the event can be activated if and only if the trigger condition is violated and the dynamical adaptive control is also involved. Moreover, the Zeno behavior under this strategy is eliminated by Lyapunov method and matrix theory. Finally, a numerical example is given to verify the theoretical results.

Adaptive Dynamic Cutting Force Control Analysis for Peripheral Milling

The implementation of condition monitoring tools can improve plant availability and lower downtime costs in general. A reliable adaptive control system can prevent machine downtime or undesired situations such as chatter vibration and excessive tool wear, permitting the best utilization of a tool's life. This study used dynamic force analysis to create an adaptive dynamic control system for Computer Numerical Control (CNC) milling to adjust a controlled system for signals from offline measurements that will be processed and supplied back to the machine tool controller to correct cutting parameters. This paper describes a better adaptive control system for peripheral milling with helical end mills based on a dynamic cutting force model. This theoretical model is based on the oblique cutting principle and takes into account the effects of the size of undeformed chip thickness and the effective rake angle. Simulation results showed that the enhanced dynamic cutting-force model accurately predicted cutting forces in peripheral milling.

ADP-Based Intelligent Tracking Algorithm for Reentry Vehicles Subjected to Model and State Uncertainties

This article presents an adaptive dynamic programming-based intelligent control algorithm for the attitude tracking issue of reentry vehicles subject to model and state uncertainties simultaneously. The traditional control approaches struggle to achieve satisfactory tracking performance since the model and state are together influenced and deviated by the both uncertainties. Instead, the attitude tracking issue in this article is first transformed into an optimal regulation issue of the tracking error. Then, a novel cost function inspired by the idea of zero-sum game is introduced to eliminate the model uncertainties, and state uncertainties are handled dynamically by updating weights based on the optimality principle of the critic network. Consequently, the intelligent tracking control law is obtained by the optimal regulation. The stability of the system and the convergence of network weights are further analyzed using the Lyapunov stability theory. The effectiveness of the proposed control scheme is verified by simulations.

Observer-Based Dynamic Event-Triggered Adaptive Control of Distributed Networked Systems With Application to Ground Vehicles

In this paper, a distributed observer-based event-based practical consensus control for networked systems subject to adaptive nonlinear couplings is investigated. Considering that the node's state is not always measurable and the finite communication bandwidth exists in engineering systems, two distributed adaptive event-triggered control protocols subject to nonlinear couplings are proposed under observer-based dynamic output feedback. Then, distributed dynamic event-triggered control policies corresponding to two above mentioned adaptive controllers are proposed, respectively. The proposed nonlinear coupled event-triggered control schemes not only overcome continuous communication among nodes, but also relax the requirement of relying on global information for achieving a distributed practical consensus. Finally, the theoretical analysis is verified through a team of practical networked ground vehicles implemented thorough Matlab/Similink and Robot Operating Systems (ROS) environment.

Dynamic event-triggered adaptive neural control for MIMO nonlinear systems via a single parameter learning method

The dynamic event-triggered control issue for multi-input and multi-output (MIMO) nonlinear systems with unknown dynamics and disturbances is solved in this paper. It is the first attempt to design a dynamic event-triggered mechanism for MIMO systems, which can further reduce data transmission from controller to actuator, thereby producing fewer event. The unknown dynamics of MIMO systems are approximated by neural networks. Then, the composited uncertain vector consisting of approximated terms and disturbances is transformed into a linear parameterized form with a single parameter. In addition, the dynamic event-triggered adaptive control method is designed under the backstepping framework. Finally, the validity of the presented dynamic event-triggered control solution is verified by simulation example and comparison analysis.

Adaptive Tracking Control of Hybrid Switching Markovian Systems with Its Applications

This paper focuses on the model reference adaptive tracking control problem of uncertain hybrid switching Markovian systems. The stochastic multiple piecewise Lyapunov function method is set up for designing a hybrid switching signal and a piecewise dynamic switching adaptive controller. The hybrid switching signal is presented to improve the adaptive tracking capability by providing plenty of adjusting time during the stochastic switching stage. A piecewise dynamic parameter projection adaptive control technique is developed, which provides more freedom in designing a model reference adaptive law. A set of piecewise dynamic switching adaptive controllers are designed such that all the signals of the tracking error system remain within a bounded region under the proposed hybrid switching signal, and the tracking error converges to a neighborhood of zero where the radius of the neighborhood can be made arbitrarily small by choosing the regulation parameters appropriately. Finally, the developed adaptive tracking control theory of uncertain hybrid switching Markovian systems is illustrated by using a numerical example and an application example of an electro-hydraulic model.

Adaptive Dynamic Programming-based Adaptive Optimal Tracking Control of a Class of Strict-feedback Nonlinear System

Adaptive Dynamic Programming-based Adaptive Optimal Tracking Control of a Class of Strict-feedback Nonlinear System

Adaptive Dynamic Programming-Based Cross-Scale Control of a Hydraulic-Driven Flexible Robotic Manipulator

This paper focuses primarily on adaptive dynamic programming (ADP)-based tracking control of the hydraulic-driven flexible robotic manipulator system (HDFRMS) with varying payloads and uncertainties via singular perturbation theory (SPT). Firstly, the dynamics is derived using a driven Jacobin matrix, which represents the coupling between the hydraulic servo-driven system and rigid–flexible manipulator established using the assumed mode method and Lagrange principle. Furthermore, the whole dynamic model of the manipulator system is decoupled into a second slow subsystem (SSS), a second fast subsystem (SFS) and a first fast subsystem (FFS). The three subsystems can describe a large range of movement, flexible vibration and electro-hydraulic servo control, respectively. Hereafter, an adaptive dynamic programming trajectory tracking control law with a critic-only policy iteration algorithm is presented in the second slow timescale, while both robust optimal control (ROC) in the second first timescale and adaptive sliding mode control (ASMC) in the first fast timescale are also designed using the Lyapunov stability theory. Finally, the numerical simulations are carried out to illustrate the rightness and robustness of the singular perturbation decomposition and proposed composite control algorithm.

Dynamic event-triggered adaptive control for zero-sum games of nonlinear stochastic systems

This paper considers solving the zero-sum games (ZSGs) of nonlinear stochastic systems under the architecture of dynamic event-triggered mechanism (DETM). The adaptive dynamic programming (ADP) approach with great approximate computing power is proposed to seek the saddle-point equilibrium solution of ZSGs. First, the Hamilton–Jacobi–Isaacs equation of stochastic version is constructed which is the key to solving the two-player ZSGs. Then, the ADP method is employed to search the approximated solution under the dynamic triggering mechanism. On the foundation of static event-triggered mechanism (SETM), the DETM is derived through introducing an internal dynamic invariable to further lower the update frequency for the strategies. Additionally, the stability of the closed system is demonstrated in detail via the Lyapunov theory. Finally, two representative examples are provided to present the availability and reliability of this approach from different perspectives.

Improved dynamic event-triggered anti-unwinding control for autonomous underwater vehicles

This article investigates an improved adaptive dynamic event-triggered control scheme for autonomous underwater vehicles (AUVs) under the quaternion-based attitude representation. The main innovation in this paper is to improve the dynamic event-triggered (DET) algorithm, which makes the trade-off between the control performance of the AUV system and the communication frequency in the controller-to-actuator (CA) channel easier to be achieved. Primarily, to describe the attitude representation of the AUV in a unique form, a unified auxiliary variable confining the scalar quaternion's initial error value is developed to avoid the potential unwinding phenomenon. Subsequently, based on the unified auxiliary variable, an event-based adaptive control law is tactfully designed to mitigate the adverse effects of lumped external perturbations and unknown model dynamics. Moreover, by introducing the modified trigger adjustment algorithm, the trigger frequency can be adapted to the performance requirements of the AUV system at different evolutionary stages. Finally, theoretical analysis and numerical simulations are presented to demonstrate the stability and effectiveness of the provided method.

Adaptive fuzzy dynamic surface fault-tolerant control for coupled spacecraft with actuator faults and saturation

This study investigates the fault-tolerant control problem associated with pose tracking for coupled spacecraft in the presence of both actuator faults and actuator saturation constraint. By using the Lie group SE(3), a six-degrees of freedom (6-DOF) coupled kinematics and dynamics model is established to derive the relative motion tracking errors in the exponential coordinates. Then, all system states are reconstructed to develop a dynamic surface fault-tolerant controller. Considering the lumped disturbance consisting of internal uncertainties, external unknown disturbances and actuator faults, a fuzzy adaptive method is used to estimate and compensate for the lumped disturbance. Next, the controller is nested with an auxiliary first-order filter to compensate for the saturation constraints. A Lyapunov analysis is used to prove the stability and finite time convergence of the proposed controller. Finally, The tracking performance is verified through a numerical example.

Adaptive neural dynamics-based speed control strategy for stable retrieval of tethered satellite system

This work proposed a novel adaptive neural dynamics (AND)-based speed control strategy for the stable sub satellite’s retrieval of the tethered satellite system (TSS). The new control scheme is implemented by adjusting the retrieval speed only. An analytical speed function is used to ensure stable retrieval without libration motion overall. A high-efficiency adaptive neural dynamic control law with the retrieval speed as control input is used to eliminate the libration motion. In the control loop, the tension in the tether is monitored and restrained directly. The Lyapunov stability of the control law is proved analytically. The simulation results show the proposed adaptive neural dynamics-based speed control strategy is very effective in keeping a stable retrieval by adjusting the retrieval speed only. The libration motions can be eliminated promptly in a fast manner with limited control input and tension constrain.

Finite-time H∞ control for switched fuzzy systems: A dynamic adaptive event-triggered control approach

This paper investigates the finite-time H∞ control problem for switched fuzzy systems using the edge-dependent average dwell time switching. A dynamic event-triggered mechanism is adopted to alleviate communication pressure. An adaptive law is set up to adjust the threshold on-line, which deeply affects the triggering times. It is rigorously proved that the Zeno behavior is excluded. A set of dynamic event-triggered controllers and edge-dependent average dwell time switching signals are co-designed to achieve desired performance. Finally, a chemical reaction example is given to validate the effectiveness of the proposed method.

Learning-Based Neural Dynamic Surface Predictive Control for MMC

Reinforcement learning technique was developed recently as an interesting topic in designing adaptive optimal controllers. This technique explicitly provided a feasible solution to circumvent the “curse of dimensionality” and requiring a system model inherent in the classical dynamic programming algorithm. By virtue of this property, in our work, by introducing this technique into a predictor-based online adaptive neural dynamic surface predictive control architecture, we concentrate on a novel robust predictive control framework subject to system uncertainties. To be specific, in this presented framework, an adaptive dynamic programming control strategy utilizing a critic neural network point of view is developed to learn the optimal control policy. Our modification is able to facilitate the alleviation of performance deterioration caused by system uncertainties and enable the smooth and fast learning, while keeping the merits of the finite control-set model predictive control. Finally, the interest and applicability of the proposed control methodology are verified by performance evaluation.