Adaptive Neural Output Feedback Control Research Articles

Adaptive disturbance rejection neural output feedback control of hydraulic manipulator systems

This paper proposes an adaptive disturbance rejection neural output feedback control (ADRNC) scheme for multi-degree-of-freedom (n-DOF) hydraulic manipulator systems, subjected to unknown nonlinearities, external disturbances and unmeasured system states. The controller design is formulated by integrating Radial Basis Function Neural Networks (RBFNNs) with state and disturbance observers using the backstepping method. The RBFNNs are synthesized to handle unknown nonlinear functions and the residual estimate error, coupled with external disturbances, is estimated through the combination of state observer and disturbance observer. The unique features of the proposed controller lies in its capability to estimate both matched and unmatched lumped disturbances. The auxiliary disturbance estimation law is guided by the neural learning weights and estimated system states provided by state observers. By effectively utilizing neural networks to approximate and mitigate most nonlinear uncertainties, the workload of the disturbance observer is substantially reduced. High-gain feedback is therefore avoided and improved tracking performance can be expected. Moreover, to avoid the tedious analysis and the problem of “explosion of complexity” in the conventional backstepping method, we employ a first-order sliding-mode differentiator. Rigorous analysis via Lyapunov methods establishes the stability of the entire closed-loop system, ensuring guaranteed and satisfactory tracking performance under the integrated influence of unknown nonlinearities, unmeasured states, and external disturbances. Extensive simulations are conducted to verify the effectiveness of the nested control strategy.

Event-Triggered Adaptive Fuzzy Neural Network Output Feedback Control for Constrained Stochastic Nonlinear Systems.

This article investigates the problem of command-filtered event-triggered adaptive fuzzy neural network (FNN) output feedback control for stochastic nonlinear systems (SNSs) with time-varying asymmetric constraints and input saturation. By constructing quartic asymmetric time-varying barrier Lyapunov functions (TVBLFs), all the state variables are not to transgress the prescribed dynamic constraints. The command-filtered backstepping method and the error compensation mechanism are combined to eliminate the issue of "computational explosion" and compensate the filtering errors. An FNN observer is developed to estimate the unmeasured states. The event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to a small neighborhood of the origin, and all signals in the closed-loop systems are bounded. Finally, a physical example is used to verify the feasibility of the theoretical results.

Event-triggering adaptive neural network output feedback control for networked systems under false data injection attacks

This paper investigates the event-triggering adaptive neural network (NN) output feedback control problem for networked control systems subject to false data injection (FDI) attacks. To reduce unnecessary data transmission, an event-triggered mechanism (ETM) related to the measured output is designed on the channel of the sensor-to-observer. Meanwhile, a novel ETM associated with the observer state and parameter estimation signal is devised on the channel of the observer-to-controller to save communication resources on the controller-to-actuator channel. Subsequently, NN technique is employed to approximate FDI attacks, then an adaptive neural output feedback controller is designed to counteract the impact of FDI attacks on the system. Sufficient conditions for the semi-globally uniformly ultimately bounded of the system are obtained by means of the Lyapunov function. Additionally, a co-design scheme for the control gain, observer gain and event-triggered parameters is presented. Finally, a practical example is given to demonstrate the effectiveness of the proposed method.

Adaptive neural output-feedback control for nonstrict-feedback stochastic nonlinear time-delay systems with hysteresis

This paper proposes an adaptive neural outputfeedback controller for a class of nonstrict-feedback stochastic nonlinear time-delay systems with unknown backlash-like hysteresis. First, a state observer is established for estimating the unmeasured states. Second, an appropriate Lyapunov-Krasovskii functional is used to compensate the unknown time-delay terms, and the neural network is employed to approximate the unknown nonlinear functions. At last, an output-feedback adaptive neural control scheme is constructed by using Lyapunov stability theory and backstepping technique. It is shown that the designed neural controller can ensure that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two examples are presented to further show the effectiveness of the proposed approach.

Inverse optimally adaptive neural output‐feedback control of stochastic nonlinear systems

SummaryIn this article, for a class of stochastic nonlinear systems with non‐strict feedback, a neural adaptive inverse optimal output feedback control design scheme is presented. First, according to the existing inverse optimal criterion, an auxiliary system is established. On this basis, a novel observer is built to evaluate the unpredictable states. Second, in the control process, neural networks (NNs) are applied to estimate the unknown functions. Based on NNs and the backstepping technology, an adaptive neural inverse optimal output feedback controller is established. It is indicated that the proposed scheme could ensure the semi‐globally uniformly ultimately bounded of the closed‐loop system and also achieve the objective of inverse optimality. Eventually, an example is applied to testify the feasibility of this scheme.

Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data

This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transmission, are taken into account. To tackle the issue of network resource constraints, an event-sampled scheme is developed to obtain the course data, and a novel event-sampled adaptive neural-network-based state observer (NN–SO) is developed to achieve the state reconstruction of discontinuous yaw. Using a backstepping design method, an event-sampled mechanism, and an adaptive NN–SO, an adaptive neural output feedback (ANOF) control law is designed, where the dynamic surface control technique is introduced to solve the design issue caused by the intermission course data. Moreover, an event-triggered mechanism (ETM) is established in a controller–actuator (C–A) channel and a dual-channel event-triggered adaptive neural output feedback control (ETANOFC) solution is proposed. The theoretical results show that all signals in the closed-loop control system (CLCS) are bounded. The effectiveness is verified through numerical simulations.

Observer-based prescribed performance adaptive neural output feedback control for full-state-constrained nonlinear systems with input saturation

In this study, a prescribed performance adaptive neural output feedback controller is developed for full-state-constrained nonlinear systems that are subject to uncertainties and input saturation. To approximate system unknown nonlinear functions, the radial basis function neural network is employed. And, based on the approximated outputs of neural network, the state observer and nonlinear disturbance observer are structured to evaluate unmeasured states and unknown lumped disturbances, respectively. Barrier Lyapunov function involving prescribed performance function is constructed to constrain tracking error and all states within the specified ranges. Then, based on the constructed neural network and observers, an output feedback controller is proposed to constrain the tracking error and states via backstepping technique. Moreover, an auxiliary dynamic system is established to deal with input saturation possible caused by the more efforts to constrain tracking error and states under rough working conditions. Moreover, stability of the entire closed-loop system is strictly certified using Lyapunov theory, and the desirable prescribed tracking performance without violation of all states is assured under the combined influence of uncertainties and input saturation. Finally, the proposed control method is verified by two examples.

Finite-time adaptive neural network event-triggered output feedback control for PMSMs

This article investigates the finite-time adaptive neural network event-triggered output feedback control for the permanent magnet synchronous motor (PMSM) systems. The addressed PMSM systems include unknown nonlinear dynamics and immeasurable states. The neural networks are utilized to approximate the unknown nonlinear dynamics and an equivalent control design model is established, by which a neural network state observer is given to estimate the immeasurable states. By constructing an event-triggered mechanism and under the framework of adaptive backstepping control design technique and finite-time stability theory, a finite-time adaptive event-triggered output feedback control scheme is developed. It is proved that the proposed control scheme ensures the closed-loop system to be stable and the angular velocity, stator current and other state variables remain bounded in a finite time. Finally, the computer simulation is provided to confirm the effectiveness of the presented controllers.

Dynamic Event-Triggered Adaptive Neural Output Feedback Control for MSVs Using Composite Learning

This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution.

Adaptive Neural Network Output Feedback Control of Uncertain Underactuated Systems With Actuated and Unactuated State Constraints

Underactuated systems are widely applied in industry, construction, manufacturing, etc., and the complex working environment puts forward higher demands for safety and transient performance. Hence, it is necessary to consider how to simultaneously ensure actuated and unactuated motion constraints by fewer control inputs, especially when systems suffer from model uncertainties, unavailable velocities, etc. Unfortunately, it is still a significant challenge to overcome in real applications and theoretical analysis. Additionally, most existing studies merely consider the specific control objects and few general methods are applicable to a class of underactuated systems. To this end, we design a new adaptive output-feedback controller for a class of uncertain underactuated systems. Compared with existing methods only handling actuated constraints, an important merit of this article is that by introducing the elaborately designed coupling term composed of actuated and unactuated constraints together, all state variables are kept within the preset time-variant ranges and converge to their desired values. Furthermore, a new Lyapunov function candidate is utilized to provide a theoretical guarantee. As far as we know, without the need of exact model knowledge and velocity feedback, this article provides the first solution to achieve accurate motion control and state constraints for both actuated and unactuated variables, which is meaningful both theoretically and practically. Meanwhile, the asymptotic stability of the equilibrium point for the closed-loop system is proven by utilizing Lyapunov techniques and Barbalat’s lemma. For verification, the presented controller is applied to underactuated overhead and rotary cranes, respectively, together with detailed theoretical analysis and experimental validations.

Adaptive neural output feedback control of automobile PEM fuel cell air-supply system with prescribed performance

Adaptive neural output feedback control of automobile PEM fuel cell air-supply system with prescribed performance

Decentralized adaptive output feedback dynamic surface control for stochastic nonstrict‐feedback interconnected nonlinear systems with actuator failures and input quantization via command filter

AbstractIn this article, a new decentralized adaptive neural output feedback control scheme based on first‐order command filter is proposed for stochastic nonstrict‐feedback interconnected systems with prescribed performance, input quantization, actuator failures and unmodeled dynamics. The unknown smooth functions are eliminated by using the radial basis function neural networks. The immeasurable states in the system are estimated using the decentralized K‐filters, unmodeled dynamics is processed using dynamic signal, and the hyperbolic tangent function is applied to the construction of the prescribed performance function. The hysteretic quantizer and actuator failure are denoted in the form of linearization, and a smoothing function is introduced to compensate for the effects of quantization and bounded stuck faults. Based on the dynamic surface control (DSC) method and using the properties of Gaussian function to deal with stochastic nonstrict‐feedback interconnected systems, the first‐order command filter is used to replace the first‐order filter in the traditional DSC to eliminate the influence of filtering error on the systems, and an error compensation signal is introduced at the recursive each step of the design to construct a new error dynamic surface, which simplifies the derivation process and the design of the controller. Finally, the Lyapunov method is used to prove that all the signals in the whole controlled system are semiglobally uniformly ultimately bounded in probability and the tracking error is within the time‐varying constraint. The effectiveness of the proposed decentralized adaptive control method is verified by a numerical simulation and an example simulation.

Adaptive event‐triggered output‐feedback control for switched nonlinear systems with time‐varying delays: A modified MDADT method

In this paper, the issue of adaptive event-triggered output-feedback control for a class of switched nonlinear systems with time-varying delays is investigated. By adopting the mode-dependent average dwell time (MDADT) method and the backstepping technique, an adaptive neural output-feedback control strategy is proposed for the switched system with event-triggered mechanism. In the process of controller design, an intelligent approximation technique and a switched filter are introduced to overcome the difficulties caused by the unknown nonlinear functions and the immeasurable states, respectively. Moreover, the appropriate Lyapunov–Krasovskii functionals are designed to compensate the effects of time delays. It is proved that the proposed control strategy guarantees that all the signals in the switched closed-loop systems are bounded under arbitrary switchings with MDADT. In the end, two simulation examples are provided to illustrate the feasibility of the developed control strategy.

Observer-based adaptive neural control of robotic systems with prescribed performance

In this paper, an adaptive neural output feedback control (ANOFC) scheme is proposed for controlling an electrically driven robotic manipulator (EDRM) system with prescribed errors constraint by using a neural network-based adaptive observer (NNAO) and a backstepping design technique. First, the NNAO is designed to observe the unknown and unmeasured joint velocities of EDRM. Second, the prescribed performance bounds of output tracking are used to achieve the prescribed transient and steady-state performance based on barrier Lyapunov function. Then, the ANOFC scheme is derived by using the backstepping design methodology, where neural networks with adaptive update laws are utilized to approximate the unknown nonlinear functions. By using the Lyapunov stability analysis method, the observer and closed-loop control system can be proven to be stable such that all the uniformly bounded variables in the system are guaranteed and the output tracking errors remain within the specified prescribed bounds. Finally, the simulation results demonstrate that the proposed NNAO and ANOFC schemes can achieve the satisfied estimation capability and tracking effectiveness.

Adaptive neural output-feedback control for a class of output-constrained switched stochastic nonlinear systems

In this paper, a neural network-based adaptive output-feedback control problem is investigated for a class of output-constrained switched stochastic nonlinear systems. By introducing nonlinear mapping, the asymmetric and symmetric output constrained stochastic nonlinear system is transformed into a new system without any constraint. It is the first time that a switching system is used to convert symmetric and asymmetric output constraints in the same system. An adaptive neural output-feedback controller is developed based on the backstepping technique. A state observer is designed to estimate the unmeasurable system state signals. An adaptive controller is designed to ensure that the output tracking error converges to a small region of the origin. The control scheme ensures that all signals in the closed-loop systems are semi-global uniformly ultimately bounded. Results of simulation cases are presented to prove the effectiveness of the theoretical analysis.

Observer‐based adaptive neural output‐feedback event‐triggered control for discrete‐time nonlinear systems using variable substitution

AbstractIn this paper, an event‐triggered adaptive neural output feedback control scheme is developed for a class of uncertain discrete‐time strict‐feedback nonlinear systems subject to immeasurable system states and network resource limitation. Ani‐step ahead predictor is synthesized to obtain the future signal of the reference orbit. By combining the neural observer and the variable substitution technology, an event‐triggered adaptive neural control scheme is developed, thereby estimating the immeasurable system states and avoiding then‐step delays of the existing controller. To promote the transient system performance, an improved triggering condition is designed to increase the number of triggering events in the transient process. The stability analysis of the closed‐loop system is divided into two parts to deal with the challenge from the simultaneous presence of state estimations, unknown system dynamics, and aperiodical controller weight updating laws. The proposed scheme achieves the state estimation, guarantees the output tracking performance with the improved transient control performance, and reduces the communication resource. Simulation studies on a numerical example and a networked robot manipulator are, respectively, implemented to show the validity of the proposed scheme.

Control Design for Uncertain Switched Nonlinear Systems: Adaptive Neural Approach

This paper addresses adaptive neural output feedback control for uncertain nonlinear switched systems. The main difficulty for control design comes from the loss of the precise information on those virtual coefficients of each subsystem. To overcome this difficulty, we give a robust observer design scheme by using convex combination approach. Furthermore, develop an observer-based output feedback control strategy. During the procedure of control design, adaptive neural control approach is used to deal with the unknown nonlinear functions and backstepping technique is employed to construct the ideal control laws. It is shown that the presented control law achieves the control issue of getting small tracking error, meanwhile, ensuring boundedness of all the closed-loop signals. Finally, a simulation example is used to test our results.

Adaptive Neural Output Feedback Control for MSVs With Predefined Performance

In this paper, we investigate the problem of trajectory tracking control for marine surface vehicles (MSVs), which are subject to dynamic uncertainties, external disturbances and unmeasurable velocities. To recover the unmeasurable velocities, a novel adaptive neural network-based (NN-based) state observer is constructed. To guarantee the transient and steady-state tracking performance of the system, a novel nonlinear transformation method is proposed by employing a tracking error transformation together with a newly constructed performance function, which is characterized by a user-defined settling time and tracking control accuracy. With the aid of the state observer and the nonlinear transformation method in combination with the adaptive NN technique and vector-backstepping design tool, an adaptive neural output-feedback trajectory tracking control scheme with predefined performance is developed. With regard to the developed control scheme, uncertainties can be reconstructed only by utilizing the position and heading of the MSVs. Independent designs of the state observer and the controller can be achieved, and the position tracking error can be guaranteed to fall into a predefined residual set in the user-defined time frame and remain in the above set. A rigorous stability analysis validates that all signals in the closed-loop trajectory tracking control system for MSVs are uniformly ultimately bounded. Simulation results verify the effectiveness of the developed adaptive neural output-feedback trajectory tracking control scheme.

Observer-Based Adaptive Neural Network Control for Nonstrict-Feedback Nonlinear Systems With Time Delays

An observer-based adaptive neural network control problem was investigated for a class of nonstrict-feedback nonlinear systems with time delays. A state observer was constructed to estimate unknown variables in nonlinear systems. With the approximation ability of RBF NNs and the backstepping technique, an adaptive neural network output feedback control approach was proposed. The designed controller ensures the semi-global uniform boundedness of all signals in the closed-loop system. Finally, the simulation example shows the effectiveness of the proposed control approach.

Adaptive Neural Output Feedback Tracking Control of Underactuated Ships Against Uncertainties in Kinematics and System Matrices

Adaptive neural output feedback tracking control is developed for underactuated ships with uncertainties in kinematics (i.e., kinematic uncertainties) and uncertain off-diagonal terms in the matrices of their system dynamics (i.e., dynamic uncertainties), where radial basis function networks are incorporated. As the kinematic uncertainties unavoidably exist in the off-diagonal terms of system matrices and only the posture information (position variables and yaw angle) of the ship is available, the velocities should be observed and the kinematic and dynamic uncertainties should be simultaneously compensated to achieve the satisfactory ship tracking control performance. Therefore, the kinematic and dynamic uncertainties of the underactuated ship are rigorously derived. The kinematic equations with uncertainties are then derived such that the proposed nonlinear velocity observer can be used to estimate the combined velocities and kinematic uncertainties using the posture information irrespective of the ship dynamics. The dynamic uncertainties are also derived while taking into consideration the uncertainties in the system matrices. An adaptive neural uncertainty observer is then proposed to estimate the dynamic uncertainties. In this way, the proposed adaptive neural output feedback control law can compensate for the uncertainties in both the kinematics and dynamics. The estimation errors in the combined velocities and kinematic uncertainties and the dynamic uncertainties are rigorously considered in the proposed controller design. A stability analysis and the simulation results of the ship tracking control system are provided to demonstrate the validity of the proposed method.