Composite Learning Control Research Articles

Fixed-time neural network composite learning control for uncertain nonlinear systems

A novel fixed-time neural network composite learning control (FNNCLC) scheme is proposed for nonlinear strict-feedback systems with unknown dynamics. The neural network (NN) is employed to handle system uncertainty. By utilizing tracking errors and prediction errors to update NN weights, accurate network learning is achieved under a weaker excitation condition termed interval excitation (IE) condition, instead of the typically required strict persistent excitation (PE) condition. Moreover, for the first time, a high order term and first order term of the prediction error are introduced to design composite learning adaptive laws, achieving the convergence of NN weights within fixed time. Additionally, a smooth fixed-time (FXT) dynamic surface control scheme is constructed without potential singularity problems, which mitigates complexity explosion by avoiding fractional power terms and complex switching strategies when formulating the control law. The stability of the proposed control scheme is analyzed by using Lyapunov technique. Simulation results demonstrate the effectiveness of the proposed controller.

Neural networks-based composite learning control for robotic systems with predefined time error constraints

This paper investigates the neural networks-based control problem for robotic systems with error constraints. By embedding a new predefined time performance function into an asymmetric barrier function, a predefined time constraints method is proposed with the following features: (1) it is a general approach that can handle asymmetric error constraints and can be directly applied to the predefined performance control for other high-order nonlinear systems without any changing; (2) both settling time and convergent compact set can be preassigned by the user; (3) the results are global i.e., the robot can start arbitrarily within the physical domain. By extending the regression operators with online data memory, a prediction error is constructed, by using which, a new neural networks based composite learning law is constructed to handle unknown dynamics. Compared with the traditional method such as gradient descent algorithm, σ-modification and ϵ-modification which cannot guarantee the convergence of the neural networks weights or can only force the weights to stay around preselected values, the neural networks guarantees that the weights converge to the region around the true values, in particular under a weak interval excitation (IE) condition rather than the typically stringent persistent excitation (PE) condition. The benefits and effectiveness of the proposed control are theoretically authenticated and experimentally validated.

Finite-time composite learning control for flexible-link manipulator based on disturbance observer

With the development of robotic technology and theory, there is an expectation for robots to operate in complex environments. This paper addresses the joint tracking control problem of flexible-link manipulator system under unknown external disturbances, modelling uncertainties, and actuator saturation constraints. A research is conducted, proposing a composite learning control scheme based on adaptive finite-time disturbance observer (AFTDO). The designed AFTDO can estimate the upper bound of unknown disturbances by adaptive laws, and a neural network controller is developed based on the AFTDO algorithm to compensate and suppress disturbances from multiple sources, ensuring the stability of all variables within a finite time. Simultaneously, a barrier Lyapunov function (BLF) is chosen to guarantee that the system satisfies constraints under flexible vibrations. Finally, through simulation comparisons with other control strategies, the feasibility and superiority of the proposed method are convincingly demonstrated.

Composite learning control for strict feedback systems with neural network based on selective memory

Composite learning control for strict feedback systems with neural network based on selective memory

Neural-based fixed-time composite learning control for multiagent systems with intermittent faults

In this paper, a distributed fixed-time composite learning control problem is addressed for nonlinear multiagent systems (MASs) subject to intermittent actuator faults. First, a distributed estimator is constructed for followers that are unable to communicate directly with the leader. Then, instead of using the traditional adaptive neural network (NN) algorithm, a predictor-based composite learning technique is proposed, which incorporates the prediction error into the NN update law to enhance the estimation accuracy of the unknown nonlinearity. Furthermore, an adaptive fault-tolerant control compensation mechanism is developed for intermittent faults that may occur indefinitely and frequently. To guarantee that all signals of the closed-loop system are bounded in fixed time, a nonsingular fixed-time fault-tolerant controller in the form of quadratic function is established. Finally, simulation results confirm the effectiveness of the presented algorithm.

Finite-time composite learning control for nonlinear teleoperation systems under networked time-varying delays

Finite-time composite learning control for nonlinear teleoperation systems under networked time-varying delays

Adaptive composite learning control of a flexible two‐link manipulator with unknown spatiotemporally varying disturbance

AbstractThis article presents a novel adaptive composite learning (ACL) control strategy combining reinforcement learning and a disturbance observer (DOB) to address vibration issues in a flexible two‐link manipulator (FTLM) system affected by unknown spatiotemporally varying disturbances. Based on the assumed mode method, the FTLM system is initially transformed into an ordinary differential equation model, while effectively capturing the elastic deformation and vibration characteristics of the flexible link. A composite learning controller, based on the actor‐critic algorithm and DOB, is then developed to achieve trajectory tracking and vibration suppression in the FTLM system. The DOB in the controller compensates for unknown disturbances resulting in reduced system error. It is noting that the proposed optimal control strategy is continuously gathering system experience and evaluating the current policy's effectiveness. The stability and robustness of the closed‐loop system incorporating the composite controller are analyzed using Lyapunov's direct method, and the semi‐global uniform ultimate boundedness of the tracking and vibration errors are also demonstrated. To validate the effectiveness and superiority of the proposed ACL controller, comparative simulations and experiments are conducted on the Quanser experimental platform.

Practical fixed‐time composite‐learning control for full‐state constraint strict‐feedback non‐linear systems: A dynamic regressor extension and mixing based approach

AbstractA practical fixed‐time composite learning control scheme, by combining dynamic regressor extension and mixing (DREM) parameter identification algorithm and adaptive dynamic surface control (DSC) technique, is proposed for a class of strict‐feedback non‐linear systems subjected to linear‐in‐parameters uncertainties and full‐state constraint. To address the problem of state constraint, a non‐linear transformation function is introduced to convert the originally constrained non‐linear system into an unconstrained one. Meanwhile, the hyperbolic tangent function is employed to avoid singularity issues that often appeared in the traditional fixed‐time (FXT) control designs. In order to relax the requirement of persistency of excitation condition, a modified FXT‐DREM parameter identification approach with an interval excitation condition is constructed by introducing a three‐layer transformation technique derived from the classical DREM algorithm. Then, the modified FXT‐DREM parameter identification algorithm is seamlessly integrated into the adaptive DSC framework, resulting in a composite‐learning control scheme. By employing Lyapunov stability analysis, the fixed‐time convergence of both the parameter estimation error and the trajectory tracking error is proved. Finally, the effectiveness of the proposed design is demonstrated through simulation test.

Performance enhancement of a bionic rigid–flexible coupling flapping wing based on composite learning control

The bionic rigid–flexible coupling flapping wing (BRFCFW) is inspired by the flight mechanics of birds, particularly seagulls. It enriches the advantages of light weight, flexible maneuver and low energy consumption. The flight, however, is affected by elastic vibration and system uncertainty, which can degrade the control performance. To address these issues, this paper proposes a composite learning (CL) control method for the BRFCFW. Through the improved rigid finite element (IRFE) formulation, a dynamic model of the flapping wing system is developed and visualized in MapleSim. Considering non-minimum phase behavior, the system dynamics are transformed into two subsystems through output redefinition: the inner system and the input–output system. For the inner system, we employ the critical gain method to adjust the proportional–derivative (PD) parameters. In the input–output system, the closed-loop control is designed to account for unknown dynamics by utilizing neural modeling error. Stability analysis of the closed-loop system is conducted using the Lyapunov method, and co-simulation is performed with MapleSim and MATLAB/Simulink software. At the end, the feasibility of the recommended strategy is confirmed by comparing it with PD control and neural network (NN) control strategies.

Fault-tolerant learning control of air-breathing hypersonic vehicles with uncertain parameters and actuator faults

This paper investigates fault-tolerant learning control for air-breathing hypersonic vehicles (AHVs) subject to parametric uncertainties, external disturbances, and actuator faults. By treating the flexible dynamics as equivalent disturbance, the AHV model can be decomposed into velocity subsystem and altitude subsystem. An intelligent fault diagnosis approach is deployed to estimate the actuator fault information for each subsystem sequentially. Furthermore, combined with a fixed-time neural disturbance observer, composite learning control is deployed to construct control commands using the historical stack to improve the learning accuracy, while compensating for lumped disturbances, including fault diagnosis errors and external disturbances. The designed controller addresses the fault-tolerant tracking problem by combining offline and online learning strategies, which is a significant advantage over other existing AHV controllers. Simulation results validate the effectiveness of the fault-tolerant learning control.

Composite learning control of strict‐feedback nonlinear system with unknown control gain function

AbstractThe composite learning control with the heterogeneous estimator is proposed to deal with the multiple uncertainties of strict‐feedback nonlinear systems. The article applies the recorded data‐based neural learning and the disturbance observer (DOB) to learn the multiple uncertainties, including the nonlinear dynamics, the unknown control gain function (CGF), and the time‐varying disturbance. The lumped prediction error is constructed and included into the update law by neural approximation and disturbance observation. Furthermore, the asymmetric saturation nonlinearity (ASN) of the control input is represented by the smooth form model to ensure the input limitation, and a projection algorithm is adopted to avoid the singularity problem. The closed‐loop system stability is rigorously analyzed and the boundedness of the system tracking error is guaranteed. Through the tests of the third‐order nonlinear system and the autonomous underwater vehicle (AUV), it is observed that the proposed approach can improve the system tracking accuracy with the expected learning performance.

Finite-time composite learning control for trajectory tracking of dynamic positioning vessels

This paper studies the trajectory tracking control method for dynamic positioning vessels with state constraints and parameter uncertainties. A composite learning method is introduced to identify unknown parameters online. Regressor filtering together with dynamic regressor extension and mixing procedure are combined not only to relax the dependence of parameter estimation convergence process on the persistent excitation condition, but also to ensure the independence and flexibility of each parameter. Subsequently, a finite-time composite learning controller is designed based on asymmetric integral barrier Lyapunov functions, which guarantees the asymmetric constraints on vessel states. Furthermore, Lyapunov stability shows that the parameter identification and tracking errors can converge to zero in finite-time. Finally, tracking control task for dynamic positioning systems is carried out to illustrate the merits of the proposed method.

Composite learning sliding mode control of uncertain nonlinear systems with prescribed performance

This paper explores the prescribed performance tracking control problem of nonlinear systems with triangular structure. To obtain the desired transient performance and precise estimations of uncertain terms, the techniques of neural network control, sliding mode control and composite learning control are incorporated into the proposed control method. The presented control strategy can ensure the tracking error converges to a prescribed small residual set. Compared with the persistent excitation condition required in the conventional adaptive control, the interval excitation condition needed in the proposed control approach is weak, which guarantees that the radial basis function neural networks approximate the unknown nonlinear terms more accurately. Finally, two simulation examples are exploited to manifest the effectiveness of the proposed approach.

Composite Learning Control of Uncertain Fractional-Order Nonlinear Systems with Actuator Faults Based on Command Filtering and Fuzzy Approximation

Composite Learning Control of Uncertain Fractional-Order Nonlinear Systems with Actuator Faults Based on Command Filtering and Fuzzy Approximation

Composite learning tracking control for underactuated marine surface vessels with output constraints.

In this paper, a composite learning control scheme was proposed for underactuated marine surface vessels (MSVs) subject to unknown dynamics, time-varying external disturbances and output constraints. Based on the line-of-sight (LOS) approach, the underactuation problem of the MSVs was addressed. To deal with the problem of output constraint, the barrier Lyapunov function-based method was utilized to ensure that the output error will never violate the constraint. The composite neural networks (NNs) are employed to approximate unknown dynamics. The prediction errors can be obtained using the serial-parallel estimation model (SPEM). Both the prediction errors and the tracking errors were employed to construct the NN weight updating. Using approximation information, the disturbance observers were designed to estimate unknown time-varying disturbances. The stability analysis via the Lyapunov approach indicates that all signals of unmanned marine surface vessels are uniformly ultimate boundedness. The simulation results verify the effectiveness of the proposed control scheme.

Bioinspired Composite Learning Control Under Discontinuous Friction for Industrial Robots

Adaptive control can be applied to robotic systems with parameter uncertainties, but improving its performance is usually difficult, especially under discontinuous friction. Inspired by the human motor learning control mechanism, an adaptive learning control approach is proposed for a broad class of robotic systems with discontinuous friction, where a composite error learning technique that exploits data memory is employed to enhance parameter estimation. Compared with the classical feedback error learning control, the proposed approach can achieve superior transient and steady-state tracking without high-gain feedback and persistent excitation at the cost of extra computational burden and memory usage. The performance improvement of the proposed approach has been verified by experiments based on a DENSO industrial robot.

Composite learning tracking control for underactuated autonomous underwater vehicle with unknown dynamics and disturbances in three-dimension space

In this paper, a composite learning tracking control scheme is developed for underactuated autonomous underwater vehicles (AUVs) in the presence of unknown dynamics and time-varying disturbances. Line-of-sight (LOS) tracking control is employed to handle the underactuation of AUVs. The unknown dynamics of the AUVs are approximated by adaptive neural networks (NNs). The serial-parallel estimation models are built to obtain the prediction errors. Both the prediction errors and the tracking errors are employed to design the composite weights updating law. Nonlinear disturbance observers based on composite learning control are constructed to estimate time-varying disturbances. The stability analysis via the Lyapunov method indicates that the uniformly ultimate boundedness of all signals of the AUV trajectory tracking close-loop control system. The simulation results on an AUV verify the effectiveness and the superiority of the proposed composite learning tracking control scheme.

Composite Learning Control of Overactuated Manned Submersible Vehicle With Disturbance/Uncertainty and Measurement Noise.

In this article, a novel composite learning control scheme based on nonlinear disturbance observer (NDOB), neural network (NN), and model-based state observer (MSOB) is investigated for the manned submersible vehicle. First, an MSOB is employed to reconstruct the real output signals from noise-contained measurements. Second, a composite estimation is developed where an NDOB is designed to estimate external disturbance and an NN is employed for model uncertainty. Furthermore, a control allocation technique is used to address the overactuated problem of the manned submersible vehicle. The rigorous stability analysis of the closed-loop manned submersible system is given via the Lyapunov theorem. Finally, several representative simulation results illustrate the superior control performance of the composite learning control scheme for the manned submersible vehicle.

Composite learning control of robotic systems: A least squares modulated approach

Most current studies of adaptive robot control concentrate on parameter convergence in the steady state, while parameter convergence rates are rarely investigated. This paper proposes a least-squares modulated composite learning robot control based on Moore–Penrose pseudoinverse to improve the performance of parameter convergence. In the composite learning, a prediction error is constructed based on online historical data and regressor extension, and both the prediction and tracking errors are applied to update parameter estimates such that accurate and smooth parameter estimation is obtained under a weak excitation condition termed interval excitation (IE). The distinctive features of the proposed method include: (1) Asymptotic stability of the closed-loop system is proven without the IE condition; (2) exponential stability is proven and balanced and easily tunable rates of parameter convergence are achieved under the IE condition, where the rates are independent of unpredictable excitation levels in different regressor channels. These two features are generally not achievable with the existing adaptive robot control methods. Experimental results on an industrial manipulator have demonstrated the effectiveness and superiority of the proposed approach.

Enhanced parameter estimation in adaptive control via online historical data

Parameter convergence is desirable for adaptive control as it enhances the overall stability and robustness properties of the closed-loop system. In existing online historical data (OHD)-driven parameter estimation schemes, all OHD are exploited to update parameter estimates such that exponential parameter convergence is ensured under a condition of sufficient excitation which is strictly weaker than the traditional persistent excitation (PE) condition. However, the exploitation of all OHD not only results in possible unbounded adaptation but also loses the flexibility of handling slow time-varying uncertainties. In this brief, a novel OHD-driven parameter estimation scheme that exploits only partial OHD is presented to improve parameter convergence and is incorporated with direct adaptive control to construct a composite learning control strategy. The proposed approach guarantees exponential parameter convergence under a condition of interval excitation which is also strictly weaker than the PE condition while eliminating the drawbacks of existing OHD-driven parameter estimation schemes. Numerical results have verified the effectiveness and superiority of the proposed approach.