Control Of Uncertain Nonlinear Systems Research Articles

Adaptive event-triggered control for uncertain nonlinear systems with unknown measurement sensitivity via output feedback

Adaptive event-triggered control for uncertain nonlinear systems with unknown measurement sensitivity via output feedback

Adaptive RISE-based tracking control of uncertain nonlinear systems: A FAS approach

A fully actuated system (FAS) approach integrated with adaptive robust integral of the sign of the error (ARISE) feedback control strategy is proposed for multi-input multi-output nonlinear systems in the presence of both external disturbances and parametric uncertainties. Owing to an inability to eliminate unmeasured disturbances and model inaccuracies simultaneously, the existing results based on the FAS approaches are typically limited to the uniformly ultimate boundedness of the tracking errors. To achieve the asymptotic tracking performance confronted with parametric uncertainties and time-varying disturbances, an ARISE feedback controller with desired compensation is synthesized to suppress the adverse effects arising from nonlinearity and uncertainty of the system. The improvements compared to the traditional RISE feedback control are attributed to two aspects: (i) the feedback gains in the RISE term are adjustable-online without having to know the prior bounds of disturbances and their time derivatives; (ii) a desired compensation-based adaptive feedforward term, primarily employing the desired trajectories in place of the measured states, could weaken the underlying interaction between the adaptive compensation and robustness part. A rigorous stability analysis is provided to demonstrate that the system state can asymptotically track a bounded desired trajectory in spite of bounded disturbances and parametric uncertainties. Comparative simulations on an under-actuated planar manipulator, possessing an equivalent multi-order FAS model, have been conducted to verify the effectiveness and merits of the developed controller. Experimental validation on a two-wheeled self-balancing robot is also provided to show the feasibility of the proposed approach.

Data-driven adaptive control for uncertain nonlinear systems

Data-driven adaptive control for uncertain nonlinear systems

Constraint-dependent switching event-triggered control for uncertain nonlinear systems with state constraints

Constraint-dependent switching event-triggered control for uncertain nonlinear systems with state constraints

Disturbance observer-based fuzzy adaptive switched tuning control of uncertain nonlinear systems with full state constraints

This article examines the issue of disturbance observer-based fuzzy adaptive switched tuning output feedback control for a family of uncertain nonlinear systems subjected to full state constraints. By introducing monotonically bounded positive time-varying gain functions (TVGFs), a set of improved intermediate-variable-based disturbance observers (IVBDOs) is devised. Applying the convex combination technique and the LMI toolbox, the solution of the observation gain matrix is realized. Combining the switched tuning control function and the composite integral barrier Lyapunov method, a new switching nature controller is constructed. Particularly, the developed framework overcomes the conservatism of conventional constraints. Through theoretical analysis, it is exhibited that for any given initial value of the system, all states of the system are enclosed within predetermined compact sets while all signals are bounded. Lastly, simulation and experimental results under unknown disturbances attest the validity and superiority of the suggested strategy.

Event-triggered adaptive tracking control for uncertain nonlinear systems with preassigned prescribed performance constraints under three cases

Event-triggered adaptive tracking control for uncertain nonlinear systems with preassigned prescribed performance constraints under three cases

Command Filtered-Based Adaptive Predefined-Time Control for Uncertain Nonlinear Systems With Applications to RLC Circuit

Command Filtered-Based Adaptive Predefined-Time Control for Uncertain Nonlinear Systems With Applications to RLC Circuit

Adaptive neural network terminal sliding mode tracking control for uncertain nonlinear systems with time-varying state constraints

Exploring a novel adaptive asymmetric sliding mode control methodology with time-varying state constraints (TVSCs), we address trajectory tracking issues in uncertain nonlinear systems. The asymmetric barrier Lyapunov functions (ABLFs) and neural networks is employed within each subsystem’s virtual control design process using back-stepping control (BSC) method. This ensures the imposition of TVSCs and effectively addresses challenges posed by system uncertainties. Additionally, to enhance the convergence of tracking deviations within small zero neighborhoods, a nonsingular integral terminal sliding mode control (NITSMC) method is incorporated into the actual control algorithm design. This method illustrates that, the system states consistently stay within the specified boundaries, tracking errors rapidly converge to a confined range. All signals within the system remain bounded. Simulation findings affirm the efficacy of the suggested control strategy.

Disturbance observer-based nonsingular fixed-time fuzzy adaptive event-triggered output feedback control of uncertain nonlinear systems

This article examines the fuzzy adaptive event-triggered output feedback control issue of eliminating fixed-time singularity for a type of uncertain nonlinear systems with disturbance observer structures. By introducing novel bounded time-varying functions (TVFs), a set of more general intermediate-variable-based disturbance observers (IVBDOs) is constructed. Combining the corresponding auxiliary reduced-power function and the quartic Lyapunov method, a co-design scheme for a fuzzy approximation high-order controller and an improved event-triggered mechanism with a judgment indicator threshold is proposed. Especially, the developed framework not only removes the singularity during the fixed-time design process but also further reduces the communication resources. By means of theoretical analysis, the devised solution achieves fixed-time stable control (FTSC) of the system and the tracking error converges to the desired neighborhood around the origin. After verifying the absence of Zeno behavior, the efficacy of the presented methodology is supported by two simulation examples.

Global output feedback control for uncertain nonlinear systems with input quantisation and sensor gain perturbation

A new quantised control method via output feedback is proposed by establishing two pairs of time-varying coupled matrix inequalities. Compared to relevant quantised control results, our control method has the novelty: (i) the sensor gain perturbation is allowed to be large and nondifferentiable; (ii) the growth constraint of system nonlinearities is relaxed to depend on unmeasured states and x 1 -polynomial function; (iii) a dynamic scaling technique, instead of backstepping design, is introduced to reduce the computational complexity of controller design. In particular, by using a symbolic function (or hyperbolic tangent function), global asymptotic stability is achieved under any hysteresis quantiser.

Fault tolerant control of uncertain hydraulic system against actuator fault based on redesign Lyapunov approach

The design of stabilizing controller for uncertain nonlinear systems with control constraints is a challenging problem. This paper proposes the design of fault tolerant control for a class of uncertain nonlinear systems with actuator faults based on Lyapunov redesign principle. First, an assumption is introduced to design the control of the nominal system. Then, a new control law has been introduced to handle the difficulty caused by actuator failures. It is shown that the actuator faults can be completely compensated by the proposed nonlinear controller. Finally, the method will be applied to a hydraulic system to show its effectiveness.

Logic-Based Fixed-Time Control for Uncertain Nonlinear Systems With Unknown Control Directions.

The fixed-time control problem is investigated for a class of uncertain nonlinear systems subjected to multiple unknown control directions. The control coefficients of nonlinear systems under consideration are time varying and their signs are not required to be identical. To tackle this challenge, a switching mechanism along with a novel dynamic boundary function is proposed. Utilizing the devised dynamic boundary function, adaptive parameters are introduced into the controller to effectively handle system uncertainties. It is proved that the system output converges to a small neighborhood of the origin in fixed time and the boundedness of all system signals is maintained. Finally, two simulation examples are used to show the validity of the presented switching control strategy.

Practical fixed-time adaptive fuzzy control of uncertain nonlinear systems with time-varying asymmetric constraints: a unified barrier function based approach

Practical fixed-time adaptive fuzzy control of uncertain nonlinear systems with time-varying asymmetric constraints: a unified barrier function based approach

Self-organizing feature selection fuzzy neural network-based terminal sliding mode control for uncertain nonlinear systems

A composite terminal sliding mode controller (CTSMC) for a kind of uncertain nonlinear system (UNS) is developed in this study. The primary aim of the design is to enhance the control performance of the CTSMC by learning its unknown parameters using a newly fuzzy neural network (FNN). Firstly, the stability and convergence of CTSMC for UNS with known parameters are demonstrated. Secondly, since some parameters of actual UNS are unmeasurable, a self-organizing feature selection fuzzy neural network (SOFSFNN) is intended to approach these unknown parts. Finally, the CTSMC using SOFSFNN is applied to UNS. The outcomes demonstrate that it has minimal tracking error, good robustness, and the ability to dynamically modify the network structure.

Dynamic event-triggered neuro-optimal control for uncertain nonlinear systems with unknown dead-zone constraint

In this article, we propose a dynamic event-triggered neuro-optimal control scheme (DETNOC) for uncertain nonlinear systems subject to unknown dead-zone and disturbances through the design of a composite control law. An integral sliding mode-based discontinuous control law is utilized to compensate for the effects of unknown dead-zone, disturbance, and a component of uncertainties. As a result, a system dynamics that evolves free of these effects during the sliding mode is obtained. Then, an adaptive dynamic programming-based dynamic event-triggered optimal control law is designed to stabilize the sliding mode dynamics with the help of critic-only neural network architecture. Finally, stability analysis of the closed-loop system is provided and the effectiveness of the developed DETNOC scheme is verified.

Disturbance-observer-based adaptive neural event-triggered fault-tolerant control for uncertain nonlinear systems against sensor faults

In this article, the adaptive event-triggered fault-tolerant control (FTC) issue of uncertain nonlinear systems suffering from sensor and actuator faults as well as external disturbances is studied. A disturbance observer (DO) is constructed to compensate for external disturbances and approximation errors generated by neural networks (NNs). Then, switching event-triggered control and command filtering techniques are introduced to balance the communication frequency and tracking performance of the controlled systems while avoiding “explosion of complexity” issue caused by iterative derivations of virtual controllers. Furthermore, compensation signals are designed to eliminate filtered errors. Finally, it is proven that the developed FTC scheme can esure that all signals are bounded and free from Zeno phenomenon. Simulation results of a spring damping mechanical system verifies the merits of the designed control algorithm.

Dynamic gain adaptive control for uncertain nonlinear systems with actuator and sensor faults

AbstractThis article focuses on the adaptive output feedback control problem for a class of uncertain nonlinear systems under unknown actuator and sensor faults. In contrast to existing works, we consider unknown time‐varying discontinues faults and loosen their conservative conditions on nonlinear functions. First, since there is just inaccurate output information is available, an observer based on dynamic gain is conceived to reconstruct state variables that cannot be measured. Second, the dynamic gain is introduced into the design of virtual controller by coordinate transformation, and is employed to construct Lyapunov function to handle unknown parameters. On the other hand, a new global output feedback control algorithm is proposed by utilizing dynamic gain which improves the flexibility of parameters selection on the premise of ensuring stability. Based on the Lyapunov stability theory, the resulting closed‐loop system is proved strictly all signals are global bounded. Finally, a simulation example is presented to illustrate the efficiency of the proposed control algorithm.

Adaptive Event-Triggered Fixed-Time Practical Tracking Control for Uncertain Nonlinear Systems

Adaptive Event-Triggered Fixed-Time Practical Tracking Control for Uncertain Nonlinear Systems

Safe MPC-based disturbance rejection control for uncertain nonlinear systems with state constraints

This paper studies a safe model predictive control (MPC)-based disturbance rejection control for a broad range of uncertain nonlinear systems subject to complex state safety constraints. The system under study is composed of a nominal model and an uncertain term that encapsulates modeling uncertainty, control mismatch, and external disturbances. In order to estimate the system state and total uncertainty, an extended state observer (ESO) is first designed. Utilizing the output of the ESO, the control compensates for the total uncertainty in real time and concurrently implements a control barrier function (CBF)-based MPC for the compensated system. The proposed control framework guarantees both safety and disturbance rejection. Compared to the baseline algorithm CBF-MPC, the proposed method significantly enhances system stability with a smaller root mean square (RMS) error of the system state from the equilibrium point. Rigorous theoretical analysis and simulation experiments are provided to validate the effectiveness of the proposed scheme.

Adaptive dynamic‐surface repetitive control for uncertain nonlinear systems with mismatched disturbances and input delay

AbstractBased on additive state decomposition technique, this article presents an adaptive dynamic‐surface repetitive control method for a class of uncertain nonlinear systems with mismatched disturbances and input delay. First, the original uncertain nonlinear system is decomposed into a linear time invariant (LTI) primary system responsible for the periodic signal tracing and rejection task, and a nonlinear secondary system with input time delay responsible for the robust stabilization task. A modified repetitive controller with a small correction to the delay constant is then independently designed for the LTI primary system, while an adaptive dynamic surface controller is designed for the nonlinear secondary system. Stability analysis is performed for each subsystem and the design procedure of the whole system is presented in detail. Finally, comparative simulations with other repetitive control and dynamic surface control methods show that the presented method not only relaxes the constraints on the reference inputs and exogenous disturbances, but also ensures that the system has better disturbance rejection and periodic‐signal tracking performance in both transient and steady states. As a result, this method broadens the application of both repetitive control and dynamic surface control.