Observer-based Adaptive Control Research Articles

Observer-based adaptive neural network control: the convergence properties analysis under the influence of persistent excitation level

Observer-based adaptive neural network control: the convergence properties analysis under the influence of persistent excitation level

Adaptive Event-Triggered Consensus Control of Nonlinear Multi-Agent Systems via Output Feedback Methodology: An Application to Energy Efficient Consensus of AUVs

For dealing with the energy consumption in multi-agent systems (MASs), an event-triggered (ET) methodology is promising, which relies on the activation of communication devices only when communication of data is needed. This paper explores the leaderless consensus for nonlinear MASs using an adaptive ET approach via an output feedback methodology. This adaptive ET scheme is preferred as it can adapt to the environment through setting a communication threshold. The proposed approach renders the observed states of agents by use of nonlinear observers in an output feedback control dilemma, making it more practical. Simple Luenberger observers are developed to avoid the problem of always measuring agents’ states. The strategy of adaptive ET-based control is employed to minimize resource use and information transmission. Design conditions for the observer-based adaptive ET consensus control of nonlinear MASs have been derived via a Lyapunov function, containing state estimation error, consensus error, adaptation term, and nonlinearity bounds. In contrast to the existing methods, the present approach applies a more practical output feedback schema, uses adaptive ET proficiency, and deals with nonlinear agents. An example of a formation of autonomous underwater vehicles achieving the basic consensus realization between displacement and velocity is included to illustrate the viability of the resultant approach.

Multi-bandwidth observer-based adaptive robust pressure control for electro-hydraulic brake system with uncertainties and measurement noise

Accurate pressure regulation of electro-hydraulic brake system (EHB) is essential for enhancing braking performance in automobiles. However, component wear and pressure measurement noise can cause model parameters and controlled states to drift, resulting in system chattering. This nonlinear and uncertain state can significantly degrade pressure control performance. Motivated by this, the article presents a controlled state reconstruction-based adaptive robust pressure control technique with noise suppression. Firstly, a reduced dynamics model is established for controller design, which effectively captures the fundamental characteristic of the EHB. Secondly, a multi-bandwidth extended state observer (MBESO) capable of noise suppression is designed to reconstruct controlled states, including unknown disturbances and unmeasured pressure change rates of the EHB. Thirdly, an MBESO-based adaptive robust pressure control technique is introduced, where the control gain and robust factor are updated based on control and observation errors. In addition, the overall performance of the proposed control strategy is analyzed in the frequency domain, and close-loop stability is demonstrated via the Lyapunov method. Finally, the pressure control precision and robustness of the proposed algorithm are validated in both dynamic and static scenarios through sinusoidal and step-response experiments on a hardware-in-loop test bench. The results indicate that the proposed algorithm reduces dynamic pressure-tracking error by up to 62% compared to traditional method under sinusoidal conditions, with steady-state error remaining within 1 bar under step-response conditions.

Compensation Function Observer-based Adaptive Anti-swing Control for Quadrotor Slung-load System With Constrained Flight States

Compensation Function Observer-based Adaptive Anti-swing Control for Quadrotor Slung-load System With Constrained Flight States

Observer-Based Adaptive Fuzzy Control for Singular Systems with Nonlinear Perturbation and Actuator Saturation

Observer-Based Adaptive Fuzzy Control for Singular Systems with Nonlinear Perturbation and Actuator Saturation

Disturbance observer-based adaptive fuzzy control for pure-feedback systems with deferred output constraints

Disturbance observer-based adaptive fuzzy control for pure-feedback systems with deferred output constraints

Observer-Based Adaptive Event-Triggered Control for Nonlinear Networked Systems Under Multiple Cyber Attacks

Observer-Based Adaptive Event-Triggered Control for Nonlinear Networked Systems Under Multiple Cyber Attacks

Finite-Time Disturbance Observer-Based Adaptive Course Control for Surface Ships.

In this paper, a finite-time disturbance observer-based adaptive control strategy is proposed for the ship course control system subject to input saturation and external disturbances. Based on the Gaussian error function, a smooth saturation model is designed to avoid the input saturation of the system and reduce steering engine vibrations, and an auxiliary dynamic system is introduced to compensate for the effect of the rudder angle input inconsistency on the system. By constructing an auxiliary dynamic, a finite-time disturbance observer is designed to approximate the external disturbance of the system; an adaptive updating law is also constructed to estimate the upper bound of the derivative of the external disturbance. Combining the finite-time disturbance observer with the auxiliary dynamic system, a novel adaptive ship course control law is proposed by using the hyperbolic tangent function. Moreover, according to LaSalle's Invariance Principle, a system stability analysis method with loose stability conditions and easy realizations is designed, while the stability of the closed-loop system and the ultimately uniformly boundedness of all its signals are proven. Finally, the course control simulation analysis of a surface ship is carried out. The results show that the proposed control law has a strong resistance to external disturbances and a strong non-fragility to system parameter perturbations, which ensure that the course control system has great control performance.

Observer-based adaptive fuzzy control of stabilized platform in rotary steerable system with input saturation and output constraint

This paper explores the control strategy for toolface tracking in the stabilized platform of a rotary steerable system, considering both input saturation and output constraint. To mitigate the impact of unknown friction torque and modeling error on the stabilized platform, we propose an adaptive fuzzy backstepping control approach. A fuzzy state observer is specifically devised to handle the uncertain state arising from unknown friction torque and modeling errors. Introducing an auxiliary system effectively compensates for input saturation, and the resolution of the output error constraint is achieved through the construction of a barrier Lyapunov function. Furthermore, employing the Lyapunov method establishes that all signals in the entire closed-loop control system are semi-globally uniformly ultimately bounded. Simulation results confirm the efficacy of the proposed control methodology.

Observer-based adaptive memory event-triggered consensus tracking control for high-speed train under DoS attacks

This paper focuses on the event-triggered consensus tracking of high-speed trains under denial-of-service (DoS) attacks. A novel anti-disturbance control strategy is proposed, based on an adaptive memory state observer and a disturbance observer. A memory is introduced to buffer system output information which enhancing the precision of the state observer, and the gust disturbance during the high-speed train operation is estimated by the disturbance observer. The Linear Matrix inequality technique is used to obtain the observer feedback coefficient and the controller gain, and the Lyapunov theory is employed to demonstrate the controller’s consistency tracking performance. Applying the memory to the event-triggered mechanism, a new adaptive memory event-triggered scheme is designed to better ensure system performance and effectively prevent the occurrence of Zeno behavior. To achieve precise identification of DoS attacks in the event-triggered environment, a new detection algorithm is proposed. Finally, simulation examples are used to validate the effectiveness and practicality of the proposed scheme.

Disturbance Observer-Based Adaptive Fault Tolerant Control with Prescribed Performance of a Continuum Robot

This paper studies an adaptive fault tolerant control (AFTC) scheme for a continuum robot subjected to unknown actuator faults, dynamics uncertainties, unknown disturbances, and prescribed performance. Specifically, to deal with uncertainties, a function approximation technique (FAT) is employed to evaluate the unknown actuator faults and uncertain dynamics of the continuum robot. Then, a nonlinear disturbance observer (DO) is developed to estimate the unknown compounded disturbance, which contains the unknown disturbances and approximation errors of the FAT. Furthermore, the prescribed error bound is treated as a time-varying constraint, and the controller design method is based on an asymmetric barrier Lyapunov function (BLF), which is operated to strictly ensure the steady-state and transient performance of the continuum robot. Afterwards, the simulation results validate the effectiveness of the proposed AFTC in dealing with the unknown actuator faults, uncertainties, unknown disturbances, and prescribed performance. Finally, the effectiveness of the proposed AFTC scheme is verified through experiments.

Observer-Based Adaptive Fuzzy Fractional Backstepping Consensus Control of Uncertain Multiagent Systems via Event-Triggered Scheme

Observer-Based Adaptive Fuzzy Fractional Backstepping Consensus Control of Uncertain Multiagent Systems via Event-Triggered Scheme

Reduced-Order Observer-Based Adaptive Fuzzy Self-Triggered Control for Fractional Order Nonlinear Systems Without Feasibility Conditions

Reduced-Order Observer-Based Adaptive Fuzzy Self-Triggered Control for Fractional Order Nonlinear Systems Without Feasibility Conditions

Large-Signal Stabilization of Interleaved Multilevel Boost Converter Interfaced High-Power DC Microgrid Using an Adaptive Observer-Based Nonlinear Control Scheme

Large-Signal Stabilization of Interleaved Multilevel Boost Converter Interfaced High-Power DC Microgrid Using an Adaptive Observer-Based Nonlinear Control Scheme

Observer-Based Adaptive Control for a Coupled PDE–ODE System of a Flexible Wing

Observer-Based Adaptive Control for a Coupled PDE–ODE System of a Flexible Wing

Output-Feedback Consensus Maneuvering of Uncertain MIMO Strict-Feedback Multiagent Systems Based on a High-Order Neural Observer.

In this article, a distributed output-feedback consensus maneuvering problem is investigated for a class of uncertain multiagent systems with multi-input and multi-output (MIMO) strict-feedback dynamics. The followers are subject to immeasurable states and external disturbances. A distributed neural observer-based adaptive control method is designed for consensus maneuvering of uncertain MIMO multiagent systems. The method is based on a modular structure, resulting in the separation of three modules: 1) a variable update law for the parameterized path; 2) a high-order neural observer; and 3) an output-feedback consensus maneuvering control law. The proposed distributed neural observer-based adaptive control method ensures that all followers agree on a common motion guided by a desired parameterized path, and the proposed method evades adopting the adaptive backstepping or dynamic surface control design by reformulating the dynamics of agents, thereby reducing the complexity of the control structure. Combined with the cascade system analysis and interconnection system analysis, the input-to-state stability of the consensus maneuvering closed loop is established in the Lyapunov sense. A simulation example is presented to demonstrate the performance of the proposed distributed neural observer-based adaptive control method for output-feedback consensus maneuvering.

Observer-based adaptive neural network control design for nonlinear systems under cyber-attacks through sensor networks

This paper considers the adaptive neural tracking control for uncertain nonlinear strict-feedback systems under state-dependent cyber-attacks through sensor networks. As a distinctive feature, by constructing a novel adaptive neural observer, the estimates of system states are used for feedback control instead of the compromised system states corrupted by sensor attacks, such that the assumption on the derivative of attack weight can be removed from the adaptive controller design process. Then, an observer-based control scheme is developed such that all the system signals are bounded and the tracking error converges to a small neighborhood of zero. During the procedure of control design, adaptive backstepping technique is combined with radial basis function neural networks (RBFNNs) to construct controllers. Finally, two examples validate the efficacy of the proposed control scheme.

Observer-Based Adaptive Control for Uncertain Fractional-Order T-S Fuzzy Systems with Output Disturbances

Observer-Based Adaptive Control for Uncertain Fractional-Order T-S Fuzzy Systems with Output Disturbances

Adaptive observer-based integral event-triggered antivibration control of a full aircraft active landing gear system with irregular runway excitations

Adaptive observer-based integral event-triggered antivibration control of a full aircraft active landing gear system with irregular runway excitations

Observer-based adaptive neutral network inverse optimal containment control for nonlinear multiagent systems with input quantization

This article focuses on the issue of addressing an adaptive neural network (NN) inverse optimal containment control for nonlinear multiagent systems (MASs), which are subject to immeasurable states and quantized input signals simultaneously. To tackle this problem, we utilize a NN to model unknown agents and design a NN observer to estimate the immeasurable states. Additionally, we decompose the hysteretic quantized input into two bounded nonlinear functions. By employing the adaptive backstepping approach and inverse optimal principle, we formulate an adaptive NN inverse optimal containment control method. The developed inverse optimal containment control scheme guarantees that the controlled system is input-to-state stabilizable (ISS). Finally, we validate the effectiveness of our proposed control scheme through simulation results.