Observer-based Control Research Articles

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

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

Fast Finite-Time Observer-Based Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems with Full-State Constraints.

This article studies a class of uncertain nonlinear multiagent systems (MASs) with state restrictions. RBFNNs, or radial basis function neural networks, are utilized to estimate the uncertainty of the system. To approximate the unknown states and disturbances, the state observer and disturbance observer are proposed to resolve those issues. Moreover, a fast finite-time consensus control technique is suggested in order to accomplish fast finite-time stability without going against the full-state requirements. It is demonstrated that every signal could be stable and boundless, and an event-triggered controller is considered for the saving of resources. Ultimately, the simulated example demonstrates the validity of the developed approach.

Observer-Based Finite-Time Adaptive Fault-Tolerant Control for Nonlinear System with Unknown Time-Varying Delay

Observer-Based Finite-Time Adaptive Fault-Tolerant Control for Nonlinear System with Unknown Time-Varying Delay

Sampled-data event-triggered control with sampling-independent positive guarantees on inter-event times

To fit with a digital environment in networked control systems, sampled-data event-triggered control (ETC) has been proposed where the event-triggering mechanisms sense and process information only at discrete sampling instants (not necessarily periodically). One deficiency in the previous study on sampled-data ETC is the lack of analysis on transmission performance, which yields the potential occurrence of an undesirable phenomenon that the minimum inter-event time is always equal to the minimum inter-sampling interval, no matter how small the latter is. To overcome this drawback, in this paper, we propose a novel sampled-data ETC scheme such that the lower bounds of inter-event times have sampling-independent positive guarantees. A linear networked control system is considered under observer-based controllers, external disturbances, and multiple communication channels. The improved transmission performance is due to the utilization of dynamic event-triggering conditions and some mean-rate error signals, which are the ratios between network-induced errors and some time-increasing functions. After modeling the closed-loop dynamics into a hybrid system, sufficient conditions on the upper bound of inter-sampling intervals and parameters in ETC are provided to ensure input-to-state stability (rather than its practical version) and sampling-independent positive guarantees simultaneously. Furthermore, a new tradeoff relationship between the sampling and transmission performance is revealed: a faster sampling frequency is conducive to improving inter-event times. Finally, a linearized model of an inverted pendulum is simulated to illustrate the efficiency and feasibility of the obtained results.

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.

Neural Network and Extended State Observer-Based Model Predictive Control for Smooth Braking at Preset Points in Autonomous Vehicles

Neural Network and Extended State Observer-Based Model Predictive Control for Smooth Braking at Preset Points in Autonomous Vehicles

Observer-based security control for state-dependent fractional-order system with multiple attacks via polytope-type approach and application to tunnel diode model

This work examines a polytope-type approach for observer-based security control of a fractional-order system that is under multiple attacks via an event-triggered scheme. A novel multiple-attack model is first proposed for fractional-order systems such as denial-of-service attacks and reply attacks, which can destroy the system's security during the signal process. In contrast to previous studies, the design of the controller is developed using an observer-based event-triggered scheme in the presence of multiple attacks. An event-triggering strategy is proposed to reduce the communication burden of the output measurement signals. An observer-based control is designed to keep the fractional-order system stable in all scenarios where one or more simultaneous attacks may occur. These findings can ensure the absence of Zeno behaviour and considerably reduce data transmissions. By employing the suitable Lyapunov–Krasovskii functional, sufficient conditions are derived to ensure fractional-order system stability. Controller and observer-gain matrices are obtained by using the linear matrix inequality technique. Finally, two examples are provided to demonstrate the feasibility and usefulness of the suggested methodology.

Lowering carbon emissions from a zinc oxide rotary kiln using event-scheduling observer-based economic model predictive controller

Background:The leading carbon emitting unit of the zinc smelting industry is the zinc oxide rotary kiln, which preserves a trade-off between carbothermic reduction and the zinc recovery rate. To scale up a substantial zinc restoration rate during the hydrometallurgical process, a substantial amount of coke is combusted, which leads to significant greenhouse gas emissions. It is imperative to mitigate the emission of carbon as reduced as possible. Since the kiln operates in three different regions — gas, solid, and wall — several highly non-uniform paths are typically linked to one another to yield complex looping processes. In industrial settings, solid maldistribution and gas refluxing pose prevalent challenges in the gas–solid flow mechanism. Consequently, control over processes and observation of the internal states/concomitant parameters become critical for ensuring safe and reliable operation. Therefore, it is imperative to devise effective control layouts to optimize the functioning of the kiln system. Method:(i) The usual form of model-predictive control rule is paired with an economic stage cost (using the CasADi optimization method) to offer optimal consumption of the coal and the rotation speed, thereby effectively alleviating the adverse effects of the unmeasured perturbations, actuator faults, or multi-sensor failures. (ii) With the aid of accessible state sensors, a cubature Kalman filter infused with a singular-value-decomposition strategy reliably predicts uncertainties/faults of the actuators or the unmeasured states of the kiln. (iii) cubature Kalman filter and event-triggered scheduling are utilized together, to consume less communication resources. Significant findings:A variety of indicators, such as zinc recovery rate (1.35% improvement), carbon emissivity (11.54% improvement), residence time (6% shorter), accuracy of the controllers/observers were well assessed and compared with standard form of the non-linear model predictive control law to figure out the efficiency and acceptability of the economic model predictive controller, demonstrating on an industrial rotary kiln. An in-depth study of the proposed control method satisfying Lyapunov inequality has been addressed.

Resilient interval observer-based coordination control for multi-agent systems under stealthy attacks

In this paper, the coordination control problem of multi-agent systems (MASs) subjected to stealthy attacks and uncertainties is considered, where the uncertainties refer to unknown external disturbances and initial states. We consider a scenario in which the sensors are maliciously attacked, rendering the traditional interval observer’s bounds ineffective for estimating the system states. By solving the linear programming problem, the maximum destructive attack sequences are generated. A distributed resilient interval observer composed of a resilient framer and a control protocol is designed, in which the construction of control protocol for each agent depends on the framer information of its own and its neighbors. It is demonstrated by the cooperativity theory and the Lyapunov stability theory that the distributed resilient interval observer can not only realize interval-valued state estimation for each agent but also drive the cooperative behavior of MASs. In addition, the observer gain matrix is selected and optimized by solving a semi-definite programming problem to provide tighter bounds for each agent. Meanwhile, the bounds of stealthy attacks are calculated. Finally, through a simulation example, the superiority of the distributed resilient interval observer and the effectiveness of the resilient interval observer-based consensus control algorithms are validated.

Nonlinear observer-based impedance control of a fully-actuated hexarotor for accurate aerial physical interaction

PurposeFully-actuated unmanned aerial vehicles (UAVs) are a growing and promising field of research, which shows advantages for aerial physical interaction. The purpose of this paper is to construct a force sensor-denied control method for a fully-actuated hexarotor to conduct aerial interaction with accurate force exerted outward.Design/methodology/approachFirst, by extending single-dimension impedance model to the fully-actuated UAV model, an impedance controller is designed for compliant UAV pose/force control. Then, to estimate the interaction force between UAV end-effector and external environment accurately, combined with super-twisting theory, a nonlinear force observer is constructed. Finally, based on impedance controller and estimated force from observer, an interaction force regulation method is proposed.FindingsThe presented nonlinear observer-based impedance control approach is validated in both simulation and environments, in which the authors try to use a fully-actuated hexarotor to accomplish the task of aerial physical interaction finding that a specified force is able to be exerted to environment without any information from force sensors.Originality/valueA solution of aerial physical interaction for UAV system enabling accurate force exerted outward without any force sensors is proposed in this paper.

Predefined-Time Platoon Control of Unmanned Aerial Vehicle with Range-Limited Communication

In this paper, the predefined-time platoon control for multiple uncertain unmanned aerial vehicles (UAVs) under range-limited communication and external disturbance constraints is considered. A novel control scheme, which can guarantee communication connectivity, collision avoidance, and the predefined convergence time simultaneously, is proposed. To achieve disturbance robustness, an observer-based distributed control law is firstly proposed with a time-varying gain. Then, a radial basis function neural network (RBFNN) with an adaptive tuning law is applied to approximate uncertainties of the system. Under the time and error transformation techniques, uniformly ultimate boundedness (UUB) stability of the closed-loop system is guaranteed within predefined convergence time. Compared with the existing results, the proposed method allows the system to have UUB within any predefined time without depending on the initial conditions or system parameters. Finally, simulation results are presented to verify the derived theorem.

Observer-based leader-following cluster consensus for positive multi-agent systems with input time-varying delay

ABSTRACT This work aims to analyse the cluster consensus for positive multi-agent systems (MASs) with nonlinearities. This study focuses on introducing an observer-based controller with the aim of achieving cluster consensus in the considered positive nonlinear MASs, where the controller includes time-varying delay. Specifically in cluster consensus, agents are divided into multiple clusters to accomplish various collective tasks, each influenced by distinct inter-cluster and intra-cluster communications. In this study, a fixed directed graph is employed to express the communication among agents. By designing a proper Lyapunov-Krasovskii functional and utilising the properties of graph, a set of conditions required for cluster consensus of the examined MASs is established as linear matrix inequalities. Finally, the effectiveness of the derived theoretical findings is confirmed through numerical examples.

Dynamic Output Feedback of Second-Order Systems: An Observer-Based Controller with Linear Matrix Inequality Design

This paper presents an observer-based dynamic output-feedback controller design procedure using linear matrix inequality (LMI) optimization for second-order systems with uncertainty and persistent perturbation in the states. Using linear-quadratic criteria, cost functions are minimized in a two-stage procedure to compute optimal state-feedback gains, and observer gains are coupled into a dynamic output-feedback optimal controller. The LMI set used in the two stages is matrix inversion free, a key issue for polytope formulation when uncertainty is present. The approach is tested in a mobile inverted pendulum robotic platform, and the effectiveness is verified in this underactuated and undesensed case.

Observer-based hierarchical distributed model predictive control for multi-linear motor traction systems

This paper proposes an observer-based hierarchical distributed model predictive control (MPC) strategy for ensuring speed consistency in multi-linear motor traction systems. First, a communication topology is considered to ensure information exchange. Secondly, the control architecture of each agent is divided into upper layers and lower layers. The upper layer utilizes a distributed MPC method to track the leader’s speed. The lower layer uses a decentralized MPC method to track the command signals sent by its upper layer controller. In addition, to eliminate the negative impact of disturbance, a nonlinear disturbance observer is designed. We then prove the asymptotic stability of the entire system by properly designing the Lyapunov equation. Finally, the feasibility of the proposed strategy is verified based on several simulations.

Observer based pitch control for load mitigation and power regulation of floating offshore wind turbines

Abstract Most commercial wind turbines use proportional-integral (PI) collective blade-pitch control to regulate rotor speed in the above-rated wind speed regime. A significant drawback of this type of controller is that it assumes that the blades have identical structural properties and are subject to similar aerodynamic loads, which is seldom the case. Also, these controllers are designed to regulate the rotor speed and are not designed for structural vibration/load reduction. However, it is well known that blade pitch control can reduce structural loads on wind turbines. This opens up the possibility of designing controllers that use existing actuators and sensors like the blade pitch actuators to reduce structural loads/vibrations while maintaining the required rotor speed. Recent studies have investigated individual blade pitch control (IPC) to address these shortcomings. However, the vast majority of studies published in the literature depend on the availability of state measurement. Although sensors are commonly placed on all wind turbines, and some information is readily available, the measurement required by the typical state-feedback controllers is usually not available. Displacements and velocities of the blade, the tower and the floating platform are difficult to measure. This paper develops an observer-based individual blade pitch controller for load mitigation and power regulation of floating offshore wind turbines. We propose to use a Kalman filter to estimate the state from the accelerometer and strain gauge measurement for use in the state-feedback controller. The state-feedback controller was proposed previously by the authors that showed excellent performance. This paper extends the capability of the state-feedback controller by designing an observer (Kalman filter) to estimate the state from limited measurements. The proposed observer based controller is compared against a baseline proportional integral collective blade pitch controller and full state-feedback controllers to evaluate its performance. Numerical results show that the proposed output feedback controller offers performance improvements over the baseline controller, similar to the full state-feedback controller.

Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method.

Disturbance Observer-Based Prescribed-Time Tracking Control of Nonlinear Systems With Non-Vanishing Uncertainties

Disturbance Observer-Based Prescribed-Time Tracking Control of Nonlinear Systems With Non-Vanishing Uncertainties

Extended Two-State Observer-Based Speed Control for PMSM With Uncertainties of Control Input Gain and Lumped Disturbance

Extended Two-State Observer-Based Speed Control for PMSM With Uncertainties of Control Input Gain and Lumped Disturbance