Input Observer Research Articles

Distributed Antittack Fault-Tolerant Tracking Control for Vehicle Platoon Systems Under Cyber-Physical Threats

Vehicle platoon systems are considered as automatous vehicles in a platoon-based driving pattern in which a following vehicle follows the preceding vehicle and maintains the desired vehicle spacing. This study investigates the leader-following tracking issue of vehicle platoon systems under cyber-physical threats with the distributed anti-attack fault-tolerant tracking control strategy. Vehicle platoon systems, complicated actuator faults in physical layer, and connectivity-mixed attacks in cyber layer are modeled, respectively. Decentralized fault-estimation unknown input observer and distributed anti-attack fault-tolerant tracking control designs are developed in an integrated control framework to guarantee the robust and resilient tracking property of estimation errors and platoon tracking errors as well as the reliable inter-vehicle spacing by virtue of attack activation rate and attack frequency metrics. Simulations validate the proposed distributed anti-attack fault-tolerant tracking control algorithm in pernicious cyber-physical threatened scenarios.

Unknown input observers for time-varying delay Takagi–Sugeno fuzzy systems with unmeasured nonlinear consequents

This paper investigates the problem of unknown input observer (UIO) design for Takagi–Sugeno (TS) fuzzy systems with time-varying delay. For observer design, all the unmeasured nonlinearities are isolated in the nonlinear consequent parts of TS fuzzy systems. This allows dealing with the well-known issue related to unmeasured premise variables for TS fuzzy observer design in a more effective fashion via the mean value theorem. Using the unknown input (UI) decoupling technique, no a priori information on the UI is needed for the proposed TS fuzzy observer design, which can guarantee the asymptotic estimations of both the system state and the unknown input. Employing a fuzzy Lyapunov–Krasovskii functional with a suitable augmented vector, the effect of time-varying delay on the estimation error dynamics is taken into account in the observer design. Numerical examples are presented to demonstrate the effectiveness of the proposed fuzzy UIO design method for nonlinear time-varying delay systems.

Defending State-Feedback Based Controllers Against Sensor Attacks

Context: This paper is motivated by the need to improve the resilience of industrial control systems. Many control systems currently operating in the industry were designed and implemented before the boom in communications (wired and wireless networks) within industrial control systems. However, nowadays they operate connected to the communications network. This increase in connectivity has made the systems susceptible to cyber-attacks that seek to degrade the proper operation of the control loop even affecting only one sensor.
 Method: We use concepts from fault tolerant control and classic control theory to show that it is possible to reconstruct the system state without (any) one of the system outputs. This fact is used in the recalculation of the control signal through an algorithm of attack detection and isolation, to avoid for an attack to be feedback to the system, mitigating its effect. We show the effectiveness of our proposal with simulations on a four-tanks testbed using Matlab and Simulink.
 Results: We show that a bank of unknown input observers can be designed to recover true information from attacked sensors, i.e., recover the information without the effect of the attack. Therefore, the estimation obtained from those observers can be utilized for computing a control action that mitigates the effect of the attack.
 Conclusions: This mitigation of the attack prevents a single sensor attack from signi cantly impair the action of low-level controllers, improving the resilience on the system just modifying the digital controller architecture. The development presented is limited to cyber-attacks on system sensors pre- sented one at a time, that still can compromise seriously the system behavior. Future work will address the extension of the presented results in situations with simultaneous attacks on more than one sensor, and/or consider attacks on the control system actuators.

Consensus Control of Linear Parameter-Varying Multi-Agent Systems with Unknown Inputs.

This paper investigates the observer-based consensus control problem for linear parameter-varying (LPV) multi-agent systems (MASs) with unknown inputs. Firstly, an interval observer (IO) is designed to generate the state interval estimation for each agent. Secondly, an algebraic relationship is established between the system state and unknown input (UI). Thirdly, an unknown input observer (UIO) capable of generating estimates of UI and the system state has been developed through the algebraic relations. Finally, a UIO-based distributed control protocol scheme is proposed to realize the consensus of the MASs. In the end, to verify the validity of the proposed method, an example of a numerical simulation is given.

Unknown Input Observer-Based Fixed-Time Trajectory Tracking Control for QUAV with Actuator Saturation and Faults

The trajectory tracking control problem of a quadrotor unmanned aerial vehicle (QUAV) subject to external disturbances, inertia uncertainties, actuator faults, and input saturation is addressed in this paper. In contrast with previous works, input saturation herein refers to rotor speed saturation rather than thrust and torque saturation. First, the control system is decoupled into translational and rotational subsystems. Then, for both subsystems, two novel fixed-time unknown input observers (UIO) based on disturbance filtering are developed to estimate the lumped disturbance rapidly and precisely without awareness of the boundary of disturbances. Furthermore, fixed-time tracking controllers for translational and rotational subsystems are proposed based on the estimation values provided by UIO to stabilize tracking errors into a small region in fixed time regardless of the initial values. The theoretical analysis based on the Lyapunov method is presented to demonstrate the stability. Finally, the simulation results show that the proposed control method is effective. The comparison simulation is carried out to validate superiority of the proposed observer and its advantage can be summed up as: (1) the upper bound of the disturbance or its derivative is not needed; (2) the estimation results are smoother and the observation precision is higher due to the absence of sign function; (3) the mutant disturbance can be also estimated quickly and precisely.

Robust Sensor Fault Detection for a Single-Phase Pulse Width Modulation Rectifier

Maintaining safe and efficient operation in a single-phase pulse width modulation (PWM) rectifier that employs current sensors relies heavily on accurate sensor readings. However, several factors such as environmental conditions, aging, or damage can lead to sensor faults. Therefore, it is imperative to implement robust fault detection methods to ensure reliable system operation. The use of unknown input observer techniques is one such method that involves analyzing the differences between actual and estimated states to detect and identify faults in the system. This paper presents the development of a fault detection method that employs an unknown input observer with high sensitivity to faults and disturbance rejection to achieve robust fault detection. The method involves modeling the system as a state-space model and designing an observer to estimate the system’s state variables based on input and output measurements. The deviations between the actual and estimated states are then analyzed to detect and identify sensor faults, without the need for additional hardware, making it a cost-effective solution. Hardware-in-the-loop tests confirm the effectiveness of the proposed method.

Fault diagnosis and fault‐tolerant control for bilinear stochastic distribution systems with actuator fault

SummaryIn the paper, the problem of fault diagnosis and fault‐tolerant control for the bilinear stochastic distribution system with actuator fault is studied. Firstly, based on a grain processing device, a bilinear stochastic distribution system model is established. Then, a new unknown input observer is designed to diagnose the fault, and a model‐predicted fault‐tolerant controller is designed for the system on the basis of the estimated fault information, in order to keep the system stable or still maintain the control performance after the fault occurs. Finally, the validity of the proposed algorithm is verified by the grain processing process.

Robust Discrete-Time Lateral Control of Racecars by Unknown Input Observers

This brief addresses the robust lateral control problem for self-driving racecars. It proposes a discrete-time estimation and control solution consisting of a delayed unknown input-state observer (UIO) and a robust tracking controller. Based on a nominal vehicle model, describing its motion with respect to a generic desired trajectory and requiring no information about the surrounding environment, the observer reconstructs the total force disturbance signal, resulting from imperfect knowledge of the time-varying tire-road interface characteristics, presence of other vehicles nearby, wind gusts, and other model uncertainty. Then, the controller actively compensates the estimated force and asymptotically steers the tracking error to zero. The brief also presents a closed-loop stability proof of the method, ensuring perfect asymptotic estimation and tracking by the controlled vehicle. The proposed solution advantageously needs no a-priori information about the total disturbance boundedness, additional variables to model uncertainty, or observer parameters to be tuned. Its effectiveness and superiority to existing methods are studied in theory and shown in simulations where a full racecar model, based on the vehicle dynamics blockset, is required to track aggressive maneuvers. Through a faster and more accurate disturbance estimation, the solution robustly ensures better dynamic responses even with measurement noise.

Identification of track irregularities with the multi-sensor acceleration measurements of vehicle dynamic responses

Track irregularities induce potential risks to the safety and stability of railway track systems. This paper proposes a novel methodology to identify vertical and lateral track irregularities. The method involves measuring system-based attitude calculation and a model-based unknown input observer estimator, based on the dynamic responses of distributed multi-sensors on the vehicle and bogie. First, a mechanical model of wheel-rail contacts is built with dynamic methods. The model considers the different directions of motion for a railway vehicle and consists of two bogies and four wheelsets. Based on the multi-sensor acceleration measurement, the vertical and lateral acceleration signals of the vehicle and bogies are integrated into the displacement signal. Then a state-space description of the vehicle suspension model is established for inverse dynamical analysis to extract the input signals. A suitable unknown input observer is constructed to estimate the track irregularities by transforming the state space equations of the vehicle into an augmented system that can monitor the track irregularities in-service. This method provides an opportunity to reduce the costs of the monitoring infrastructure and provide quicker and more reliable information about the status of a track.

Virtual Sensor: Simultaneous State and Input Estimation for Nonlinear Interconnected Ground Vehicle System Dynamics.

This paper proposes a new observer approach used to simultaneously estimate both vehicle lateral and longitudinal nonlinear dynamics, as well as their unknown inputs. Based on cascade observers, this robust virtual sensor is able to more precisely estimate not only the vehicle state but also human driver external inputs and road attributes, including acceleration and brake pedal forces, steering torque, and road curvature. To overcome the observability and the interconnection issues related to the vehicle dynamics coupling characteristics, tire effort nonlinearities, and the tire-ground contact behavior during braking and acceleration, the linear-parameter-varying (LPV) interconnected unknown inputs observer (UIO) framework was used. This interconnection scheme of the proposed observer allows us to reduce the level of numerical complexity and conservatism. To deal with the nonlinearities related to the unmeasurable real-time variation in the vehicle longitudinal speed and tire slip velocities in front and rear wheels, the Takagi-Sugeno (T-S) fuzzy form was undertaken for the observer design. The input-to-state stability (ISS) of the estimation errors was exploited using Lyapunov stability arguments to allow for more relaxation and an additional robustness guarantee with respect to the disturbance term of unmeasurable nonlinearities. For the design of the LPV interconnected UIO, sufficient conditions of the ISS property were formulated as an optimization problem in terms of linear matrix inequalities (LMIs), which can be effectively solved with numerical solvers. Extensive experiments were carried out under various driving test scenarios, both in interactive simulations performed with the well-known Sherpa dynamic driving simulator, and then using the LAMIH Twingo vehicle prototype, in order to highlight the effectiveness and the validity of the proposed observer design.

Adaptive unknown input observer-based detection and identification method for intelligent transportation under malicious attack

This paper aims at developing a novel detection and identification method against malicious attacks in intelligent transportation. Due to the development and applications of communication and advanced sensor technologies, intelligent transportation has faced new safety risks. In particular, the emerging malicious attacks, such as false data injection attack, can mask the destruction of physical dynamic by tampering with information in layer to fool the current detection methods. Because of this reason, an adaptive unknown input observer-based detection and identification method is developed. Firstly, a physical dynamics model of vehicle networking system is established by considering the actual physical state. Considering the spoofing characteristics of false data injection attack, an unknown input observer-based detection method is proposed. Through the design of adaptive unknown input observer parameters, the detection performance, can be improved by cutting down the state estimation error. Compared with the UIO-based detection method, simulations demonstrate that the false positive rate can be reduced 0.1%. Based on the feature of state residuals that is not sensitive to the attacked ith residual, but sensitive to other residuals, a novel identification criterion is developed. At last, simulation experiments on the Matlab verify the performance of the proposed detection and identification algorithm in intelligent transportation system.

Fault-tolerant control for an electro-hydraulic servo system with sensor fault compensation and disturbance rejection

The electro-hydraulic servo system (EHSS) usually faces multiple sensor faults and disturbances, which is difficult to achieve good tracking control, reliability, and stability control. In this article, an advanced fault-tolerant controller is proposed for an EHSS to deal with the above challenge. The three fault observers, called nonlinear unknown input observers (NUIOs), are developed to effectively estimate the position, velocity, pressure sensor faults and the system states. The fault detection, estimation, and isolation are then presented as effective for multiple sensors failure at a time. The first NUIO for position sensor fault is utilized for the tracking control, while the other NUIOs are used for alarm proposal. The adverse effects caused by the matched and unmatched disturbances are eliminated by two extended state observers (ESOs). In addition, to avoid the “explosion of complexity” when computing the derivatives of virtual control laws, the dynamic surface control is applied to design the fault-tolerant control (FTC) scheme. The Lyapunov principle ensures system stability under lumped disturbance and faulty conditions. Finally, simulation studies and evaluation results are performed to demonstrate the validity of the proposed FTC algorithm.

Fault detection and isolation for a secondary loop refrigeration system

Secondary loop refrigeration systems offer advantages over conventional system architectures: refrigerant charge reduction, maintenance simplification, and reduced safety hazards. Currently, no model-based fault detection for a secondary loop system has been reported. This article presents a fault detection and isolation scheme for a secondary loop refrigeration system where parametric faults (dirt/ice-buildup and secondary loop pump failure) and sensor faults (four temperature sensors in the secondary loop) are detected and isolated. The fault detection and isolation system is based on unknown input observers and on extended Kalman filters. The models are obtained through physical relations combined with experimental parameter determination (gray-box model) and are cross-validated against measurement data. The complete fault detection and isolation architecture is experimentally validated on a secondary loop refrigeration system mounted on a cooling box inside a climate chamber. Experimental results show that important parametric faults can be reliably detected and isolated, and the fault detection and isolation of sensor faults are effective.

Context‐aware trajectory prediction for autonomous driving in heterogeneous environments

AbstractThe prediction of surrounding agent trajectories in heterogeneous traffic environments remains a challenging task for autonomous driving due to several critical issues, such as understanding social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories in accordance with real‐world rules, all of which hinder prediction accuracy. To address these issues, a new multimodal trajectory prediction framework based on the transformer network is presented in this study. A hierarchical‐structured context‐aware module, inspired by human perceptual logic, is proposed to capture contextual information within the scene. An efficient linear global attention mechanism is also proposed to reduce the computation and memory load of the transformer framework. Additionally, this study introduces a novel auxiliary loss to penalize infeasible off‐road predictions. Empirical results on the Lyft l5kit data set demonstrate the state‐of‐the‐art performance of the proposed model, which substantially enhances the accuracy and feasibility of prediction outcomes. The proposed model also possesses a unique feature, effectively dealing with missing input observations. This study underscores the importance of comprehending social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories adhering to real‐world rules in autonomous driving.

An auxiliary approach to interval observer design for linear systems with disturbances

An auxiliary filtering system approach is adopted in this paper to treat the interval observer design for linear systems with state and output disturbances. By augmenting the filtering system along with the disturbances boundaries, interval observer is built under the unknown input observer framework. Relying on the Lyapunov stability method and H∞ technique, the existence of the interval observer is formulated into strict linear matrix inequalities instead of the combination of linear programming and linear matrix inequalities. Compared with the existing result, the proposed method could render tighter state intervals from simulation studies.

State and unknown input set-valued estimation for uncertain linear discrete-time systems

Set-valued algorithms are designed in this work to compute rigorous bounds of the unknown inputs of uncertain discrete-time linear systems. First, based on the classical full-order and reduced-order unknown input observers, non-conservative numerical schemes are proposed to characterize the reachable set of the estimation error. Then, by using these results, set-membership estimators of the unknown inputs are presented. Moreover, the results of these estimators can be merged together via set-intersection operators to obtain more accurate estimates.

Model-based shared control approach for a power wheelchair driving assistance system using a force feedback joystick

This paper presents a haptic-based assistance system (AS) for power wheelchair users designed using the model-based shared control approach. The idea is to combine robust control with a high-level driving supervisor in order to successfully share control authority between the wheelchair user and the assistance system. This shared control strategy is composed of two parts, namely an operational part and a tactical part. Through the haptic joystick interface, this assistance system aims to reduce user’s effort when manipulating the joystick, guide the user to avoid any potential collisions, and maintain the active participation of the user in driving the wheelchair. In the operational part, an optimal Takagi-Sugeno fuzzy control approach is proposed to deal with the time-varying user’s intention represented by his desired longitudinal and angular position errors and velocities and hand torques. The control design is formulated as an LMI optimization problem which can be easily solved with numerical solvers. Two unknown input observers for Takagi-Sugeno fuzzy model have been designed to estimate the user’s intention in order to generate an assistance torque via a haptic force feedback joystick. The control supervisor in the tactical part, aims to provide a decision-making signal which allows for the conflict management based on the user hand torque estimation. A specific algorithm has been developed to solve the conflict between the user’s desired actions and the suggestions from the assistance system to ensure the user remains the final decision-maker. Experimental results show the effectiveness and the validity of the proposed assistance system.

Observer-Based Optimal Control of a Quadplane with Active Wind Disturbance and Actuator Fault Rejection.

Hybrid aircraft configurations with combined cruise and vertical flight capabilities are increasingly being considered for unmanned aircraft and urban air mobility missions. To ensure the safety and autonomy of such missions, control challenges including fault tolerance and windy conditions must be addressed. This paper presents an observer-based optimal control approach for the active combined fault and wind disturbance rejection, with application to a quadplane unmanned aerial vehicle. The quadplane model is linearised for the longitudinal plane, vertical takeoff and landing and transition modes. Wind gusts are modelled using a Dryden turbulence model. An unknown input observer is first developed for the estimation of wind disturbance by defining an auxiliary variable that emulates body referenced accelerations. The approach is then extended to simultaneous rejection of intermittent elevator faults and wind disturbance velocities. Estimation error is mathematically proven to converge to zero, assuming a piecewise constant disturbance. A numerical simulation analysis demonstrates that for a typical quadplane flight profile at 100 m altitude, the observer-based wind gust and fault correction significantly enhances trajectory tracking accuracy compared to a linear quadratic regulator and to a H-infinity controller, which are both taken, without loss of generality, as benchmark controllers to be enhanced. This is done by adding wind and fault compensation terms to the controller with admissible control effort. The proposed observer is also shown to enhance accuracy and observer-based rejection of disturbances and faults compared to three alternative observers, based on output error integration, acceleration feedback and a sliding mode observer, respectively. The proposed approach is particularly efficient for the active rejection of actuator faults under windy conditions.

A Fuzzy Lyapunov Function Approach for Fault Estimation of T–S Fuzzy Fractional-Order Systems Based on Unknown Input Observer

This article discusses the fault estimation (FE) problem of nonlinear fractional-order (FO) systems subject to faults and unknown inputs through the T–S fuzzy approach. A novel fuzzy FO unknown input observer is well synthesized to not only achieve the desired FE but also decouple the unknown input in contrast to the traditional integer-order observer design technique. It is worth pointing out that the provided FE scheme can reconstruct the fault signal appearing in both input and output equations simultaneously. Furthermore, by resorting to linear matrix inequalities, the stability criteria of observation error dynamics are first formulated based on general quadratic Lyapunov functions, which are further extended via fuzzy Lyapunov functions containing the information of membership functions. Finally, the performance of theoretical results is verified through two elaborate examples.

Prescribed-Time Unknown Input Observers Design by Using Periodic Delayed Output With Application to Fault Estimation

This article considers the design of prescribed-time unknown input observers (PTUIOs) for linear systems, i.e., observers that estimate the state of a linear system with unknown inputs at a prescribed finite time. To this end, the generalized inverse is used first to transform a form amenable for observer design. Then, PTUIOs are designed by using the periodic delayed output so that their estimation errors converge to zero at a prescribed time. Both full-order and reduced-order PTUIOs are considered. For the reduced-order observer design, an alternative approach is also adopted, based on a transformation to a generalization of the normal form. In addition, the proposed PTUIOs are applied to estimate faults. Finally, two examples are given to illustrate the effectiveness of the proposed approaches.