Observer-based Control Research Articles

Fault-tolerant observer-based leader-following consensus control for LPV multi-agent systems using virtual actuators

This paper introduces a fault-tolerant consensus control approach for a continuous multi-agent system with actuator faults. The faults in the multi-agent system are modelled as additive and multiplicative actuator faults. The objective is to design a leader-following control for consensus-based Linear Parameter Varying (LPV) multi-agent systems. The proposed control approach employs a leader-following strategy, where the dynamics of the LPV virtual leader are defined. The synchronisation error between the followers and the virtual leader is considered, and a consensus control law is introduced. The fault-tolerant mechanism involves the design of a distributed LPV observer and an LPV virtual actuator. Fault-tolerance is achieved through the redistribution of control inputs based on the effectiveness of actuators and dynamic virtual actuators. The primary focus is on establishing Linear Matrix Inequalities (LMIs) conditions ensuring the existence of LPV consensus control, LPV observers, and LPV virtual actuator gains. To demonstrate the effectiveness of the proposed fault-tolerant control, simulation results considering a team of Unmanned Aerial Vehicles (UAVs) in formation under actuator faults are presented.

Pitch control for floating offshore wind turbines via model-dependent extended state observer-based active disturbance rejection control

Various types of disturbances bring significant challenges to the stable operation of floating offshore wind turbines. This paper formulates the pitch control of floating offshore wind turbines using a novel model-dependent extended state observer-based active disturbance rejection control, which can effectively estimate and compensate for total disturbances. Firstly, a Markov model is proposed to describe wind and wave combined external disturbances and the aerodynamic characteristics of floating offshore wind turbines. Then, an active disturbance rejection control is developed based on the constructed model-dependent extended state observer. The approach provided in this paper can adjust the pitch angle to ensure that the generator speed output tracks the desired reference value. Based on this, the multi verse optimization algorithm is introduced to optimize the parameters of the controller. Finally, the superiorities of the results presented in this paper are illustrated by a 5 MW floating offshore wind turbine model. Simulation results demonstrate that the model-dependent extended state observer-based active disturbance rejection control strategy can significantly adjust the pitch angle to guarantee the stable operation of floating offshore wind turbines under wind and wave combined external disturbances.

Observer-based self-triggered adaptive decentralized control for uncertain interconnected nonlinear systems

In this paper, an observer-based self-triggered adaptive decentralized control method is presented for uncertain interconnected nonlinear systems (UINS) with unmeasured states. First, an adaptive state observer is constructed to estimate unmeasured system states. Then, the observer-based self-triggered controller is developed by applying the famous backstepping technique, in which the controller signal is updated based on the well-designed self-triggered protocol. Moreover, parameter adaptive laws are designed to address unknown parameters of UINS. Lyapunov theory is employed to prove that all signals of the UINS are semi-globally uniformly ultimately bounded (SGUUB) and the Zeno behavior will not appear. Simulation results manifest the efficacy of the derived self-triggered control solution.

Multi-rate sampled-data observer-based networked load frequency control for multi-area interconnected power systems under multiple cyber-attacks

This paper addresses the problem of adaptive event-triggered load frequency control (LFC) for multi-rate sampling multi-area interconnected power systems with integrated renewable energy. Different network channels can be vulnerable to multiple cyber-attacks, including deception attacks and DoS attacks. First, a matching mechanism is constructed to fuse the multi-rate sampled data, and a set of observers based on multi-rate sampled data is designed to independently estimate the states of the individual power systems. A networked sampled-data PI controller is then developed by introducing a new synchronous sampling scheme and an adaptive event-triggered mechanism. A co-design approach is proposed that integrates decentralized PI controllers, observers, and adaptive event-triggered mechanisms to achieve exponential mean-square stability with a given H∞ performance level for the networked LFC system in the presence of multiple cyber-attacks and multi-rate sampling. Finally, the effectiveness of the proposed method is verified through an example of an interconnected power system.

Delay-dependent observer based control for uncertain fuzzy-chaotic systems with deception attacks and faulty actuators

In this study, the robust fault-tolerant control problem for T-S fuzzy chaotic systems is studied with the use of a proportional integral observer. Particularly, to accurately consider the real-world scenario, the chaotic system model incorporates the parameter uncertainties, input delay and external disruptions. Furthermore, a fuzzy-based observer is put forward based on analyzed system output with the intent of estimating the states of undertaken chaotic systems. Herein, the system output is susceptible to randomly occurring deception attacks and adheres to the Bernoulli distribution. Precisely, the consideration of deception attacks in the output channel allows for more secure state estimation in a networked setting. Subsequently, we develop a proportional integral observer-based fault-tolerant control that allows for the attainment of goals despite faults and delays in the input channel. Moreover, by setting up the Lyapunov-Krasovskii functional and blending it with Wirtinger's integral inequalities, we put together adequate conditions that ensure the asymptotic stability of the closed-loop systems by establishing conditions in the form of linear matrix inequalities. In the follow-up, based on the established matrix inequalities, the exact procedure for computing the gain matrices is outlined. As a last step, we put forward numerical simulation outcomes that exhibit the viability of the theoretical findings.

A dynamic event-triggered approach for observer-based formation control of multi-agent systems with designable inter-event time

This paper addresses the leader-following formation control problems for generic linear multi-agent systems under directed topology with designable inter-event time. A synthesis approach combining controller and observer design is developed under a dynamic event-triggered communication and control scheme. The proposed feedback control, state estimation, and event-triggered rules are distributed, and only local information is required for each agent to implement these algorithms. The proposed dynamic event-triggered protocol incorporates model-based estimation and clock-like auxiliary dynamic variables to prolong the inter-event time and economize the network resources. Furthermore, the inter-event time is designable, which allows more flexible tuning of communication frequency with only minor performance degradation. Sufficient conditions for formation control are established by linear matrix inequalities. The proposed method exhibits significant improvement over the dynamic event-triggered control methods described in the existing literature. Compared to the existing static event-triggered strategy, the proposed approach significantly reduces the utilization of communication resources while preserving asymptotic convergence to the desired formation. Comparative simulations demonstrate the validity and effectiveness of the proposed theoretical results.

Neural network prescribed-time observer-based output-feedback control for uncertain pure-feedback nonlinear systems

To prevent the complicated backstepping design, as well as the coupled design of state observer/disturbance approximator and baseline controller that makes the parameter synthesis difficult, this article proposes a neural network (NN) prescribed-time observer-based output-feedback control approach for uncertain pure-feedback nonlinear systems. After reformulating the original system into a canonical form with matched lumped disturbance, an adaptive NN prescribed-time observer (NNPTO) is developed to quickly provide accurate estimates of unknown states and disturbance. Then the observer is integrated with designed non-backstepping state-feedback controller to construct an output-feedback control scheme. Three features distinguish our approach from existing non-backstepping methods: (1) the accurate estimation is realized in a finite time prescribed through a single parameter; (2) the design of the state observer/disturbance approximator and baseline controller is decoupled, which eases the synthesis process and makes lots of existing non-backstepping state-feedback controllers compatible with our output-feedback control approach; (3) The NNPTO has trivial oscillation and small observation peaking errors under large disturbance, which effectively improves the output tracking performance. Numerical examples and an application example of a rigid single-link manipulator system are presented to demonstrate the effectiveness and practicability of our approach.

Transparency-enhanced observer-based adaptive backstepping torque–position control for teleoperation systems with position error constraint and time-varying delay

Nonlinear teleoperation systems are susceptible to significant problems, including transparency, safety, stability and external torques/forces measurement. In order to deal with these limitations, an observer-based adaptive backstepping torque–position control approach is proposed in this paper. In order to achieve this objective, a novel external torque observer is proposed to alleviate the limitations associated with the use of force sensors. This paper’s main idea is to employ a three-channel architecture using a force sensor-less reflecting signal, which can improve transparency and increase algorithm performance. Furthermore, the another point of this paper is to enhance the safeness of operations in the presence of time-varying delays in the communication channel, a position error constraint control strategy is employed in the core of the proposed approach. Moreover, the primary advantage of this paper is that, the proposed observer-based control algorithm is relieved from the joints acceleration measurement of the leader and follower manipulators which is difficult in robotic systems. Finally, the stability analysis of the controller and observer together is conducted by the Barrier Lyapunov functional and series of simulations, comparisons and practical experiments are performed to validate the performance of the proposed algorithm.

Disturbance observer-based control with hysteresis identification in piezoelectric deformable mirrors

Piezoelectric deformable mirrors are complex systems with a reflective face-sheet and underlying actuators. The inherent hysteresis phenomenon in piezoelectric materials introduces nonlinearity and delay, rendering conventional control methods inappropriate for deformable mirror actuation. This study presents a disturbance observer-based controller with the generalised Bouc–Wen hysteresis parameter identification algorithm. Analytical proofs establish fixed-time stability for the observer and controller. A key feature of the proposed disturbance observer is the convergence of the estimated hysteresis to the actual value after a prescribed settling time, which facilitates precise hysteresis parameter identification. This is based on a data clustering technique, unlike most previous works. The proposed controller has the capability of hysteresis compensation and reference tracking in the piezoelectric deformable mirror actuators. Numerical simulation results are present to evaluate the tracking performance of the controller in the presence of random wavefront, the convergence of disturbance to the actual value in the fixed time, and identification of hysteresis parameters.

Observer-based fully distributed bipartite consensus of multiagent systems with disturbance rejection

Observer-based output feedback control method is utilized to deal with the bipartite consensus problem of multiagent systems (MASs) suffering deterministic disturbances. Based on the leaderless and leader-follower methods, two fully distributed observer-based output feedback controllers are devised to guarantee the bipartite consensus of MASs. Moreover, due to the limited bandwidth of communication channels in practical systems, an event-triggered output feedback controller for the bipartite consensus of MASs is also developed and can guarantee that Zeno behavior does not occur. Finally, the effectiveness and advantages of the control protocols are verified via illustrate examples.

T–S Fuzzy Observer-Based Output Feedback Lateral Control of UGVs Using a Disturbance Observer

This paper introduces a novel observer-based fuzzy tracking controller that integrates disturbance estimation to improve state estimation and path tracking in the lateral control systems of Unmanned Ground Vehicles (UGVs). The design of the controller is based on linear matrix inequality (LMI) conditions derived from a Takagi–Sugeno fuzzy model and a relaxation technique that incorporates additional null terms. The state observer is developed to estimate both the vehicle’s state and external disturbances, such as road curvature. By incorporating the disturbance observer, the proposed approach effectively mitigates performance degradation caused by discrepancies between the system and observer dynamics. The simulation results, conducted in MATLAB and a commercial autonomous driving simulator, demonstrate that the proposed control method substantially enhances state estimation accuracy and improves the robustness of path tracking under varying conditions.

Refined Metamodel Disturbance Observer-Based Control for Coarse Pointing Assembly Under Constraints

The target tracking performance of the coarse pointing assembly (CPA) is a critical factor in determining the data transmission efficiency of the inter-satellite laser communication. However, under the influence of multiple disturbances, such as gimbal coupling torque, friction, and satellite-transmitted vibration, the angle tracking performance of the CPA can be decreased, which then affects the safety of the system in terms of angular velocity. To address the performance and safety requirements of the CPA, this paper proposes a refined metamodel disturbance observer-based state constraints controller. First, a refined metamodel disturbance observer is proposed to achieve accurate disturbance estimation by combining the data-driven state-space Kriging metamodeling method with a refined disturbance observer. Both disturbance numerical data and partially known information (e.g. vibration frequency and structure) are fully utilized to reduce estimation conservatism. Second, based on the observer, a state constraint controller is designed to ensure the angle tracking performance and angular velocity constraints. Finally, the effectiveness and robustness of the proposed control method are validated through numerical simulations, indicating an enhanced angle tracking performance compared to the traditional disturbance observer-based control method.

High-Order Disturbance Observer-Based Fuzzy Fixed-Time Safe Tracking Control for Uncertain Unmanned Helicopter with Partial State Constraints and Multisource Disturbances

In the real-world operation of unmanned helicopters, various state constraints, system uncertainties and multisource disturbances pose considerable risks to their safe fight. This paper focuses on anti-disturbance adaptive safety fixed-time control design for the uncertain unmanned helicopter subject to partial state constraints and multiple disturbances. Firstly, a developed safety protection algorithm is integrated with the fixed-time stability theory, which assures the tracking performance and guarantees that the partial states are always constrained within the time-varying safe range. Then, the compensation mechanism is developed to weaken the adverse impact induced by the filter errors. Simultaneously, the influence of the multisource disturbances on the system stability are weakened through the Ito^ differential equation and high-order disturbance observer. Further, the fuzzy logic system is constructed to approximate the system uncertainties caused by the sensor measurement errors and complex aerodynamic characteristics. Stability analysis proves that the controlled unmanned helicopter is semi-globally fixed-time stable in probability, and the state errors converge to a desired region of the origin. Finally, simulations are provided to illustrate the performance of the proposed scheme.

Flatness-based nonlinear internal model control with disturbances compensation via improved extended state observer for remotely operated vehicle

This paper proposes an improved Active Disturbance Rejection Control (ADRC) scheme for nonlinear control-affine systems, achieving superior robustness/ performance trade-offs. By leveraging the flatness property within a closed-loop structure, Nonlinear Internal Model Control (NLIMC) is employed as a model-based controller utilises partial plant knowledge to enhance control performance and eliminate static tracking errors. Subsequently, an improved Extended State Observer (IESO) is incorporated with NLIMC to address unmodelled dynamics, disturbances, and parameter uncertainties in addition to the full state's estimation. The IESO's added hyperbolic function dynamic ensures higher sensitivity and accuracy compared to conventional linear ESOs, especially in case of large errors. Control performances are evaluated through a Remotely Operated Vehicle (ROV) application. Numerical simulations have been carried out and the results confirm its effectiveness and superiority compared to Ocean Disturbance Observer-based Double-loop Sliding Mode Controller (ODSMC) and the linear ADRC.

Neural observer-based output feedback control for electrohydraulic cylinder systems with state constraints and input saturation

The output feedback control for electrohydraulic cylinder systems with state constraints and input saturation is investigated in this paper. For the unmeasured state information, a neural observer is constructed to estimate the velocity and acceleration of electrohydraulic cylinders. Considering the physical limitations of electrohydraulic cylinder systems, the time-varying barrier Lyapunov function is employed to guarantee that the system states are confined within the preset region. In addition, a dynamic auxiliary system is designed to deal with the issue of input saturation. On this basis, an output feedback controller is presented to realize the displacement tracking of the reference trajectory by employing the dynamic surface control (DSC) technique. Theoretical analysis demonstrates that the proposed control algorithm can ensure that the control system remains semi-globally uniformly ultimately bounded (SGUUB). The simulation results also showcase the effectiveness of the proposed approach.

Observer-Based Event-Triggered Fuzzy Control for Switched Multi-Agent Systems with State Constraints and Sensor Faults

In this paper, we consider the output feedback event-triggered tracking control problem for a class of switched nonlinear multi-agent systems (MASs) with sensor faults and state constraints. For unknown nonlinear functions in the system, fuzzy logic systems are used to approximate them. To address sensor faults, sensor error compensation coefficients are designed to compensate for the errors caused by sensor faults. Meanwhile, state observer is designed to estimate the unmeasurable states in the system, providing necessary information for control design. Based on directed switching networks containing a directed spanning tree, an event-triggered output feedback controller is designed to enable the MAS to track the leader agent. By constructing a suitable barrier Lyapunov function, the stability of the system is analyzed, and it is proved that the consensus tracking can be achieved without violating the state constraints, the uniform boundedness of all the closed-loop system signals is ensured and the Zeno behavior can be eliminated. Finally, the effectiveness of the proposed algorithm is verified through a simulation example.

Observer-based fuzzy T–S control with an estimation error guarantee for MPPT of a photovoltaic battery charger in partial shade conditions

To improve the efficiency and performance of a photovoltaic system (PV) an observer-based fuzzy controller design methodology is provided in the study. The desired controller is achieved by employing a combination of linear matrix inequalities (LMIs). The system consists of a photovoltaic generator (PVG) connected to a boost converter. A battery is linked to the boost converter to stock additional energy for further use. A fuzzy controller based on a T–S fuzzy type observer that guarantees a predefined L2 performance is suggested to achieve maximum power point tracking (MPPT) even under changing weather conditions. An optimal trajectory should be tracked to ensure maximum power operation. For this aim, a specific reference fuzzy model is proposed to create the aimed trajectories. Using this method, the system dynamics are precisely reproduced over a large range of operations. The whole T–S fuzzy methodology, suggested in this paper, aims to ensure the most efficient energy recovery to recharge a battery under partially shaded conditions, resulting in high system efficiency. The proposed method is simulated with MATLAB/SIMULINK and the simulation results, with realistic reference trajectories, are driven while taking into account climate variations. The analysis of these simulations, along with a comparison with two other commonly used approaches, led to the conclusion that the suggested strategy succeeded in reducing the tracking time, as well as eliminating the oscillation that often occurs around maximum power point (MPP).

Observer-Based Asynchronous Boundary Stabilization for Stochastic Markovian Reaction-Diffusion Neural Networks.

This work investigates the observer-based asynchronous boundary stabilization for a kind of stochastic Markovian reaction-diffusion neural networks with exogenous disturbances. Specifically, parameter uncertainties are considered in the drift item. First, a hidden Markov model is introduced that guarantees the observer modes run asynchronously with the system modes. It should be noted that the asynchronous observer constructed in this work only uses the boundary measurement information. Then a nonfragile asynchronous observer-based boundary controller is designed. Taking advantage of inequality techniques and stochastic analysis method, sufficient criterion is provided to satisfy input-to-state exponentially mean-square stability, and the asynchronous boundary observer/controller gains are further derived. As a special case, the synchronous observer-based boundary stabilization is also obtained. Finally, a numerical example is exploited to manifest the validity of the established results.

Adaptive finite-time extended state observer-based model predictive control with Flatness motivated trajectory planning for 5-DOF tower cranes

This paper introduces a new method to control a 5-DOF tower crane (3DTC). By considering the 3DTC as a flat system, a time-optimal trajectory is proposed for the payload. System states and control signal references can be calculated based on the flatness theory. In addition, the 3DTC works in an environment containing many factors impacting control performance and the system states are hard to measure. An adaptive finite-time extended state observer (AFT-ESO) is introduced to solve these problems. With AFT-ESO, system states and lumped disturbances can be estimated accurately, facilitating the prediction for Lyapunov-based model predictive control (LMPC) when an accurate model is required. The LMPC takes advance of the second-order sliding mode control stability conditions as a strict constraint to guarantee the global stabilization of the closed-loop system. Finally, simulations based on the quasi-physical model are proposed to show the effectiveness and robustness of the proposed strategy.

Fuzzy Observer-Based Finite-Time Adaptive Formation Control for Multiple QUAVs With Malicious Attacks

Fuzzy Observer-Based Finite-Time Adaptive Formation Control for Multiple QUAVs With Malicious Attacks