Adaptive Observer Research Articles

Multiple Transformation Matrix-Based Adaptive Projective Vortex Formation Tracking for Multiagent Systems With a Leader of Completely Unknown Input.

This article systematically studies projective vortex formation tracking (PVFT) of linear multiagent systems (MASs) on directed graphs through multiple transformation matrices, in which the input of leader and its upper bound information are not available to any follower. First, an innovative class of distributed adaptive observer is designed using the projection matrices to capture multiple desired virtual signals of the leader. Next, a novel kind of distributed PVFT protocol based on distributed observer, local observer and coordinates coupling matrices are proposed. Two different adaptive update mechanisms and nonlinear functions are introduced in the observer and controller to override the unknown input of the leader. Then, an algorithm is given, and the protocol under the algorithm is proved from three processes of estimation, aggregation, and rotation to enable linear systems to achieve PVFT. Finally, the effects of parameters on aggregation and rotation are analyzed systematically, and several simulation examples are given to illustrate the reliability of the results.

Adaptive Observer Design for Sensorless IPMSM Drives With Known Regressors Variant of Extended EMF Model

Adaptive Observer Design for Sensorless IPMSM Drives With Known Regressors Variant of Extended EMF Model

Reduced-Order Adaptive Observer With Asymptotic Stability for Sensorless Control of SPMSM

Reduced-Order Adaptive Observer With Asymptotic Stability for Sensorless Control of SPMSM

Torque Ripple Mitigation in Sensorless PMBLDC Motor Drive with Adaptive Observer for LEV

Torque Ripple Mitigation in Sensorless PMBLDC Motor Drive with Adaptive Observer for LEV

Finite-dimensional adaptive observer design for linear parabolic systems

A new finite-dimensional adaptive observer is proposed for some linear parabolic systems. The observer is based on the modal decomposition approach and uses a classical persistent excitation condition to ensure practical exponential convergence of both states and parameters estimation.

Lyapunov-Based Long Short-Term Memory (Lb-LSTM) Neural Network-Based Adaptive Observer

Lyapunov-Based Long Short-Term Memory (Lb-LSTM) Neural Network-Based Adaptive Observer

Data-Driven H∞ Output Consensus for Heterogeneous Multiagent Systems Under Switching Topology via Reinforcement Learning.

In this article, a novel model-free policy gradient reinforcement learning algorithm is proposed to solve the H∞ tracking problem for discrete-time heterogeneous multiagent systems with external disturbances over switching topology. The dynamics of the followers and the leader are unknown, and the leader's information is missing for each agent due to the switching topology. Therefore, a distributed adaptive observer is introduced to learn the leader's dynamic model and estimate its state for each agent. For the H∞ tracking problem, an exponential discount value function is established and the related discrete-time game algebraic Riccati equation (DTGARE) is derived, which is the key to obtaining the control strategy. Furthermore, a data-based policy gradient algorithm is proposed to approximate the solution of the GAREs online and the utilization of agents' accurate knowledge is avoided. To improve the efficiency of data utilization, an offline dataset and the experience replay scheme are used. In addition, the lower bound of the exponential discount value is explored to ensure the stability of the systems. In the end, a simulation is provided to show the validity of the proposed method.

Improve methanol efficiency for direct methanol fuel cell system via investigation and control of optimal operating methanol concentration

This study investigates and controls the optimal operating methanol concentration for direct methanol fuel cell (DMFC) system, which effectively improves the efficiency of DMFC system. Firstly, a DMFC output characteristic model and a nonlinear stack methanol concentration model were established. Moreover, an adaptive observer was developed to reconstruct unmeasurable stack methanol concentration to estimate methanol concentration. Then, in order to achieve the minimum methanol consumption under required power, the reference methanol concentration was determined by analyzing DMFC system with its maximum energy conversion efficiency. Finally, the sliding mode algorithm was adopted to track reference optimal methanol concentration. The estimated performance of adaptive observer and non-adaptive observer for methanol concentration was compared through experimental bench data. Furthermore, the methanol consumption and energy conversion efficiency under proposed optimal methanol concentration strategy and fixed methanol concentration were compared. The comprehensive result demonstrates that the adaptive observer has better performance than non-adaptive observer in terms of estimated performance. Moreover, the proposed strategy for optimal methanol concentration was compared with fixed methanol concentration of 0.3 mol/L and 0.4 mol/L, and the fuel consumption of DMFC system was reduced by 7.9% and 30.9% respectively.

Adaptive output feedback event-triggered consensus control for networked reaction-diffusion PDE systems

This paper explores the adaptive output feedback event-triggered consensus control of a network of parabolic systems modelled by partial differential equations (PDEs). An infinite-dimensional state observer is established to estimate the state based on the relative output information. On the basis of the adaptive state observer, two novel distributed event-triggered controllers are proposed to address the leaderless consensus and leader-follower consensus problem. Based on the Lyapunov method and PDE theory, it is shown that the asymptotic consensus control can be achieved. Compared to the related works, a distinctive feature of the designed event-triggered consensus protocols is fully distributed which does not demand any global information of the graph. In addition, the absence of Zeno behaviour is demonstrated. Lastly, the obtained results are demonstrated using two simulations.

Rotation matrix-based finite-time trajectory tracking control of AUV with output constraints and input quantization

This study investigated a rotation-matrix-based finite-time trajectory tracking problem for autonomous underwater vehicles (AUVs) in the presence of output constraints, input quantization, and uncertainties. First, a rotation matrix-based attitude representation is introduced, which allow attitude dynamics to be globally and uniquely represented without unwinding. To satisfy the finite-time stability of AUV tracking control and the output constraints imposed by introducing the new attitude error vector, a novel finite-time command-filtered backstepping controller (FTCFBC) is proposed based on the asymmetrical time-varying barrier Lyapunov function (TVBLF). Subsequently, a second-order auxiliary dynamic system (ADS) is proposed to estimate the negative effects that of input quantization errors. Because the quantized control inputs can be switched at an appropriate earlier or later switching timing depending on the output of the ADS, the negative impact of quantization errors on the control accuracy is reduced. Moreover, an adaptive finite-time disturbance observer (AFTDO) is developed to estimate the lumped uncertainties without prior information on the bounds of the uncertainties. Finally, the results of the theoretical analysis verified that the tracking errors can converge within a finite time. Numerical simulations confirmed the effectiveness of the proposed control scheme.

STA-based design of an adaptive disturbance observer for autonomous underwater vehicles: From concept to real-time validation

In this paper, we propose to improve the robustness of the well-known PD controller through an adaptable disturbance observer. The proposed disturbance observer is based on the Super-Twisting Algorithm, and is designed with adaptive gains. The stability analysis of the resulting closed-loop controller/observer is achieved using Lyapunov arguments. Real-time experiments in different operating conditions are conducted to demonstrate the effectiveness and robustness of the proposed methodology, compared to the nominal PD control.

Model-based diagnosis for sequential shunt faults in HVDC transmission lines

This work proposes a model-based diagnosis for detecting and locating sequential conductance deteriorations faults on-line in monopolar high-voltage direct current (HVDC) transmission lines, if only measurements are available at the ends of the line. The basis of the diagnosis algorithm is a recursive procedure of a distributed adaptive observer scheme named the shunt fault estimator scheme (SFES), which monitors the line by identifying and locating any single shunt. By considering the equivalence between the transmission line model with one and two faults, two outcomes are shown: (1) the lack of isolability in steady-state for two simultaneous shunt faults and (2) the ability of the SFES to solve the two faults’ scenario recursively if the shunt faults occur sequentially. Thus, by using the equivalence relationships between the parameters for one and two faults, a location algorithm can be designed through the SFES that provides accurate and fast parameter estimations for the two faults that can be applied on-line. Its performance is studied with numerical simulations and with synthetic data obtained from a deteriorated line 300×103 [m] long. The location time in this case was fewer than approximately 4 [ms] that displays the algorithm potentiality in a practical diagnosis scenario.

Fault estimation for persistent dwell time switched systems with actuator and sensor faults

This paper investigates the fault estimation (FE) problem for a class of switched systems with external disturbances, actuator faults and sensor faults. A new adaptive FE observer is proposed to estimate the sensor and actuator faults simultaneously. Meanwhile, a more general and flexible persistent dwell time (PDT) switching signal is utilized to depict slow switching and fast switching phenomenon of switched systems. Then, sufficient conditions can be achieved to guarantee the stability of the estimation error dynamics. Finally, a simulation example of the circuit system is presented to show the effectiveness of the proposed FE scheme.

Fixed-time fault-tolerant attitude control for flexible spacecraft without angular velocity sensor

This paper deals with the appealing problem of fixed-time fault-tolerant attitude control, using attitude-information-only, for a flexible spacecraft under the influence of inertial parametric variations, external disturbances, and multiple actuator faults while suppressing the flexible appendages’ vibrations without using additional sensors or smart vibration suppression actuators. First, an adaptive fixed-time model-free observer (AFTMFO) is designed, using the attitude information, for rapid estimation of unavailable angular velocity. A novel adaptive continuous control is then synthesized based on an anti-unwinding fast fixed-time nonsingular sliding surface (AFFTNSS), utilizing variable gains in both the control law and sliding surface; that simultaneously alleviates the chattering but also improves the convergence speed when compared to existing fixed-time approaches. The proposed scheme offers superior performance characteristics such as velocity sensor-free fixed-time attitude maneuvering with high pointing accuracy, fault tolerance, vibration suppression, nonsingular and chattering-free control. The spacecraft can carry out the coveted control objective in a predeterminable time independent of the knowledge of initial states while overcoming the unwinding effect to reduce the control effort and time. The fixed-time closed-loop stability of the proposed scheme is corroborated via Lyapunov techniques. Finally, a comparative simulation analysis with the existing results elucidated the proposed scheme’s efficacy.

Adaptive backstepping-extended state observer control for electrohydraulic position servo systems with multiple disturbances using the compound friction compensation

Multiple disturbances coming from friction, matched, and mismatched uncertainties make it difficult for electrohydraulic servosystems to obtain the satisfactory position-tracking performance. The existing adaptive backstepping controllers fail to effectively distinguish the difference of disturbance between the mechanical subsystem and the hydraulic subsystem, which limits the compensation effect of multiple disturbances, especially for friction nonlinearity. Therefore, the adaptive backstepping-extended state observer position-tracking controller combined with compound friction compensation is proposed to simultaneously compensate for the fast-varying friction disturbance of a mechanical subsystem and the slow-varying matched disturbance of a hydraulic subsystem. The extended state observer algorithm is integrated into the adaptive backstepping controller to suppress the null bias of the average tracking error. The compound friction compensation includes a LuGre model-based compensation and a high-order disturbance observer, which can improve the tracking performance of system and avoids the excessive gain of observer. A large number of comparative experiments are conducted to verify the effective of the proposed controller.

A Composite Super-Twisting Sliding Mode Approach for Platform Motion Suppression and Power Regulation of Floating Offshore Wind Turbine

The floating platform motion of an offshore wind turbine system can exacerbate output power fluctuations and increase fatigue loads. This paper proposes a new scheme based on a fast second-order sliding mode (SOSM) control and an adaptive super-twisting extended state observer to suppress the platform motion and power fluctuation. Firstly, an affine nonlinear model of the floating wind turbine pitch system is constructed. Then, a fast SOSM pitch control law is adopted to adjust the blade pitch angle, and a new adaptive super-twisting extended state observer is constructed to achieve total disturbance observation. Finally, simulations are conducted under two cases of wind and wave conditions based on FAST (fatigue, aerodynamics, structures, and turbulence) and MATLAB/Simulink. Compared with the traditional proportional integral (PI) control scheme and standard super-twisting control scheme, the platform roll under the proposed scheme is reduced by 13% and 4%, and pitch is reduced by 16% and 3% in Case 1. Correspondingly, the roll is reduced by 9% and 15%, and pitch is reduced by 7% and 1% in Case 2. For the tower top pitch and yaw moment, load reductions of 7% and 3% or more are achievable compared with those under the PI control scheme. It is indicated that the proposed scheme is more effective in suppressing floating platform motion, stabilizing output power of the wind turbine system, and reducing tower loads.

Model Reference Adaptive Observer for Permanent Magnet Synchronous Motors Based on Improved Linear Dead-Time Compensation

Model Reference Adaptive Observer for Permanent Magnet Synchronous Motors Based on Improved Linear Dead-Time Compensation

Robust adaptive tracking control for dynamic positioning ships subject to dynamic safety constraints and actuator saturation

This paper proposed a novel robust adaptive trajectory tracking control scheme for dynamic positioning (DP) ships subjected to unknown time-varying disturbances, unknown model parameters, dynamic safety constraints, and actuator saturation. The dynamic safety constraints were guaranteed based on the tan-type barrier Lyapunov function (BLF) technique, which ensured the position and velocity of the DP vessel within the given constraints. Moreover, the radial basis function neural network (RBFNN) was adopted to approximate the unknown parameters of the dynamics model, and an adaptive neural disturbance observer (ANDO) was designed to compensate for lumped disturbances. An anti-windup system was developed to handle the actuator saturation effect. By combining the above techniques and back-stepping method, the final control law was presented, and the semi-global asymptotic convergence was achieved via rigorous theoretical analysis and Lyapunov proof. The results of simulations demonstrated the superiority and robustness of this approach in resolving the trajectory tracking challenges of DP ships. Moreover, the proposed control scheme is highly effective in optimizing the trajectory tracking of DP ships, as it takes into account the multiple constraints that may arise during the process, which is a valuable contribution to the field of DP ship trajectory tracking, providing a more comprehensive and effective solution to this complex problem.

Frequency Adaptive Disturbance Observer for Sensorless Control of a 3 × 3-Phase PMA-SynRM Driven by Mono-Inverter

Frequency Adaptive Disturbance Observer for Sensorless Control of a 3 × 3-Phase PMA-SynRM Driven by Mono-Inverter

Anti-Attack Event-Triggered Control for Nonlinear Multi-Agent Systems With Input Quantization.

In this article, an anti-attack event-triggered secure control scheme for a class of nonlinear multi-agent systems with input quantization is developed. With the help of neural networks approximating unknown nonlinear functions, unknown states are obtained by designing an adaptive neural state observer. Then, a relative threshold event-triggered control strategy is introduced to save communication resources including network bandwidth and computational capabilities. Furthermore, a quantizer is employed to provide sufficient accuracy under the requirement of a low transmission rate, which is represented by the so-called a hysteresis quantizer. Meanwhile, to resist attacks in the multi-agent network, a predictor is designed to record whether an edge is attacked or not. Through the Lyapunov analysis, the proposed secure control protocol can ensure that all the closed-loop signals remain bounded under attacks. Finally, the effectiveness of the designed scheme is verified by simulation results.