Velocity-Free State Feedback Fault-Tolerant Control for Satellite with Actuator and Sensor Faults

A velocity-free state feedback fault-tolerant control approach is proposed for the rigid satellite attitude stabilization problem subject to velocity-free measurements and actuator and sensor faults. First, multiplicative faults and additive faults are considered in the actuator and the sensor. The faults and system states are extended into a new augmented vector. Then, an improved sliding mode observer based on the augmented vector is presented to estimate unknown system states and actuator and sensor faults simultaneously. Next, a velocity-free state feedback attitude controller is designed based on the information from the observer. The controller compensates for the effects of actuator and sensor faults and asymptotically stabilizes the attitude. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.

Fully Distributed Control for a Class of Uncertain Multi-Agent Systems with a Directed Topology and Unknown State-Dependent Control Coefficients

To address the control of uncertain multi-agent systems (MAS) with completely unknown system nonlinearities and unknown control coefficients, a global consensus method is proposed by constructing novel filters and barrier function-based distributed controllers. The main contributions are as follows. Firstly, a novel two-order filter is designed for each agent to produce informational estimates from the leader, such that a connectivity matrix is not used in the controller's design, solving the difficultly caused by the time-varying control coefficients in a MAS with a directed graph. Secondly, combined with the novel filters, barrier functions are used to construct the distributed controller to deal with the completely unknown system nonlinearities, resulting in the global consensus of the MAS. Finally, it is rigorously proved that the consensus of the MAS is achieved while guaranteeing the prescribed tracking-error performance. Two examples are given to verify the effectiveness of the proposed method, in which the simulation results demonstrate the claims.

Adaptive Fixed-Time Neural Network Tracking Control of Nonlinear Interconnected Systems

In this article, a novel adaptive fixed-time neural network tracking control scheme for nonlinear interconnected systems is proposed. An adaptive backstepping technique is used to address unknown system uncertainties in the fixed-time settings. Neural networks are used to identify the unknown uncertainties. The study shows that, under the proposed control scheme, each state in the system can converge into small regions near zero with fixed-time convergence time via Lyapunov stability analysis. Finally, the simulation example is presented to demonstrate the effectiveness of the proposed approach. A step-by-step procedure for engineers in industry process applications is proposed.

Modelling and Parameter Identification for a Flexible Rotor With Periodic Impacts

The ability of a multibody dynamics model to accurately predict the behavior of a real system depends heavily on the correct choice of model parameters. The identification of unknown system parameters, which cannot be directly computed or measured is usually time consuming and costly. If experimental measurement data of the real system is available, the parameters in the mathematical model can be determined by minimizing the error between the model response and the measurement data. The latter task can be solved by means of optimization. While many optimization methods are available, optimization with a genetic algorithm is a promising approach for searching optimal solutions for complex engineering problems, as reported in a paper of one of the authors. So far, however, there is no general approach how to apply genetic optimization algorithms for complex multibody system dynamics models in order to obtain unknown parameters automatically — which is however of great importance when dealing with real flexible multibody systems. In the present paper we present a methodology to determine several unknown system parameters applied to a flexible rotor system which is excited with periodic impacts. Experiments were performed on the physical system to obtain measurement data which is used to identify the impact force as well as the support stiffnesses of the rotor system using genetic optimization.

Why Mixture?

From the most known Gaussian mixture to the cutting-edge multi-Bernoulli mixture of various forms, mixture offers a fundamental means to deal with uncertainties, which has led to a variety of appealing applications in the state estimation realm based on a single sensor or a sensor network. Like noise is often used to model unknown system input, one may use various hypotheses to deal with the uncertain state space model or data association. Meanwhile, consensus may be sought over the cross-correlated sensors. These all drive a need for representing the probability distribution by a mixture of properly weighted component distributions, which fuse the information gained from different models/hypotheses or from different sensors. This technical note presents information-theoretical results which answer how the averaging/mixture approach makes sense and how the fusing weights should be designed.

Why Mixture?

From the most known Gaussian mixture to the cutting-edge multi-Bernoulli mixture of various forms, mixture offers a fundamental means to deal with uncertainties, which has led to a variety of appealing applications in the state estimation realm based on a single sensor or a sensor network. Like noise is often used to model unknown system input, one may use various hypotheses to deal with the uncertain state space model or data association. Meanwhile, consensus may be sought over the cross-correlated sensors. These all drive a need for representing the probability distribution by a mixture of properly weighted component distributions, which fuse the information gained from different models/hypotheses or from different sensors. This technical note presents information-theoretical results which answer how the averaging/mixture approach makes sense and how the fusing weights should be designed.