Anti-disturbance dynamic inversion backstepping control for uncertain pure-feedback systems via multiple extended state observers

Most extant control designs for uncertain pure-feedback systems are based on backstepping procedure or dynamic surface control, requiring repeated calculation or approximation of the derivatives of the virtual control action. The fuzzy logic systems or neural networks used to cope with unknown dynamics also inherently introduce excess computation burden and sluggish convergence. In view of these, this paper provides a novel backstepping approach by combining extended state observers with dynamic inversion controllers. With high gain properties both on the observers and controllers, the resulting closed-loop system presents relatively fast convergence. By using dynamic inversion backstepping, the explosion of complexity problem that restricts the applicability of backstepping-like control methods, which are representatively employed to the control of pure-feedback systems, is entirely surmounted without resorting to filtering. The theoretical analysis of stability shows the closed-loop system has adjustable tracking performance. Finally, the efficiency of the proposed method is illustrated by comparative simulations.     

Robust extended fractional Kalman filter for nonlinear fractional system with missing measurements

Accurate and effective state estimation is essential for nonlinear fractional system, since it can provide some vital operation information about the system. However, inevitably missing measurements and additive uncertainty in the gain will affect the performance of estimation result. Thus, in this paper, in order to deal with these problems, a novel robust extended fractional Kalman filter (REFKF) is developed for states estimation of nonlinear fractional system, by which the states can be estimated accurately even with missing measurements. Finally, simulation results are provided to demonstrate that the proposed method can achieve much better estimation performance than the conventional extended fractional Kalman filter (EFKF).     

Fixed-time neuroadaptive practical tracking control based on extended state/disturbance observer for a QUAV with external disturbances and time-varying parameters

This paper investigates the fixed-time neural network adaptive (FNNA) tracking control of a quadrotor unmanned aerial vehicle (QUAV) to achieve flight safety and high efficiency. By combining radial basis function neural network (RBFNN) with fixed time adaptive sliding mode algorithm, a novel radial basis function neural network adaptive law is proposed. In addition, an extended state/disturbance observer (ESDO) is proposed to solve the problem of unmeasurable state and external interference, which can obtain reliable state feedback and interference input. Unlike most other ESO applications, this paper does not set the uncertainty model and external disturbances as total disturbances. Instead, the external disturbances are observed by extending the states and the observed states are fed back to the controller to cancel the disturbances. In view of the time-varying resistance coefficient and inertia torque in the QUAV model, the neural network is introduced so that the controller does not need to consider these nonlinear uncertainties. Finally, a numerical example is given to verify the effectiveness of the coupled non-simplified QUAV model.     

Design of observers for a class of discrete-time uncertain nonlinear systems with time delay

This paper is concerned with the problem of observer design for a class of discrete-time Lipschitz nonlinear state delayed systems with, or without parameter uncertainty. The nonlinearities are assumed to appear in both the state and measured output equations. For both the cases with and without norm-bounded time-varying parameter uncertainties, a design method is proposed, which involves solving a linear matrix inequality (LMI). When a certain LMI is satisfied, the explicit expression of a desired nonlinear observer is also presented. An example is provided to demonstrate the applicability of the proposed approach.     

A general motion control framework for an autonomous underwater vehicle through deep reinforcement learning and disturbance observers

This paper investigates the application of deep reinforcement learning (RL) in the motion control for an autonomous underwater vehicle (AUV), and proposes a novel general motion control framework which separates training and deployment. Firstly, the state space, action space, and reward function are customized under the condition of ensuring generality for various motion control tasks. Next, in order to efficiently learn the optimal motion control policy in the case that the AUV model is imprecise and there are unknown external disturbances, a virtual AUV model composed of the known and determined items of an actual AUV is put forward and a simulation training method is developed on this basis. Then, in the given deployment method, three independent extended state observers (ESOs) are designed to deal with the unknown items in different directions, and the final controller is obtained by compensating the estimated value of ESOs into the output of the optimal motion control policy obtained through simulation training. Finally, soft actor-critic is chosen as deep RL algorithm of the framework, and the generality and effectiveness of the proposed method are verified in four different AUV motion control tasks.     

Performance degradation due to measurement noise in control systems with disturbance observers and saturating actuators

Augmenting feedback control systems with disturbance observer (DOB) is a widely used technique in system design to compensate for the effect of exogenous disturbances as well as plant model uncertainties. In practice, actuator saturation should be taken into account in the design of control systems with DOB. In such cases, we have observed performance degradation due to zero mean measurement noise in the form of tracking loss. This phenomenon has never been reported in DOB literature. This paper reports the phenomenon, analyzes the conditions under which the tracking loss occurs, and also presents design guidelines to avoid the tracking loss. Experimental verification is also provided using a BLDC motor drive testbed.     

High-gain fractional disturbance observer control of uncertain dynamical systems

Disturbance observer-based control allows to compensate unknown inputs, however, in most cases, requiring their integer-order differentiability. In this paper, a novel disturbance observer-based state feedback controller is proposed to compensate a more general class of fractional-, but not necessarily integer-order, differentiable unknown inputs. The proposed fractional PI-like structure yields precise conditions for feedback gain tuning. Remarkably, the resulting controller rejects non-differentiable disturbances with a smooth controller, guaranteeing robustness, an outstanding features for tracking tasks, under a prescribed practical stability regimen. A comparison to a fractional sliding mode observer is conducted via simulations to highlight the reliability of the proposed scheme.     

Robust stability analysis for a class of fractional order systems with uncertain parameters

The research of robust stability for fractional order linear time-invariant (FO-LTI) interval systems with uncertain parameters has become a hot issue. In this paper, it is the first time to consider robust stability of uncertain parameters FO-LTI interval systems, which have deterministic linear coupling relationship between fractional order and other model parameters. Linear matrix inequalities (LMI) methods are used, and a criterion for checking asymptotical stability of this class of systems is presented. One numerical illustrative example is given to verify the correctness of the conclusions.     

Continuous finite-time extended state observer design for electro-hydraulic systems

This paper presents a novel framework towards a time-varying observer design for nonlinear electro-hydraulic actuators. The key idea of this paper is to employ a positive-increasing function associated with the observer objective to improve the estimation performance. An extended state observer is designed to estimate the full state variables and the uncertainties without any knowledge about the upper bounds of the uncertainties and their derivatives. First, without loss of generality, the system model is divided into three parts, and the extended state observers are designed for each part, independently. Then, the time-varying gains of each observer are designed to make the observation errors uniformly bounded. Finally, the simulated performance of the presented framework is compared with two valid approaches including high-order sliding mode and high-gain extended state observers.     

Distributed state estimator-based consensus tracking of multi-agent systems with exogenous disturbance

The consensus tacking problem for multi-agent systems with a leader of none control input and unknown control input is studied in this paper. By virtue of the relative state information of neighboring agents, state estimator and disturbance estimator are designed for each follower to estimate the system states and exogenous disturbance, respectively. Meanwhile, a novel control protocol based on two estimators is designed to make tracking error eventually converge to zero. Furthermore, the obtained results are further extended to the leader with unknown control input. A novel state estimator with adaptive time-varying gain is proposed such that consensus tracking condition is independent of the Laplacian matrix with regard to the communication topology. Finally, two examples are presented to verify the feasibility of the proposed control protocol.     

Sufficient and necessary conditions for stabilizing singular fractional order systems with partially measurable state

This paper is concerned with the stabilization problem of singular fractional order systems with order α?∈?(0, 2). In addition to the sufficient and necessary condition for observer based control, a sufficient and necessary condition for output feedback control is proposed by adopting matrix variable decoupling technique. The developed results are more general and efficient than the existing works, especially for the output feedback case. Finally, two illustrative examples are given to verify the effectiveness and potential of the proposed approaches.     

Active disturbance rejection control for electric power steering system with assist motor variable mode

The steering torque of automobile EPS steering system is significant for driving steering control and good driving feel. Servo motor control and external interference moment are the core factors affecting EPS steering system. With the advancement of automotive technology, the requirements of EPS control technology have been gradually improved, and the driving and handling of vehicles at high speed have become the key issues. For the current EPS steering system at high speed vibration and steering feel, active disturbance rejection EPS torque control method is proposed, EPS variable mode controller was developed. The control of the variable mode is verified by experiment and the vibration torque from the road is controlled, determine the control frequency of 30 KHz, the amount of current fluctuation is the smallest. The ADRC (active disturbance rejection controller) technology is used to suppress the interference of the road surface, finally, the validity of active immunity is verified by bench test. Steering wheel vibration torque can be reduced by an average of 28.5% to 33.3%.     

Adaptive disturbance attenuation for a class of uncertain nonlinear systems: A double-gain nonlinear observer method

This paper is concerned with the problem of adaptive disturbance attenuation for a class of nonlinear systems. The traditional adaptive methods are almost impossible to compensate the time-varying unknown disturbance by designing parameter adaptive laws without a priori knowledge about the bounds of external disturbances. To solve the problem, a new strategy is proposed by constructing an augmented system where the external disturbance is considered as another component of the augmented state vector. Based on this, a double-gain nonlinear observer is employed to estimate the state of the augmented nonlinear system. Further, an output feedback control strategy is designed, and it is proved that the proposed strategy ensures that all the signals are bounded and the tracking error exponentially converges to an adjustable compact set. Finally, an example is performed to demonstrate the validity of the proposed scheme.     

Parameter fault detection and estimation of a class of nonlinear systems using observers

The focus of this paper is on the detection and estimation of parameter faults in nonlinear systems with nonlinear fault distribution functions. The novelty of this contribution is that it handles the nonlinear fault distribution function; since such a fault distribution function depends not only on the inputs and outputs of the system but also on unmeasured states, it causes additional complexity in fault estimation. The proposed detection and estimation tool is based on the adaptive observer technique. Under the Lipschitz condition, a fault detection observer and adaptive diagnosis observer are proposed. Then, relaxation of the Lipschitz requirement is proposed and the necessary modification to the diagnostic tool is presented. Finally, the example of a one-wheel model with lumped friction is presented to illustrate the applicability of the proposed diagnosis method.     

Full-order and reduced-order observers for linear parameter-varying systems with one-sided Lipschitz nonlinearities and disturbances using parameter-dependent Lyapunov function

In this study, H_∞ full-order and reduced-order observers are designed for a class of linear parameter-varying (LPV) systems with one-sided Lipschitz nonlinearities and disturbances. Parameter-dependent Lyapunov function is used to analyze the robust stability of the proposed observers. Three practical examples are used to demonstrate the usefulness of the proposed observers. Simulation and experimental results indicate that the proposed observers work well.     

Tracking control algorithms for plants with input time-delays based on state and disturbance predictors and sub-predictors

The novel control algorithm for linear time invariant plants with input time-delays and presence of external disturbances is proposed. The algorithm based on the state and disturbance predictors ensures the tracking control with unknown reference model parameters. The accuracy in steady state depends on the highest derivative of disturbance and reference model signals, therefore, the magnitude of these signals can be sufficiently large. Further, the proposed algorithm are extended on the state and disturbance sub-predictors which implement multi-step prediction. Compared with the predictor based algorithm the sub-predictor based algorithm allows to control plants with a larger input time-delay and allows to predict the disturbance in less time. The sufficient conditions in terms of linear matrix inequalities (LMIs) provide the estimate of the maximum time-delay that preserves the closed-loop system stability. Numerical examples illustrate the efficiency of the designed method compared with some existing ones.     

A T-S fuzzy state observer-based model predictive reset control for a class of fuzzy nonlinear systems with event-triggered mechanism

In this study, the problem of observer-based control for a class of nonlinear systems using Takagi-Sugeno (T-S) fuzzy models is investigated. The observer-based model predictive event-triggered fuzzy reset controller is constructed by a T-S fuzzy state observer, an event-triggered fuzzy reset controller, and a model predictive mechanism. First, the proposed controller utilizes the T-S fuzzy model and is constructed based on state observations and discrete sampling output, which can greatly reduce the occupation of communication resources. Then, the model predictive strategy for reset law design is designed in this paper. With a reasonable reset of the controller state at certain instants, the performance of the reset control systems is improved. Finally, the validity of the proposed method is illustrated by simulation. The merits of the proposed controller in improving transient performance and reducing the communication occupation are demonstrated by comparing its results with the output feedback fuzzy controller and the first-order fuzzy reset controller.     

On convergence of the discrete-time nonlinear extended state observer

This paper concerns with the convergence of the discrete-time nonlinear extended state observer (ESO). Several kinds of discrete-time nonlinear ESO (NLESO) are proposed and then sufficient conditions based on linear matrix inequality (LMI) method are obtained to quantitatively reveal the relationship between the plant, the sampling interval, the parameter values of NLESO and its convergence. The theoretical results are verified by simulation using a motion control system. It shows that there may exist an optimal ω_o for a certain fixed sampling interval, and a smaller sampling interval generally generates better performance. What’s more, the proposed digital implementations of NLESO improve its performance over traditional Euler approximation discretization method.