Mismatched Uncertainties Research Articles

Output Consensus of Heterogeneous Multiagent Systems With Mismatched Uncertainties and Measurement Noises: An ADRC Approach

Output Consensus of Heterogeneous Multiagent Systems With Mismatched Uncertainties and Measurement Noises: An ADRC Approach

A Two-Level Neural-RL-based Approach for Hierarchical Multiplayer Systems under Mismatched Uncertainties

A Two-Level Neural-RL-based Approach for Hierarchical Multiplayer Systems under Mismatched Uncertainties

Fast Finite Time Stability in Probability of p-Norm Stochastic Nonlinear Systems With Mismatched Uncertainties and Dead-Zone

Fast Finite Time Stability in Probability of p-Norm Stochastic Nonlinear Systems With Mismatched Uncertainties and Dead-Zone

Adaptive Quantized Output Feedback Control of Nonlinear Systems With Mismatched Uncertainties and Sensor Failures

Adaptive Quantized Output Feedback Control of Nonlinear Systems With Mismatched Uncertainties and Sensor Failures

Event-Triggered Adaptive Control for a Class of Nonlinear Systems With Mismatched Uncertainties via Intermittent and Faulty Output Feedback

Due to the difficulty in handling the issue of non-differentiable virtual control signals, there is still no available result considering output-triggering and sensor faults simultaneously for nonlinear systems subject to mismatched parametric uncertainties via backstepping design, even though the underlying problem is practically important and frequently encountered. In this paper, to deal with this challenging problem, several crucial design ideas are proposed in developing the control scheme. Firstly, to generate the continuous substitutes for the states, which are necessary in differentiating the virtual control signals in the backstepping design, we construct a state observer driven by intermittent and faulty output arising from output-triggering and sensor faults. Secondly, to ensure the existence of the first derivative of the virtual control signal, we use the estimated output signal to design the virtual control signals. Thirdly, to avoid the repeated differentiations of virtual control signals in backstepping design, we employ the idea of dynamic filtering technique. It is shown that all signals in the resulting closed-loop system are semi-globally uniformly ultimately bounded. Simulation examples are given to demonstrate the effectiveness of the proposed control strategy.

Fixed-Time Sliding Mode Control for DC/DC Buck Converters With Mismatched Uncertainties

In this paper, the fixed-time control problem of DC-DC buck converter systems with mismatched disturbances is investigated. Firstly, the sliding mode fixed-time observers are constructed to estimate the matched and mismatched disturbances of the systems. Secondly, a novel segmented terminal sliding mode control (TSMC) variable considering the mismatched disturbance of the system is designed based on the observations. To improve the tracking performance, a new second-order fixed-time reaching law is proposed. Then, in order to make the DC-DC buck converter systems with mismatched disturbances achieve accurate control in a fixed-time independent of the initial state, a novel fixed-time nonsingular TSMC based on the fixed-time observers is proposed. Finally, comparative experiments are conducted to verify the effectiveness and practicality of the proposed control strategy.

Adaptive Output Feedback Control of Nonlinear Systems With Mismatched Uncertainties Under Input/Output Quantization

It is still an open problem to synthesize control with input and output quantizations for nonlinear systems subject to mismatched parametric uncertainties via backstepping design. This is because 1) the output becomes discontinuous and nondifferentiable after quantization, making the conventional recursive backstepping design method unsuitable as the differentiation of the virtual control signals does not exist, while the existing backstepping-based quantized control method is only applicable to systems modeled in normal form or systems without mismatched uncertainties; 2) it is difficult to deal with the impacts of errors caused by output quantization when the virtual control laws contain time-varying estimates of unknown parameters; and 3) since only the quantized output signal is available, the effects of certain items related to unknown parameters in currently available observers become difficult to be explicitly addressed. In this article, we present a solution to this problem by employing a dynamic filtering technique to avoid differentiating virtual control signals in backstepping design, using the parameter projection technique to design the parameter estimator and proposing a new form of state observer to facilitate the development of output feedback control. It is shown that, with the derived adaptive backstepping control involving both input and output quantizations, all the closed-loop signals are ensured bounded and the control performance in terms of the mean square error is adjustable through properly choosing certain design parameters. The benefits and effectiveness of the proposed scheme are also validated by numerical simulation.

Fuzzy-Set Theoretic Control Design for Aircraft Engine Hardware-in-the-Loop Testing: Mismatched Uncertainty and Optimality

A novel robust control design is proposed for aircraft engines. The engine is modeled as an uncertain dynamic system, whose uncertainty may be (possibly fast) time-varying. The possible value of the uncertainty is prescribed to be within fuzzy sets. This distinguishes our modeling from the Takagi–Sugeno inference system. The uncertainty is divided into matched and mismatched portions. While the matched uncertainty lies within the range space of the input matrix, the mismatched uncertainty falls outside, which poses a significant challenge for the control design. The objective is to propose a new robust control design for aircraft engines, which are subject to mismatched uncertainty. A robust control, which is deterministic and is not IF–THEN fuzzy rule-based, is designed. The control design parameter needs to be feasible, i.e., within a prescribed range. Some prescribed deterministic performances of the system are guaranteed. By taking the control cost and the performance threshold into consideration, which is under the influence of both matched and mismatched uncertainty, the unique optimal choice of the design parameter is proposed. As a result, this control design delicately blends optimality with mismatched uncertainty. This design is applied to a turbofan engine. Hardware-in-the-loop laboratory testing in the flight envelope demonstrates the superiority of the control design.

Adaptive Finite‐Time Sliding Mode Backstepping Controller for Double‐Integrator Systems with Mismatched Uncertainties and External Disturbances

In this paper, a novel adaptive finite‐time sliding mode backstepping (AFSMBS) control scheme is suggested to control a type of high‐order double‐integrator systems with mismatched disturbances and uncertainties. A robust sliding mode backstepping control method, adaptive control method, and finite‐time stability notion are incorporated to provide a better tracking performance over applying them individually and to use their benefits simultaneously. The concept of a sliding mode is used to define a new form of a backstepping controller. The adaptive control method is utilized to adaptively estimate the upper bounds of the disturbances and uncertainties and the estimated data are used in the control low. The notion of the finite‐time stability is incorporated with the suggested control scheme to ensure the system’s convergence within a finite time. The stability proof is obtained for the closed‐loop system in a finite time utilizing the Lyapunov stability theorem. Simulation results are obtained for an example of a remotely operated vehicle (ROV) with three degrees of freedom (3‐DOF) to demonstrate the efficacy of the suggested control approach.

Adaptive Neural Network Sliding Mode Control for Nonlinear Singular Fractional Order Systems with Mismatched Uncertainties

This paper focuses on the sliding mode control (SMC) problem for a class of uncertain singular fractional order systems (SFOSs). The uncertainties occur in both state and derivative matrices. A radial basis function (RBF) neural network strategy was utilized to estimate the nonlinear terms of SFOSs. Firstly, by expanding the dimension of the SFOS, a novel sliding surface was constructed. A necessary and sufficient condition was given to ensure the admissibility of the SFOS while the system state moves on the sliding surface. The obtained results are linear matrix inequalities (LMIs), which are more general than the existing research. Then, the adaptive control law based on the RBF neural network was organized to guarantee that the SFOS reaches the sliding surface in a finite time. Finally, a simulation example is proposed to verify the validity of the designed procedures.

Distributed Consensus of Nonlinear Multi-Agent Systems With Mismatched Uncertainties and Unknown High-Frequency Gains

This brief addresses the distributed consensus problem of nonlinear multi-agent systems under a general directed communication topology. Each agent is governed by higher-order dynamics with mismatched uncertainties, multiple completely unknown high-frequency gains, and external disturbances. The main contribution of this brief is to present a new distributed consensus algorithm, enabling the control input of each agent to require minimal information from its neighboring agents, that is, only their output information. To this end, a dynamic system is explicitly constructed for each agent to generate a reference output. Theoretical and simulation verifications of the proposed algorithm are rigorously studied to ensure that asymptotic consensus can be achieved and that all closed-loop signals remain bounded.

Erratum to “Quadratic Integral Sliding Mode Control for Nonlinear Harmonic Gear Drive Systems with Mismatched Uncertainties”

Erratum to “Quadratic Integral Sliding Mode Control for Nonlinear Harmonic Gear Drive Systems with Mismatched Uncertainties”

Asymptotic Tracking Control for a More Representative Class of Uncertain Nonlinear Systems With Mismatched Uncertainties

This paper is mainly focusing on the problem of high-accuracy tracking control design for a class of nonlinear systems subject to mismatched uncertainties. A novel asymptotic control framework is presented. This is achieved by developing an estimator-based controller with an observer-based estimator, which is applied to precisely estimate all the system uncertainties. It is proved that the overall tracking system can be asymptotically stabilized. The estimation error of the system uncertainties is also ensured to be asymptotically stable. The main contribution of this paper is that the proposed solution can control a more representative class of nonlinear systems. Another key feature of this control framework is that the incorporated observer-based estimator can eliminate the assumption that system uncertainties should vary slowly or even have no variation in the existing estimators for uncertainties. This superior tracking control property of the scheme is validated by a robotic manipulator example.

A Disturbance Observer Based Sliding Mode Control for Variable Speed Wind Turbine

This paper shows the effectiveness of disturbance observer (DO) based sliding mode control (SMC) scheme as compared to traditional SMC. Both the methods are simulated for variable speed wind turbine (VSWT) to ensure asymptotic tracking of rotor speed even in the presence of disturbances. Two mass model of VSWT is derived which includes the damping effect of the rotor's blade and generator. The stiffness and damping effect of low-speed shaft are also taken into account while modelling the system. The dynamic model of VSWT contains both matched and mismatched uncertainties. Traditional SMC can only attenuate the effect of match uncertainties. The DO-based SMC overcomes the drawback of traditional SMC by attenuating mismatch uncertainties. In the proposed approach, a sliding manifold is chosen based on the estimation of an uncertain parameter (aerodynamic torque), which drives the system's states to equilibrium point in a finite time.

Lyapunov Control Method for Mismatched Uncertainty and Gain Variation Compensation in Switched Systems

Since the inherent nature of many applied high-technological systems and networks possesses switching behaviors, modeling, controlling, and stabilizing problems of the switching plants are becoming as the important research scenarios in the current literature. Moreover, consideration of the effects of mismatching uncertainties along with matching uncertain parts as well as unknown dynamics is a significant topic in the control engineering community. As a result, this paper is dedicated to the controlling problem of partially unknown nonlinear switched systems whose dynamics are perturbed by both matched and mismatched uncertainties. In addition, since the existence of gain deviations in real-world implementations of physical actuators can lessen the control act of the switching system, this paper handles the corresponding gain variations in the control inputs via some adaptation variables. In the design procedure, no limiting supposition is made for the switch signal of the switched plant. The proposed control algorithm is based on the variable structure control theory in which the selected sliding surface is able to deal with mismatching uncertainties and the deriving control law is robust against the lumped uncertain components. As a contribution, the Lyapunov functions are built such that not only to provide an asymptotic stable system but also to assure the global (practical) finite-time stability of the equilibrium state of the final closed-loop cascade switched plant. The robust performance of the developed control strategy is confirmed via two illustrative examples.

Neural dynamic sliding mode control of nonlinear systems with both matched and mismatched uncertainties

Mismatched uncertainty and chattering appear as two challenges in sliding mode control. To overcome the problem of mismatched uncertainty, multiple sliding surfaces with virtual inputs are proposed. Accordingly, we have proposed two new methods based on designed neural observer: sliding mode control (SMC) and dynamic sliding mode control (DSMC) methods. Although, the proposed SMC can significantly cope with the mismatched uncertainties, but it suffers from chattering phenomenon. The chattering problem can be removed in DSMC, because an integrator is placed before the system. This results in increased number of the system states. This new state can be identified with the proposed neural observer. Note that in both proposed approaches, the robust performance (invariance property) of system is reserved, even in the presence of mismatch uncertainties. Then, to have a valid comparison the proposed DSMC is also designed using loop transfer recovery observer (LTRO). This comparison shows the good performance of the DSMC based neural networks. Moreover, the upper bound of uncertainties is not used in SMC and DSMC controllers and also in the neural observer and LTRO, which is important in practical implementation. Finally, comparing the equations, one can see the simplicity of DSMC in concept and also in realization.

Uncertainty and Disturbance Estimator-Based Backstepping Control for Nonlinear Systems With Mismatched Uncertainties and Disturbances

This paper proposes an uncertainty and disturbance estimator (UDE)-based controller for nonlinear systems with mismatched uncertainties and disturbances, integrating the UDE-based control and the conventional backstepping scheme. The adoption of the backstepping scheme helps to relax the structural constraint of the UDE-based control. Moreover, the reference model design in the UDE-based control offers a solution to address the “complexity explosion” problem of the backstepping approach. Furthermore, the strict-feedback form condition in the conventional backstepping approach is also relaxed by using the UDE-based control to estimate and compensate “disturbance-like” terms including nonstrict-feedback terms and intermediate system errors. The uniformly ultimate boundedness of the closed-loop system is analyzed. Both numerical and experimental studies are provided.

Quadratic Integral Sliding Mode Control for Nonlinear Harmonic Gear Drive Systems with Mismatched Uncertainties

For a class of nonlinear harmonic gear drive systems with mismatched uncertainties, a novel robust control method is presented on the basis of quadratic integral sliding mode surface, and the closed-loop system has satisfying performance and strong robustness against mismatched uncertainties and nonlinear disturbances. Considering time-varying nonlinear torques and parameters variations which are caused by nonlinear frictions and backlash, a nonlinear harmonic gear drive system mathematic model is established and the effect of nonlinear parts is compensated during control system design. It is proven that the quadratic integral sliding mode surface can be reached in finite time and the closed-loop system is asymptotic stable robustly. The simulation studies are carried out in comparison with traditional linear sliding mode control and integral sliding mode control, verifying the effectiveness of the proposed method.

Feedback Linearization Control for Systems with Mismatched Uncertainties via Disturbance Observers

AbstractThis paper focuses on a novel feedback linearization control (FLC) law based on a self‐learning disturbance observer (SLDO) to counteract mismatched uncertainties. The FLC based on BNDO (FLC‐BNDO) demonstrates robust control performance only against mismatched time‐invariant uncertainties while the FLC based on SLDO (FLC‐SLDO) demonstrates robust control performance against mismatched time‐invariant and ‐varying uncertainties, and both of them maintain the nominal control performance in the absence of mismatched uncertainties. In the estimation scheme for the SLDO, the BNDO is used to provide a conventional estimation law, which is used as the learning error for the type‐2 neuro‐fuzzy system (T2NFS), and T2NFS learns mismatched uncertainties. Thus, the T2NFS takes the overall control of the estimation signal entirely in a very short time and gives unbiased estimation results for the disturbance. A novel learning algorithm established on sliding mode control theory is derived for an interval type‐2 fuzzy logic system. The stability of the overall system is proven for a second‐order nonlinear system with mismatched uncertainties. The simulation results show that the FLC‐SLDO demonstrates better control performance than the traditional FLC, FLC with an integral action (FLC‐I), and FLC‐BNDO.

Decentralized Adaptive Sliding Mode Control of Large Scale Systems with Mismatched Uncertainties and Interconnections

Decentralized Adaptive Sliding Mode Control of Large Scale Systems with Mismatched Uncertainties and Interconnections