Distributed adaptive fault-tolerant control against actuator faults and lossy interconnection links

This paper presents an adaptive method to solve the robust fault-tolerant control （FTC） problem for a class of large scale systems against actuator failures and lossy interconnection links. In terms of the special distributed architectures, the adaptation laws are proposed to estimate the unknown eventual faults of actuators and interconnections, constant external disturbances, and controller parameters on-line. Then a class of distributed state feedback controllers are constructed for automatically compensating the fault and disturbance effects on systems based on the information from adaptive schemes. On the basis of Lyapunov stability theory, it shows that the resulting adaptive closed-loop large-scale system can be guaranteed to be asymptotically stable in the presence of uncertain faults of actuators and interconnections, and constant disturbances. The proposed design technique is finally evaluated in the light of a simulation example.     

On matching conditions for adaptive state tracking control ofsystems with actuator failures

In this paper, new necessary and sufficient conditions are derived for actuator failure compensation for linear time-invariant systems with actuator failures characterized by unknown input signals at some unknown fixed values and time instants, for state tracking with state feedback. It is shown that the number of fully functional actuators is crucial in determining the actuation range, which specifies the compensation design conditions in terms of system actuation structure. Such conditions are required for both a nominal design using system knowledge and an adaptive design without system knowledge. An adaptive actuator failure compensation control scheme based on relaxed system actuation conditions is developed for systems with unknown dynamic parameters and actuator failure parameters including failure values, times, and patterns. Simulation results are presented to verify the desired system performance with failure compensation     

An adaptive control scheme for systems with unknown actuator failures

A state feedback output tracking adaptive control scheme is developed for plants with actuator failures characterized by the failure pattern that some inputs are stuck at some unknown fixed values at unknown time instants. New controller parametrization and adaptive law are developed under some relaxed system conditions. All closed-loop signals are bounded and the plant output tracks a given reference output asymptotically, despite the uncertainties in actuator failures and plant parameters. Simulation results verify the desired adaptive control system performance in the presence of actuator failures.     

Observer-based adaptive sliding mode control for robust tracking and model following

This paper presents a methodological approach to design an observer-based adaptive sliding mode control to realize the problem of robust tracking and modeling following for a class of uncertain linear systems. Only partial information of the system states is known. Based on Lyapunov stability theorem, it will be shown that the proposed scheme guarantees the stability of closed-loop system and achieves zero-tracking error in the presence of parameter uncertainties and external disturbances. The proposed observer-based adaptive sliding mode control scheme can be implemented without requiring a priori knowledge of upper bounds on the norm of the uncertainties and external disturbances. This scheme assures robustness against system uncertainties and disturbances. Both the theoretical analysis and illustrative example demonstrate the validity of the proposed scheme.     

Model reference adaptive control of state delayed system with actuator failures

This paper develops adaptive state feedback control schemes for a class of linear systems with state delay in the presence of unknown actuator failures. The main contributions of this paper are the development of two controller parametrizations which attempt to anticipate the future states, the introduction of an appropriate Lyapunov–Krasovskii type functional to design the new class of adaptation algorithms, and a stability proof.     

Observer-based adaptive control of uncertain time-delay switched systems with stuck actuator faults

This paper concerns the observer-based adaptive control problem of uncertain time-delay switched systems with stuck actuator faults. Under the case where the original controller cannot stabilize the faulty system, multiple adaptive controllers are designed and a suitable switching logic is incorporated to ensure the closed-loop system stability and state tracking. New delay-independent sufficient conditions for asymptotic stability are obtained in terms of linear matrix inequalities based on piecewise Lyapunov stability theory. On the other hand, adaptive laws for on-line updating of some of the controller parameters are also designed to compensate the effect of stuck failures. Finally, simulation results for reference [1] model show that the design is feasible and efficient.     

Modeling and adaptive control for a spatial flexible spacecraft with unknown actuator failures

In this paper,we address simultaneous control of a flexible spacecraft's attitude and vibrations in a three-dimensional space under input disturbances and unkno...     

具有降阶模型的多模型鲁棒自适应控制

传统鲁棒自适应控制由于考虑了实际系统存在的不确定性,在一定程度上扩大了常规自适应控制的应用范围,但是传统鲁棒自适应控制大多只是从系统全局稳定性的角度出发来设计控制器而忽略系统动态和稳态性能,导致其无法在工况多变的实际被控系统中取得令人满意的效果。针对传统鲁棒自适应控制的不足,本文对由ARMA模型描述并包含未建模动态的系统设计了多模型鲁棒自适应控制器。首先采用正则化技术将系统未建模动态转化为系统有界扰动,并在系统降阶模型的基础上根据系统工况的变化设计了多个固定控制器和2个鲁棒自适应控制器,并根据性能指标函数选择最佳控制器作为当前系统控制器以提高系统性能。仿真实验说明当系统存在未建模动态以及系统工况发生变化时,本文设计的控制器能获得较好的控制效果。     

Application of a robust model reference adaptive control algorithm to a nonlinear automotive actuator

Model reference adaptive control is a viable control method to impose the demanded dynamics on plants whose parameters are affected by large uncertainty. In this paper, we show by means of experiments that robust adaptive methods can effectively face nonlinearities that are common to many automotive electromechanical devices. We consider here, as a representative case study, the control of a strongly nonlinear automotive actuator. The experimental results confirm the effectiveness of the method to cope with unmodeled nonlinear terms and unknown parameters. In addition, the engineering performance indexes computed on experimental data clearly show that the robust adaptive strategy provides better performance compared with those given by a classical model-based control solution with fixed gains.     

Backstepping adaptive fuzzy control of uncertain nonlinear systems against actuator faults

A class of unknown nonlinear systems subject to uncertain actuator faults and external disturbances will be studied in this paper with the help of fuzzy approximation theory. Using backstepping technique, a novel adaptive fuzzy control approach is proposed to accommodate the uncertain actuator faults during operation and deal with the external disturbances though the systems cannot be linearized by feedback. The considered faults are modeled as both loss of effectiveness and lock-in-place （stuck at some unknown place）. It is proved that the proposed control scheme can guarantee all signals of the closed-loop system to be semi-globally uniformly ultimately bounded and the tracking error between the system output and the reference signal converge to a small neighborhood of zero, though the nonlinear functions of the controlled system as well as the actuator faults and the external disturbances are all unknown. Simulation results demonstrate the effectiveness of the control approach.     

Distributed robust consensus control of multi-agent systems with heterogeneous matching uncertainties

This paper considers the distributed consensus problem of linear multi-agent systems subject to different matching uncertainties for both the cases without and with a leader of bounded unknown control input. Due to the existence of nonidentical uncertainties, the multi-agent systems discussed in this paper are essentially heterogeneous. For the case where the communication graph is undirected and connected, based on the local state information of neighboring agents, a fully distributed continuous adaptive consensus protocol is designed, under which the consensus error is uniformly ultimately bounded and exponentially converges to a small adjustable bounded set. For the case where there exists a leader whose control input is unknown and bounded, a distributed adaptive consensus protocol is proposed to ensure the boundedness of the consensus error. A sufficient condition for the existence of the proposed protocols is that each agent is stabilizable.     

On robust tracking with non-linear model predictive control

In this paper a class of stabilizing model predictive control algorithms based on an optimization problem with two different horizons (control and prediction) is considered. Particular attention is devoted to the tracking problem for reference signals constant beyond a prescribed future horizon. For such a problem, it is pointed out the necessity of an output-feedback scheme to guarantee robust steady-state zero-error regulation. Moreover, an additional constraint is introduced in the optimization problem for the robust fulfilment of the constraints on the control variable. The potentiality of this approach is shown through some simulation experiments on a highly non-linear chemical reactor.     

Reconfigurable control of piecewise affine systems with actuator and sensor faults: Stability and tracking

A reconfigurable control approach for continuous-time piecewise affine (PWA) systems subject to actuator and sensor faults is presented. The approach extends the concept of virtual actuators and virtual sensors from linear to PWA systems on the basis of the fault-hiding principle that provides the underlying conceptual idea: the fault is hidden from the nominal controller and the fault effects are compensated. Sufficient linear matrix inequality (LMI) conditions for the existence of virtual actuators and virtual sensors are given that guarantee the recovery of closed-loop stability and the tracking of constant reference inputs. Since LMIs are efficiently solvable, this solution leads to a tractable computational algorithm that solves the reconfiguration problem. The approach is proven to be robust against model uncertainties and inaccurate fault diagnosis, and is evaluated using an example system of interconnected tanks.     

Observer-based adaptive fuzzy fault-tolerant output feedback control of uncertain nonlinear systems with actuator faults

This paper develops an adaptive fuzzy control method for accommodating actuator faults in a class of unknown nonlinear systems with unmeasured states. The considered faults are modeled as lock-in-place (stuck at unknown place). With the help of fuzzy logic systems to approximate the unknown nonlinear functions, and K-filters are designed to estimate the unmeasured states. Combining the backstepping technique with the nonlinear fault-tolerant control theory, a novel adaptive fuzzy faults-tolerant control (FTC) approach is constructed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the tracking error between the system output and the reference signal converges to a small neighborhood of zero by appropriate choice of the design parameters. Simulation results are provided to show the effectiveness of the control approach.     

Synthesizing scheduled robust model predictive control with target tracking

Systems with large operating regions and non-zero state target tracking have limited the industrial application of robust model predictive control (RMPC) with synthetic action. To overcome the problem, this paper presents a novel formulation of synthesizing scheduled RMPC for linear time varying (LTV) systems. Off-line, we compute the matrix that transforms target output into steady state first. Then a set of stabilizing state feedback laws which are corresponding to a set of estimated regions of stability covering the desired operating region are provided. On-line, these control laws are implemented as a single scheduled state feedback model predictive control (MPC) which switches between the set of local controllers and achieve the desired target at last. Finally, the algorithm is illustrated with an example.     

Distributed adaptive tracking control for high-order multi-agent systems with unknown dynamics

In this paper, we investigate the adaptive tracking problem of high-order multi-agent systems with unknown parameters and unknown nonlinear functions. Under the assumption that the leader is the root of a spanning tree, a distributed adaptive controller with tuning function is constructed recursively based on backstepping design method. The designed controller can guarantee that the tracking errors and the parameter errors eventually converge to an arbitrarily small compact set by choosing design parameters. A simulation example demonstrates the effectiveness of the design scheme.     

Adaptive compensation for infinite number of actuator failures or faults

It is both theoretically and practically important to investigate the problem of accommodating infinite number of actuator failures or faults in controlling uncertain systems. However, there is still no result available in developing adaptive controllers to address this problem. In this paper, a new adaptive failure/fault compensation control scheme is proposed for parametric strict feedback nonlinear systems. The techniques of nonlinear damping and parameter projection are employed in the design of controllers and parameter estimators, respectively. It is proved that the boundedness of all closed-loop signals can still be ensured in the case with infinite number of failures or faults, provided that the time interval between two successive changes of failure/fault pattern is bounded below by an arbitrary positive number. The performance of the tracking error in the mean square sense with respect to the frequency of failure/fault pattern changes is also established. Moreover, asymptotic tracking can be achieved when the total number of failures and faults is finite.     

Computing actuator bandwidth limits for model reference adaptive control

Although model reference adaptive control theory has been used in numerous applications to achieve system performance without excessive reliance on dynamical system models, the presence of actuator dynamics can seriously limit the stability and the achievable performance of adaptive controllers. In this paper, a linear matrix inequalities-based hedging approach is developed and evaluated for model reference adaptive control of uncertain dynamical systems in the presence of actuator dynamics. The hedging method modifies the ideal reference model dynamics in order to allow correct adaptation that is not affected by the presence of actuator dynamics. Specifically, we first generalise the hedging approach to cover a variety of cases in which actuator output and the control effectiveness matrix of the uncertain dynamical system are known and unknown. We then show the stability of the closed-loop dynamical system using Lyapunov-based stability analysis tools and propose a linear matrix inequality-based framework for the computation of the minimum allowable actuator bandwidth limits such that the closed-loop dynamical system remains stable. Finally, an illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.     

An adaptive controller design for linear state-delay systems with actuator failures

In this paper, an adaptive fault tolerant controller is developed for a class of linear state delay systems against actuator failures. To design the controller, all parameters of the system are considered to be unknown, but the time delay value is assumed to be known. Actuator failures are characterized by some unknown system inputs are stuck at some unknown fixed values and at unknown time instants. The adaptive controller is designed based on SPR-Lyapunov approach for the cases with the relative degrees of one and two. Closed-loop system stability and asymptotic output tracking are proved using a suitable Lyapunov-Krasovskii functional for each case and the effectiveness of the proposed results has been illustrated through simulation studies.