Neuroadaptive Fault-tolerant Control Research Articles

Command Filtered Neuroadaptive Fault-Tolerant Control for Nonlinear Systems With Input Saturation and Unknown Control Direction.

This article studies the tracking control of a class of nonlinear systems with input saturation, subject to nonaffine faults and unknown control direction. A fault-tolerant command filtered control (CFC) method based on adaptive neural networks (NNs) is proposed for this kind of nonlinear system. First, the combination of CFC and error compensation overcomes the "explosion of complexity" issue and alleviates the impact of filter errors. Then, a set of radial basis function NNs is constructed to approximate the unknown nonlinear items containing the nonaffine fault function. Additionally, the issue of unknown control direction in the system is effectively resolved by using Nussbaum gain technology. It is proven that the designed controller can ensure that all signals in the closed-loop system are bounded and convergent, and the upper bound of the absolute value of system tracking error is given. Finally, three comparative simulation results are illustrated to show the effectiveness of the proposed method.

Neuroadaptive Fault-Tolerant Control Embedded With Diversified Activating Functions With Application to Auto-Driving Vehicles Under Fading Actuation.

This article presents a neuroadaptive fault-tolerant control method for path tracking of multiinput multioutput (MIMO) systems in the presence of modeling uncertainties and external disturbances. In dealing with modeling uncertainties, neural networks (NNs) with diversified activation/basis functions are considered, with which we establish a set of control algorithms that are robust against uncertainties, adaptive to unknown parameters, and tolerant to actuation faults. This is the first work that explicitly takes into account the neural weights uncertainties and activating function uncertainties in multiple layered neural networks in control design. In addition, we apply the developed control algorithms to unmanned ground vehicles (UGVs) with actuator failures. With the aid of Lyapunov stability theory, it is shown that the proposed control is able to drive the vehicle along the desired path with high precision and all the internal signals are uniformly ultimately bounded (UUB) and continuous. Both theoretical analysis and numerical simulation confirm the effectiveness of the designed strategy.

Neuroadaptive Fault-Tolerant Control With Unsynchronized Event Triggering for Actuation Updating and Parameter Adaptation.

Communication and computation resources are normally limited in remote/networked control systems, and thus, saving either of them could substantially contribute to cost reduction and life-span increasing as well as reliability enhancement for such systems. This article investigates the event-triggered control method to save both communication and computation resources for a class of uncertain nonlinear systems in the presence of actuator failures and full-state constraints. By introducing the triggering mechanisms for actuation updating and parameter adaptation, and with the aid of the unified constraining functions, a neuroadaptive and fault-tolerant event-triggered control scheme is developed with several salient features: 1) online computation and communication resources are substantially reduced due to the utilization of unsynchronized (uncorrelated) event-triggering pace for control updating and parameter adaptation; 2) systems with and without constraints can be addressed uniformly without involving feasibility conditions on virtual controllers; and 3) the output tracking error converges to a prescribed precision region in the presence of actuation faults and state constraints. Both theoretical analysis and numerical simulation verify the benefits and efficiency of the proposed method.

Unified neuroadaptive fault-tolerant control of fractional-order systems with or without state constraints

The problem of controlling fractional-order nonlinear systems remains interesting and challenging due to the powerful and hereditary memory features of such systems. In this paper, we investigate the adaptive tracking control problem for a class of fractional-order nonlinear systems with or without state constraints. The tan-type barrier Lyapunov function (BLF) is adopted for the first time in fractional Lyapunov direct method. Such technique allows for the accommodation of the situations with and without state constraints in a unified framework. Moreover, neural network (NN) is utilized to reconstruct the sophisticated nonlinear functions arising from the fractional-order differentiation of the virtual controller, resulting in a neuroadaptive fault-tolerant control scheme. It is shown that, with the proposed unified control, all the closed-loop signals are semiglobally ultimately uniformly bounded whether state constraints are imposed or not. Both theoretical analysis and numerical simulation confirm the effectiveness of the developed approach.

BLF-Based Neuroadaptive Fault-Tolerant Control for Nonlinear Vehicular Platoon With Time-Varying Fault Directions and Distance Restrictions

This paper investigates the neuroadaptive fault-tolerant control of nonlinear vehicular platoon with unmodeled dynamics, external disturbances, time-varying actuator fault directions, and distance restrictions. For the cases of known and unknown fault directions, by combining adaptive terminal sliding mode (TSM) control technique with barrier Lyapunov function (BLF), two neuroadaptive fault-tolerant controllers are designed based on symmetric and asymmetric BLF to ensure reliability and safety of vehicular platoon. BLF approaches are adopted to avoid collisions and to maintain communication connections simultaneously. In addition, it is worth mentioning that the unfavorable symmetry assumptions in the symmetric BLF and prescribed performance methods can be removed by adopting asymmetric BLF. In the proposed scheme, we also combine the Nussbaum function to solve the influence of unknown time-varying fault directions effectively. Furthermore, the nonsingular TSM control technique and the minimum parameter approximation method in radial basis function neural network (RBFNN) are adopted to ensure that the spacing error can converge to an arbitrarily small region in finite-time. Through the Lyapunov stability theory, we prove that all signals in the closed-loop system are bounded in finite time. The proposed control schemes are validated by means of simulation examples.

Prescribed Performance Neuroadaptive Fault-Tolerant Compensation for MIMO Nonlinear Systems Under Extreme Actuator Failures

This article investigates the issue of neuroadaptive tracking control for a family of unknown multi-input multi-output (MIMO) nonlinear uncertain systems subject to extreme actuation failures. Different from most existing methods that are built upon partial loss of actuation effectiveness, here, in this article, we explicitly consider the situation that some actuators at some particular channel completely fail to work, an issue that has not been well addressed. By integrating the neural network approximation technique with two error transformations, a neuroadaptive fault-tolerant control strategy is developed with two attractive features: 1) it is capable of coping with the scenario that some of the actuators suffer from extreme actuation faults without the need for fault detection and diagnosis (FDD)/fault detection and isolation (FDI) or actuator switching and 2) the tracking error is forced to converge to a prescribed residual (symmetric or asymmetric) boundary at a preassignable decay mode within a prechosen finite settling time despite actuator failures and external disturbances. Numerical simulation studies confirm the effectiveness and benefits of the presented control approach.

Distributed neuroadaptive fault-tolerant sliding-mode control for 2-D plane vehicular platoon systems with spacing constraints and unknown direction faults

In this paper, neuroadaptive fault-tolerant control of nonlinear vehicular platoon systems subject to unknown direction actuator faults, unmodeled dynamics, and external disturbances is investigated. A vehicle model considering the influence of velocity direction deflection angle on vehicle position is proposed on a two-dimensional (2-D) plane to realize multi-lane vehicle fusion. Then, in order to avoid collisions and maintain communication connection, the method of asymmetric barrier Lyapunov function (BLF) is adopted to eliminate the unfavorable assumption on spacing constraints in using symmetric BLF in the existing result. Nussbaum function is adopted to attenuate the negative effects caused by unknown direction actuator faults. Furthermore, by combining sliding-mode control (SMC) techniques with radial basis function neural network (RBFNN), a novel neuroadaptive fault-tolerant control scheme with minimal learning parameters is designed to not only guarantee the finite-time stability of the whole vehicular platoon but also tolerate the unknown direction actuator faults. Finally, simulation results show the effectiveness and advantages of the proposed scheme.

Neuroadaptive fault-tolerant control of state constrained pure-feedback systems: A collective backstepping design

In this work, we present a neuroadaptive and fault-tolerant tracking control scheme for uncertain nonlinear pure-feedback systems in the presence of time-varying and asymmetric full state constraints and unanticipated actuation failures. Instead of using multi-step recursive backstepping design, we employ a one-step approach for control development. By introducing a nonlinear coordinate transformation, we convert the original nonlinear system with asymmetrical state constraints into a new augmented one free from state constraints, which allows for the complete obviation of the feasibility conditions in the strategy. Furthermore, by making use of the feature from skew symmetric matrix in the augmented system, we develop the neural adaptive control algorithms collectively without the need for repetitive design procedure, in which only one Lyapunov function and one step derivation are involved, leading to a design approach whose synthesis complexity does not increase with the order of the system.

Neuroadaptive Fault-Tolerant Control Under Multiple Objective Constraints With Applications to Tire Production Systems.

Many manufacturing systems not only involve nonlinearities and nonvanishing disturbances but also are subject to actuation failures and multiple yet possibly conflicting objectives, making the underlying control problem interesting and challenging. In this article, we present a neuroadaptive fault-tolerant control solution capable of addressing those factors concurrently. To cope with the multiple objective constraints, we propose a method to accommodate these multiple objectives in such a way that they are all confined in certain range, distinguishing itself from the traditional method that seeks for a common optimum (which might not even exist due to the complicated and conflicting objective requirement) for all the objective functions. By introducing a novel barrier function, we convert the system under multiple constraints into one without constraints, allowing for the nonconstrained control algorithms to be derived accordingly. The system uncertainties and the unknown actuation failures are dealt with by using the deep-rooted information-based method. Furthermore, by utilizing a transformed signal as the initial filter input, we integrate dynamic surface control (DSC) into backstepping design to eliminate the feasibility conditions completely and avoid off-line parameter optimization. It is shown that, with the proposed neuroadaptive control scheme, not only stable system operation is maintained but also each objective function is confined within the prespecified region, which could be asymmetric and time-varying. The effectiveness of the algorithm is validated via simulation on speed regulation of extruding machine in tire production lines.

Neural Adaptive Fault Tolerant Control for High Speed Trains Considering Actuation Notches and Antiskid Constraints

Automatic operation of high speed trains (HSTs) requires dedicated control schemes to tackle uncertain dynamics, unknown resistive forces, coupling nonlinearities, interactive in-train forces, unexpected disturbances, and faults. This paper addresses the problem of position and velocity tracking control of HSTs with multiple vehicles connected through elastic couplers. A neuro-adaptive fault tolerant control scheme is developed to compensate the input nonlinearities due to traction or braking notches, uncertain impacts from in-train forces, resistive aerodynamic drag forces, traction or braking faults, and adherence-antiskid constraints. High precision velocity and position tracking is achieved by using the proposed control scheme that combines the robust adaptive control with nonlinearly layered neural networks. Closed-loop stability is ensured with strict mathematical analysis. The effectiveness of the proposed approach is also validated through numerical simulations with considering the adherence-antiskid constraints.

Neuroadaptive Fault-Tolerant Control of Nonlinear Systems Under Output Constraints and Actuation Faults.

In this paper, a neuroadaptive fault-tolerant tracking control method is proposed for a class of time-delay pure-feedback systems in the presence of external disturbances and actuation faults. The proposed controller can achieve prescribed transient and steady-state performance, despite uncertain time delays and output constraints as well as actuation faults. By combining a tangent barrier Lyapunov-Krasovskii function with the dynamic surface control technique, the neural network unit in the developed control scheme is able to take its action from the very beginning and play its learning/approximating role safely during the entire system operational envelope, leading to enhanced control performance without the danger of violating compact set precondition. Furthermore, prescribed transient performance and output constraints are strictly ensured in the presence of nonaffine uncertainties, external disturbances, and undetectable actuation faults. The control strategy is also validated by numerical simulation.

Neuro-adaptive fault-tolerant control of high speed trains under traction-braking failures using self-structuring neural networks

This paper develops an adaptive control scheme for position and velocity tracking control of high speed trains under uncertain system nonlinearities and actuator failures. Neural networks with self-structuring capabilities are integrated into control design, where the number of the neurons can be adjusted online automatically, so that not only the problem inherent in the NN with fixed structure is avoided, but also the negative impacts arising from nonlinear in-train forces, traction/braking uncertain dynamics as well as the unknown actuation faults are effectively attenuated. It is shown that the resultant control algorithms are able to achieve high precision train speed and position tracking under varying operation railway conditions, as validated by theoretical analysis and numerical simulations.

Data-Based Fault-Tolerant Control of High-Speed Trains With Traction/Braking Notch Nonlinearities and Actuator Failures

This paper investigates the position and velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers. A dynamic model reflecting nonlinear and elastic impacts between adjacent vehicles as well as traction/braking nonlinearities and actuation faults is derived. Neuroadaptive fault-tolerant control algorithms are developed to account for various factors such as input nonlinearities, actuator failures, and uncertain impacts of in-train forces in the system simultaneously. The resultant control scheme is essentially independent of system model and is primarily data-driven because with the appropriate input-output data, the proposed control algorithms are capable of automatically generating the intermediate control parameters, neuro-weights, and the compensation signals, literally producing the traction/braking force based upon input and response data only--the whole process does not require precise information on system model or system parameter, nor human intervention. The effectiveness of the proposed approach is also confirmed through numerical simulations.