A novel application of a second-order sliding mode control (SMC) scheme to the air-fuel ratio (AFR) control of automobile internal combustion engines is developed in this paper. In this scheme, the sliding surface $S[x(t)]$ is steered to zero in finite time by using the discontinuous first-order derivative of a control variable $u̇c(t)$, and the corresponding actual control variable $uc(t)$ turns out to be continuous, which significantly reduces the undesired chattering. Its sliding gain is adjusted by a novel radial basis function network based adaptation method derived using the Lyapunov theory. It not only avoids handling the unavailable parameters and variables, but also saves the unnecessary manual adjusting time of the second-order SMC. The proposed method is applied to a widely used engine benchmark, the mean value engine model for evaluation. The simulation results show substantially improved AFR control performance compared with the conventional SMC.

This paper proposes a new fault tolerant control scheme for a class of nonlinear systems including robotic systems and aeronautical systems. In this method, a sliding mode control is applied to maintain system stability under the post-fault dynamics. A neural network is used as on-line estimator to reconstruct the change rate of the fault and compensate for the impact of the fault on the system performance. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimator is guaranteed to converge to the fault change rate, while the entire closed-loop system stability and tracking control is guaranteed. Compared with the existing methods, the proposed method achieved fault tolerant control for time-varying fault, rather than just constant fault. This greatly expands the industrial applications of the developed method to enhance system reliability. The main contribution and novelty of the developed method is that the system stability is guaranteed and the fault estimation is also guaranteed for convergence when the system subject to a time-varying fault. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results demonstrated that the post-fault is stable and the performance is maintained.

Fault detection and isolation have become one of the most important aspects of automobile design. A new fault detection and isolation scheme is developed for automotive engines in this paper. The method uses an independent radial basis function neural network model to model engine dynamics, and the modelling errors are used to form the basis for residual generation. Furthermore, another radial basis function network is used as a fault classifier to isolate occurred fault from other possible faults in the system by classifying fault characteristics embedded in the modelling errors. The performance of the developed scheme is assessed using an engine benchmark, the mean value engine model with Matlab/Simulink. Five faults have been simulated on the mean value engine model, including three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10–20% changes superimposed on the measured outputs of manifold pressure, manifold temperature and crankshaft speed sensors; the component fault considered is air leakage in the intake manifold; the actuator fault considered is the malfunction of fuel injector. The simulation results show that all the simulated faults can be clearly detected and isolated in dynamic conditions throughout the engine operating range.