This paper offers an improved finite time sliding mode controller scheme for a class of robotic manipulators with external disturbances. Since conventional sliding mode controllers have a discontinuous signum function, an important problem called chattering phenomenon can occur in them. The proposed scheme presents a new Lyapunov candidate functional containing an absolute function based on a fractional power of the switching surface such that the designed control law is continuous and smooth. The recommended control technique is designed using the Lyapunov stability theory and satisfies the presence of the sliding mode around designed switching surface in the finite time. The presented method eliminates the chattering problem produced by the switching controller and satisfies high precision action. Besides, the adaptive tuning controllers are designed to approximate the unknown bound of external disturbance. An extension of the proposed control technique based on the barrier function adaptive terminal sliding mode control is also suggested for better performance and robust tracking control of the nonlinear systems with external disturbances. Some simulation and experimental outcomes exhibit the efficacy of the planned technique.

This paper addresses the problem of continuoustime dynamic sliding mode control for Takagi'Sugeno fuzzy nonlinear systems based on an event-triggered mechanism. First, a novel sliding mode dynamic system is established by introducing a corresponding sliding mode function and an efficient eventtriggered mechanism. Then, based on a new Lyapunov function and the reciprocally convex method, sufficient conditions are presented to guarantee that the constructed network sliding mode dynamics is asymptotically stable. It is shown that the trajectories of the sliding mode dynamic system can converge into a bounded domain of a sliding mode surface in finite time. Furthermore, the explicit expression of the desired sliding mode controller is given in the form of linear matrix inequalities. Finally, the applicability and effectiveness of the proposed design techniques are demonstrated by a numerical example.

The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches.

Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially in critical maneuvering and cornering with variable speed. In this study, a new controller on the basis of interval type-3 (T3) fuzzy logic system (FLSs) is designed. The main novelties and advantages are as follows. (1) The uncertainty is a main challenge in the path-following problem of ARVs. However, in the fuzzy-based approaches, the bounds of uncertainty are assumed to be known. However, in the our suggested approach, the bounds of uncertainties are also fuzzy sets and type-3 FLSs with online adaptation rules are suggested to handle the uncertainties. (2) The approximation errors (AEs) and perturbations are investigated and tackled by the compensators. (3) The bounds of estimation errors are also uncertain and are estimated by the suggested adaptation laws. (4) The stability is ensured under unknown dynamics, perturbations and critical maneuvers. (5) Comparison with the benchmarking techniques and conventional fuzzy approaches verifies that the suggested path-following scheme results in better maneuver performance.

The remarkable properties of sliding mode control (SMC)—such as robustness, accuracy, and ease of implementation—have contributed to its wide adoption by the control community. To accurately compensate for parametric uncertainties, the switching part of the SMC controller should have gains that are sufficiently large to deal with uncertainties, but sufficiently small to minimize the chattering phenomena. Hence, proper adjustment of the SMC gains is crucial to ensure accurate and robust performance whist minimizing chattering. This paper proposes the design and implementation of an optimal fuzzy enhanced sliding mode control approach for a Stewart parallel robot platform. A systematic approach of designing the table of rules of the fuzzy system so as to provide the required coefficients of the sliding mode controller is proposed. The aim is to attain optimum performance and minimum control effort, thus eliminating the need for computationally expensive expert systems and yielding control outputs below the actuator saturation ranges. The proposed approach was validated using a six degrees-of-freedom Stewart platform subject to external disturbances. Its performance was compared to that of a standard SMC approach. The obtained results and comparative study showed that the proposed control algorithm not only reduces chattering, but also responds effectively to the realistic demands of control energy, while preventing actuator saturation.

This paper investigates the prescribed performance control problem associated with attitude stabilization of a rigid body, considering angular velocity constraint, actuator faults, and input saturation. The desired performance specifications in transient and steady-state phases including convergence speed, overshoot and steady-state value for attitude variable are also provided. To this end, the prescribed performance control methodology is combined with backstepping-based barrier Lyapunov function so as to develop a controller with simple structure compared to the existing constrained controls. The main idea behind the control design is to remove partial differential and complex function terms to considerably decrease complexity of the proposed controller. Moreover, a hyperbolic tangent function and an auxiliary system are employed to develop the constrained virtual rotation velocity control and to consider input saturation. The simulation results carried out on a rigid spacecraft confirm efficiency and applicability of the proposed constrained attitude control method.