Novel Hybrid Interval Type-2 Fuzzy Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems

A Novel hybrid backstepping interval type-2fuzzy adaptive control (HBT2AC) for uncertain discrete-time nonlinear systems is presented in this paper. The systems are assumed to be defined with the aid of discrete equations with nonlinear uncertainties which are considered as modeling errors and external unknown disturbances, and that the observed states are considered disturbed. The adaptive fuzzy type-2 controller is designed, where the fuzzy inference approach based on extended single-input rule modules (SIRMs) approximate the modeling errors, non-measurable states and adjustable parameters are estimated using derived weighted simplified least squares estimators (WSLS). We can prove that the states are bounded and the estimation errors stand in the neighborhood of zero. The efficiency of the approach is proved by simulation for which the root mean squares criteria are used which improves control performance.

L1 neural network adaptive fault-tolerant controller for unmanned aerial vehicle attitude control system

In this article, a L1 neural network adaptive fault-tolerant controller is exploited for an unmanned aerial vehicle attitude control system in presence of nonlinear uncertainties, such as system uncertainties, external disturbances, and actuator faults. A nonlinear dynamic inversion controller with sliding mode control law is designed as the outer-loop controller to track the attitude angles quickly and accurately which reduces dependence on model accuracy. A L1 neural network adaptive controller of the inner loop is introduced to compensate the nonlinear uncertainties and have a good attitude tracking. The radial basis function neural network technique is introduced to approximate a lumped nonlinear uncertainty and guarantee the stability and transient performance of the closed-loop system, instead of converting it to a half-time linear system by the parametric linearization method. Simulation results demonstrate the effectiveness of the proposed controller.

Partial-State-Constrained Adaptive Intelligent Tracking Control of Nonlinear Nonstrict-Feedback Systems with Unmodeled Dynamics and Its Application

In this paper, an adaptive intelligent control scheme is presented to investigate the problem of adaptive tracking control for a class of nonstrict-feedback nonlinear systems with constrained states and unmodeled dynamics. By approximating the unknown nonlinear uncertainties, utilizing Barrier Lyapunov functions (BLFs), and designing a dynamic signal to deal with the constrained states and the unmodeled dynamics, respectively, an adaptive neural network (NN) controller is developed in the frame of the backstepping design. In order to simplify the design process, the nonstrict-feedback form is treated by using the special properties of Gaussian functions. The proposed adaptive control scheme ensures that all variables involved in the closed-loop system are bounded, the corresponding state constraints are not violated. Meanwhile, the tracking error converges to a small neighborhood of the origin. In the end, the proposed intelligent design algorithm is applied to one-link manipulator to demonstrate the effectiveness of the obtained method.

Saturated RISE Feedback Control for Uncertain Nonlinear MacPherson Active Suspension System to Improve Ride Comfort

A continuous saturated controller using smooth saturation functions is established for MacPherson active suspension system which includes nonlinear uncertainties, unknown road excitations, and bounded disturbances. The developed controller exploits the properties of the hyperbolic functions to guarantee saturation limits are not exceeded, while stability analysis procedures of the robust integral of the sign of the error (RISE) control technique utilize the advantages of high gain control strategies in compensating for unknown uncertainties. The saturated controller guarantees asymptotic regulation of the sprung mass acceleration to improve the ride comfort despite model uncertainties and additive disturbances in the dynamics. Simulation results demonstrate the improvement in the ride comfort while tire deflection and the suspension deflection are within admissible range in comparison with three other suspensions.

Robust Time-Varying Output Formation Control for Swarm Systems with Nonlinear Uncertainties

Time-varying output formation control problems for high-order time-invariant swarm systems are studied with nonlinear uncertainties and directed network topology in this paper. A robust controller which consists of a nominal controller and a robust compensator is applied to achieve formation control. The nominal controller based on the output feedback is designed to achieve desired time-varying formation properties for the nominal system. And the robust compensator based on the robust signal compensator technology is constructed to restrain nonlinear uncertainties. The time-varying formation problem is transformed into the stability problem. And the formation errors can be arbitrarily small with expected convergence rate. Numerical examples are provided to illustrate the effectiveness of the proposed strategy.