Improving sliding mode control by using model predictive, fuzzy logic, and integral augmented techniques for aerial vehicle model

Multi-input sliding mode control (SMC) is a robust controller that can be used to control linear and nonlinear plants to achieve desired performance in the presence of uncertainty and disturbance. Moreover, its stability is proven by Lyapunov’s theorem. In practical applications, SMC suffers from problems such as chattering, which increase the control effort that may lead to instability of the system. In addition, the SMC parameters ars off-line and can not be optimized. Improvement of SMC has been investigated by many researchers. One important suggested method, which can update some SMC parameters online, is model predictive sliding mode control (MPSMC) achieved by merging SMC and model predictive control (MPC). This approach is also confronted with some problems especially due to complicated calculations and conservative strategy of nonlinear MPC for a nonlinear system at each sampling time. This thesis relates to improvement of sliding mode controller performance by introducing a new strategy to merge SMC with linear MPC and fuzzy logic control (FLC). Boundary layer and integral augmented are also exploited. Two different helicopter models are considered for testing under different controllers. The first is a two-degree-of-freedom (2-DOF) helicopter as a nonlinear high coupling 2-input 2-output laboratory experimental helicopter with motions in the pitch and yaw directions controlled by improved SMC controller. In this case, the results are compared with those of the PID controller based on the linear quadratic regulator algorithm (LQR-PID). The second one is a nonlinear quadrotor helicopter model as a four-rotor six-degree-of-freedom (6-DOF) helicopter which is a kind of autonomous unmanned aerial vehicle (UAV) system. The results of improved SMC are compared with those of an integral predictive nonlinear H∞ control for this system. Moreover, a cart moving on a plane is considered for comparing the new suggested controller with model predictive integral sliding mode control. The results reveal that the new merge of SMC with boundary layer (ISMC-BL),MPC, and FLC is an improved method for input tracking, optimization, and disturbance rejection performance for various applications namely the 2-DOF helicopter, the 6-DOF quadrotor helicopter, and the cart moving on a plane. The main outcome of this research is the introduction of a new robust, stable, optimal, and intelligent control scheme which is a multi-input model predictive fuzzy integral sliding mode with boundary layer (MPFISMC-BL). In this approach, a linear MPC, which considers constraints and cost function for optimal control performance at each sampling time, is used to design switching gains of control law. Moreover, equivalent control of MPFISMC-BL deals with nonlinearity of the system. Besides, FLC is used to calculate the slope of sliding surface as an intelligent tool based on fuzzy rules.

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