This article proposes a new intelligent control scheme that uses the Fuzzy Super Twisting Sliding Mode Concept (FSTSMC) and PID controller tuned with the Artificial Bee Colony (ABC) algorithm to control a full vehicle active suspension system with new convergence proof. Suspension systems are utilized to provide vehicle safety and improve comfortable driving. The effects of road roughness transmitted by tires to the vehicle body can be reduced by using suspension systems. In this work super twisting sliding mode is combined with a fuzzy system to design a robust control method. The super twisting sliding mode concept is utilized to limit and minimize the chattering problem and the fuzzy system is used for estimating the unknown parameters and uncertainty in the suspension system components (spring, damper, and actuator). The advantage of such combination is that it can handle the uncertainties and nonlinearities efficiently. The PID controller is used to create the

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This article presents the design of super twisting sliding mode control (STSMC) based on radial basis function neural network (RBFNN) for path tracking of two link robot manipulator. The proposed controller is utilized to guarantee and achieve that the surface of sliding can be in equilibrium point within a short time and avoid the problem of chattering at the output. The Lyapunov theory is used in presenting a new convergence proof. Also, the particle swarm optimization (PSO) algorithm is employed to give the optimal parameter values of the proposed controller. Simulation results explain the goodness of the proposed control method for trajectory tracking of 2-link robot manipulator when compared with SMC strategy. Results demonstrate that the the percentage improvement in mean square error (MSE) of using STSMC when compared with the standard SMC are 15.36%, 16.94% and 12.92%, for three different cases respectively.