A composite PD and sliding mode neural network (NN)-based adaptive controller, for robotic manipulator trajectory tracking, is presented in this paper. The designed neural networks are exploited to approximate the robotics dynamics nonlinearities, and compensate its effect and this will enhance the performance of the filtered error based PD and sliding mode controller. Lyapunov theorem has been used to prove the stability of the system and the tracking error boundedness. The augmented Lyapunov function is used to derive the NN weights learning law. To reduce the effect of breaching the NN learning law excitation condition due to external disturbances and measurement noise; a modified learning law is suggested based on e-modification algorithm. The controller effectiveness is demonstrated through computer simulation of cylindrical robot manipulator.
This paper presents a low-cost Brushless DC (BLDC) motor drive system with fewer switches. BLDC motors are widely utilized in variable speed drives and industrial applications due to their high efficiency, high power factor, high torque, low maintenance, and ease of control. The proposed control strategy for robust speed control is dependent on two feedback signals which are speed sensor loop which is regulated by Sliding Mode Controller (SMC) and current sensor loop which is regulated by Proportional-Integral (PI) for boosting the drive system adaptability. In this work, the BLDC motor is driven by a four-switch three-phase inverter emulating a three-phase six switch inverter, to reduce switching losses with a low complex control strategy. In order to reach a robust performance of the proposed control strategy, the Lévy Flight Distribution (LFD) technique is used to tune the gains of PI and SMC parameters. The Integral Time Absolute Error (ITAE) is used as a fitness function. The simulation results show the SMC with LFD technique has superiority over conventional SMC and optimization PI controller in terms of fast-tracking to the desired value, reduction speed error to the zero value, and low overshoot under sudden change conditions.
Induction Motors have been used as the workhorse in the industry for a long time due to its easy build, high robustness, and generally satisfactory efficiency. However, they are significantly more difficult to control than DC motors. One of the problems which might cause unsuccessful attempts for designing a proper controller would be the time varying nature of parameters and variables which might be changed while working with the motion systems. One of the best suggested solutions to solve this problem would be the use of Sliding Mode Control (SMC). This paper presents the design of a new controller for a vector control induction motor drive that employs an outer loop speed controller using SMC. Several tests were performed to evaluate the performance of the new controller method, and two other sliding mode controller techniques. From the comparative simulation results, one can conclude that the new controller law provides high performance dynamic characteristics and is robust with regard to plant parameter variations.
The control problem for a class of a nonlinear systems that contain the coupling of unmeasured states and unknown parameters is addressed. The system actuation is assumed to suffer from unknown dead zone nonlinearity. The parameters bounds of the unknown dead zone to be considered are unknown. Adaptive sliding mode controller, unmeasured states observer, and unknown parameters estimators are suggested such that global stability is achieved. Simulation for a single link mechanical system with unknown dead zone and friction torque is implemented for proving the efficacy of the suggested scheme.
This work presents a Fuzzy based adaptive Sliding Mode Control scheme to deal with control problem of full vehicle active suspension system and take into consideration the nonlinearities of the spring and damper, unmodeled dynamics as well as the external disturbances. The control law of fuzzy based adaptive Sliding Mode Control scheme will update the parameters of fuzzy sliding mode control by using the stability analysis of Lyapunov criteria such that the convergence in finite time and the stability of the closed loop are ensured. The proposed control scheme consists of four similar subsystems used for the four sides of the vehicle. The sub control scheme contains two loops, the outer loop is built using sliding mode controller with fuzzy estimator to approximate and estimate the unknown parameters in the system. In the inner loop, a controller of type Fractional Order PID (FOPID) is utilized to create the required actuator force. All parameters in the four sub control schemes are optimized utilizing Artificial Bee Colony (ABC) algorithm in order to improve the performance. The results indicate the effectiveness and good achievement of the proposed controller in providing the best ability to limit the vibration with good robustness properties in comparison with passive suspension system and using sliding mode control method. The controlled suspension system shows excellent results when it was tested with and without typical breaking and bending torques.