فهرست مطالب mohammad haddad-zarif

This work is trying to introduce a fractional order floated pole controller as a fast and robust approach. We designed a robust variable structure control that yields a continuous and constrained control signal, also a fast response in the presence of model uncertainties and external disturbances. In the proposed controller, we employed the pole placement algorithm, then by designing proper polynomials gave it robust property, then due to a simple optimization routine, we make it fast and faster within the stability region. Finally, to evaluate the proposed method, numerical examples in different situations of the presence of noise, disturbance, and model uncertainties, also comparative results are presented. This paper proposed an accurate, fast, and robust controller. This can improve the performance of the perturbed functional systems used in the industrial fields. It is proposed to spread the benefit of fractional calculus in the control of complex systems in practical situations.

This paper presents a novel event-triggered predictive control (ETPC) approach for the stabilization of discrete-time output-feedback networked control systems (NCSs). The studied NCS is considered to be subject to both random external input and output disturbances, and network imperfections including random communication delay, random packet dropout, packet disorder, limitation of network bandwidth, and network resources. In the proposed algorithm, an observer-based event detector is designed for reducing the number of sent packets through the communication network using the estimated system states by the Luenberger observer. In this way, the system’s energy resources are saved and network-induced effects are skipped. A switched predictive controller with multiple gains are used to compensate for network-induced effects. Controller gains are designed compatible with different possible values of delays and packet dropouts. A novel augmented representation of the state-space equations of the system is derived to design observer gain and controller gains. The asymptotic stability of the system is guaranteed by designing the observer and controller based on the Lyapunov function through solving linear matrix inequalities (LMIs). Putting all the aforementioned points together has made the whole framework presented in this paper a comprehensive one. The effectiveness of the proposed approach is demonstrated by comparative simulation results.

This paper is concerned witha controller design method for Networked Control Systems (NCSs) with time-varying random delays.The proposed controller isan event-based controller and isable to effectively save thenetwork bandwidth and energy resources of the system in comparison with common control schemes while guaranteeing the stability of the system. The proposed controller switches between two main triggering schemes: event-triggered control scheme and self-triggered control scheme.The stability issue in this combinatorial method is also considered.The validity of the proposed algorithm is confirmed via simulation results and the results are well compared with results from exerting event-triggered and self-triggered control individually.

This paper presents designing an optimal adaptive controller for tracking control of robot manipulators based on particle swarm optimization (PSO) algorithm. PSO algorithm has been employed to optimize parameters of the controller and hence to minimize the integral square of errors (ISE) as a performance criteria. In this paper, an improved PSO using logic is proposed to increase the convergence speed. In this case, the performance of PSO algorithms such as an improved PSO (IPSO), an improved PSO using fuzzy logic (F-PSO), a linearly decreasing inertia weight of PSO (LWD-PSO) and a nonlinearly decreasing inertia weight of PSO (NDW-PSO) are compared in terms of parameter accuracy and convergence speed. As a result, the simulation results show that the F-PSO approach presents a better performance in the tracking control of robot manipulators than other algorithms.