CHAOS CONTROL AND SYNCHRONIZATION USING SYNERGETIC CONTROLLER WITH FRACTIONAL AND LINEAR EXTENDED MANIFOLD

In this manuscript, for the first time, a fractional-order manifold in a synergetic approach using a fractional order controller is introduced. Furtheremore, in the synergetic theory a macro variable is expended into a linear combination of state variables. An aim is to increase the convergence rate as well as time response of the whole closed loop system. Quality of the proposed controller is investigated to control and synchronize a nonlinear chaotic Coullet system in comparison with an integer order manifold synergetic controller. The stability of the proposed controller is proven using the Lyapunov method. In this regard stabilizing control effort is yielded. Simulation result confirm convergence of states towards zero. This is achieved through a control effort with fewer oscillations and lower amplitude of signls which confirm feasibility of the control effort in practice. KEYWORDS:  synergetic control theory; fractional order system; synchronization; nonlinear chaotic Coullet system; chaos contro

Using Fractional Calculus for Cooperative Car-Following Control

International audienceThe Cooperative Adaptive Cruise Control (CACC) is one of the most promising aiding systems to improve traffic flow in highways. When it comes to design a proper control algorithm, robustness against non-modeled dynamics and noise plays a key role not only for improving controller performance but also for increasing the ability of handling heterogeneous vehicle strings. This paper proposes a fractional order controller that is able to deal with non-modeled dynamics whereas keeping simplicity and a low computational cost. System robustness and string stability responses are analyzed for a string of six vehicles, showing a good performance

One-shot data-driven design of fractional-order PID controller considering closed-loop stability: fictitious reference signal approach

A one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller is proposed. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable

Multiobjective Optimization Design of a Fractional Order PID Controller for a Gun Control System

Motion control of gun barrels is an ongoing topic for the development of gun control equipments possessing excellent performances. In this paper, a typical fractional order PID control strategy is employed for the gun control system. To obtain optimal parameters of the controller, a multiobjective optimization scheme is developed from the loop-shaping perspective. To solve the specified nonlinear optimization problem, a novel Pareto optimal solution based multiobjective differential evolution algorithm is proposed. To enhance the convergent rate of the optimization process, an opposition based learning method is embedded in the chaotic population initialization process. To enhance the robustness of the algorithm for different problems, an adapting scheme of the mutation operation is further employed. With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. The numerical simulation results show that the control system can rapidly follow the demand signal with high accuracy and high robustness, demonstrating the efficiency of the proposed controller parameter tuning method

Selfish Herd Optimisation based fractional order cascaded controllers for AGC study

In a modern, and complex power system (PS), robust controller is obligatory to regulate the frequency under uncertain load/parameter change of the system. In addition to this, presence of nonlinearities, load frequency control (LFC) of a Power System becomes more challenging which necessitates a suitable, and robust controller. Single stage controller does not perform immensely against aforesaid changed conditions. So, a novel non-integer/fractional order (FO) based two-stage controller incorporated with 2-degrees of freedom (2-DOF), derivative filter (N), named as 2-DOF-FOPIDN-FOPDN controller, is adopted to improve the dynamic performance of a 3-area power system. Each area of the power system consists of both non-renewable and renewable generating units. Again, to support the superior performance of 2-DOF-FOPIDN-FOPDN controller, it is compared with the result produced by PID, FOPID, and 2-DOF-PIDN-PDN controllers. The optimal design of these controllers is done by applying Selfish Herd Optimisation (SHO) technique. Further, the robustness of the 2-DOF-FOPIDN-FOPDN controller is authenticated by evaluating the system performance under parameter variation. The work is further extended to prove the supremacy of SHO algorithm over a recently published article based on pathfinder algorithm (PFA)

Data-driven fractional-order PID controller tuning for liquid slosh suppression using marine predators algorithm

Traditional control system development for liquid slosh problems often relies on model-based approaches, which are challenging to implement in practice due to the chaotic and complex nature of fluid motion in containers. In response, this study introduces a data-driven fractional-order PID (FOPID) controller designed using the Marine Predators Algorithm (MPA) for suppressing liquid slosh. The MPA serves as a data-driven tuning tool to optimize the FOPID controller parameters based on a fitness function comprising the total norms of tracking error, slosh angle, and control input. A motor-driven liquid container undergoing horizontal motion is employed as a mathematical model to validate the proposed data-driven control methodology. The effectiveness of the MPA-based FOPID controller tuning approach is assessed through the convergence curve of the average fitness function, statistical results, Wilcoxon's rank test, and the ability to track the cart's horizontal position while minimizing the slosh angle and control input energy. The proposed data-driven tuning tool demonstrates superior performance compared to other recent metaheuristic optimization algorithms across the majority of evaluation criteria

Fractional-Order-Based ACC/CACC Algorithm for Improving String Stability

International audienceTraffic flow optimization and driver comfort enhancement are the main contributions of an Adaptive Cruise Control (ACC) system. If communication links are added, more safety and shorter gaps can be reached performing a Cooperative-ACC (CACC). Although shortening the inter-vehicular distances directly improves traffic flow, it can cause string unstable behavior. This paper presents fractional-order-based control algorithms to enhance the car-following and string stability performance for both ACC and CACC vehicle strings, including communication temporal delay effects. The proposed controller is compared with state-of-the-art implementations, exhibiting better performance. Simulation and real experiments have been conducted for validating the approach

PMU-Based FOPID Controller of Large-Scale Wind-PV Farms for LFO Damping in Smart Grid

Due to global warming problems and increasing environmental pollution, there is a strong tendency to install and apply renewable energy power plants (REPPs) around the world. On the other hand, with the increasing development of information and communication technology (ICT) infrastructures, power systems are using these infrastructures to act as smart grids. In fact, future modern power systems should be considered as smart grids with many small and large scale REPPs. One of the main problems and challenges of the REPPs is uncertainty and fluctuation of electrical power generation. Accordingly, a suitable solution can be combination of different types of REPPs. So, the penetration rate of large-scale wind-PV farms (LWPF) is expected to increase sharply in the coming years. Given that the LWPFs are added to the grid or will replace fossil fuel power plants, they should be able to play the important roles of synchronous generators such as power low-frequency oscillation (LFO) damping. In this paper, an LFO damping system is suggested for a LWPF, based on a phasor measurement unit (PMU)-based fractional-order proportional–integral–derivative (FOPID) controller with wide range of stability area and proper robustness to many power system uncertainties. Finally, the performance of the proposed method is evaluated under different operating conditions in a benchmark smart system