Omar Hanif

Vehicle platooning is a cooperative driving scenario in which a set of consecutive, connected and autonomous vehicles travel at the same speed while controlling their inter-vehicular distance. Organising traffic in platoons of vehicles can mitigate issues in road transport by improving safety, energy efficiency, and road usage. Vehicle platooning scenarios are enabled by communication across the fleet, allowing the design of distributed controllers to impose cooperative vehicle motion. In contrast to initial control strategies tailored for specific network topologies, the last decade has witnessed a substantial increase in vehicle platooning control solutions that treat the cooperative platoon motion as the synchronisation of a network of dynamic systems, thereby enabling their use across a wider range of topologies. Despite numerous publications in recent years, the literature lacks a comprehensive survey of network synchronisation methods for vehicle platooning. To fill this gap, this paper aims to review network synchronisation strategies proposed for controlling the longitudinal motion of vehicle platoons over the period 2013–2025, with particular focus on contributions from 2018 onwards. The literature on network-synchronisation-based vehicle platooning methods is reviewed within a four-component framework. Then, the most widely used families of distributed consensus controllers are analysed, and the ways in which heterogeneity, nonlinearities, delays, packet drops, external disturbances, and cyber attacks are accounted for and mitigated are examined, along with different types of closed-loop stability. The review also surveys approaches from the literature for validating and assessing synchronisation algorithms in vehicle platoons, covering both experimental and simulation studies, as well as the related simulation platforms. The review paper concludes by presenting research trends and gaps, as well as potential future directions.

Fractional-order controllers have been shown to be an effective solution for improving the tracking performance of closed-loop control systems in various engineering applications. However, the use of fractional-order solutions has only been marginally investigated for controlling platoons of vehicles. Hence, this paper proposes three novel distributed fractional-order controllers, in which the vehicle platooning control problem of a set of homogeneous followers, characterised by either second- or third-order systems, is reformulated as a consensus control problem. The resulting closed-loop systems are analysed using the root locus approach to determine the region of control gains that ensures asymptotic closed-loop stability. Furthermore, the residual spacing errors to constant leader accelerations and disturbances are computed by analysing the error dynamics in the Laplace domain. The genetic algorithm is then employed for parameter optimisation within the stable region for different scenarios, and numerical analysis supports the theoretical findings and shows reduced tracking error when the fractional-order solutions replace their integer-order counterparts.

This paper takes up the challenge of modelling a complex nonlinear distillation column type-A and designs four optimal controllers for the same. The nonlinear plant is first linearized into a linear higher-order model. Thereafter, it is reduced into a lower-order model. Subsequently, the controllers are designed for the lower-order linear model. The controllers are designed by reducing the error cost functions, namely Integral Square Error (ISE), Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE). The process of optimization is done heuristically using the Genetic Algorithm method of tuning. The four controllers designed are Proportional Integral Derivative, Fractional-order Proportional Integral Derivative, Tilt Integral Derivative and Fractional-order Internal Model Control. A thorough comparison is made between the three designed controllers, first on the Single Input Single Output (SISO), then to the reduced-order linear model and finally to the main plant. The FO-IMC controller is shown as an exhibition controller designed using GA to demonstrate the novelty of its kind. It has also been compared, but the results show poor performances as it has not been tuned with the same parameters as the rest.

This work introduces a novel variant of controller having basic structure of Proportional Integral Derivative (PID) controller as PI ^x+iy D ^a+ib . The controller is termed as Complex Fractional-order Proportional Integral Derivative (CFOPID) controller, since it has orders of fractional and complex form. This controller has more parameters to tune than the other variants of PID controller known as Fractional-order PID (FOPID). The paper employs Genetic Algorithm based tuning method for determining the parameters of PID, FOPID and CFOPID controllers by minimizing the cost function in the form of weighted sum of error specifications (due to complexity of the structures of the latter two controllers, GA proves to be handy tool). The paper, further, simulates and compares the results of the three controllers based on servo, regulatory and stability performances on a standard second order plus time delay system. Henceforth, practical results of controllers are analyzed from a case study on DC servomotor. This research is based on tuning the three PID variants through the said technique and comparing them on their controlling performances.

The trajectory tracking for a non-holonomic mobile robot is addressed in this paper. Cascade control is implemented with a Sliding Mode Controller for the outer loop and a Proportional-Integral-Derivative controller governing the inner loop. Linear Quadratic Regulator is further applied to the outer loop to optimize the controller's effort. The model is further analyzed based on various output performances such as trajectory tracking, reference tracking of linear and angular velocities, sliding surfaces of the controller, and its robustness. A comparative analysis with and without an optimal approach has been illustrated. Simulations are carried out on a virtual robotic simulator in LabVIEW.

DC-DC converters are generally modeled using small-signal approximations considering small perturbations around a steady-state point. However, this is limited to a certain range of operation which in the presence of harmonic content in addition to dc offset might not provide an accurate result. Generalized Average modeling takes into account the harmonic contents along with the dc values and this can be used for alternating components such as inductor current in a Dual Active Bridge converter. The transfer function is derived which is an open-loop stable system, and in order to establish closed-loop stability, fractional order PI controller is used in this paper. Further, the optimization technique is applied in order to optimize the control variables as the degree of freedom has increased due to the incorporation of fractional powers. These optimized values are used to establish the closed-loop stable operation of a DAB converter and the technique is validated through MATLAB simulations. The entire results are shown in the figures with specifications tabulated and confirm the accurate setpoint tracking with the ripples in range.

Modeling of a DC-DC converters is usually carried via small-signal approximations with small disturbances taken in account at steady state. Although due to presence of harmonic content or DC offset, the latter’s modeling is restricted to certain range of operation. However, this is limited to a certain range of operation. This paper is a work based on modeling of Dual Active Bridge (DAB) type converters using Generalized Average Modeling. This type of procedure takes into account the harmonic as well as DC effect of the alternating current. After modeling the DAB, it’s open loop transfer function is derived. Thereafter for controlling, parameters of fractional order (FO) controllers namely Fractional order PID and Tilt Integral Derivative (TID) are synthesized using heuristic optimization technique also known as Genetic Algorithm. The use of FO controllers enables more controllability of the plant than classical integer order controllers. The paper then compares the result obtained after simulation of plant controlled by FOPID and TID controllers respectively. It discusses various Power as well as Control results and then concludes the topic by summarizing the procedure and highlighting the importance of FO controllers.

The increasing integration of the solar based system, EV’s(Electrical vehicles) with the existing conventional generation has led to required dc-dc converters for interfacing. For improve the system performance the DC-DC converters have to meet many requirements, such as high energy density, less ripple in the system, low EMI(Electromagnetic interference), compact size. Hence, a DC-DC interleaved CUK converter (ICC) is most suitable choice for meeting the requirements. This paper is based on the novel idea of modeling and controlling an interleaved CUK converter. Modeling is carried by linearizing the nonlinear model of the converter via average and small signal modelling and tuning of controller parameters is based on heuristic search technique known as Genetic Algorithm. The results show a better response time (rise time, settling time) and error specification in case of GA tuned controller than without controller. Hence, it can be concluded that this element (converter) coupled controller can give the desired results as demanded by the energy device.