Kumeresan A. Danapalasingam

Enhanced Backstepping Controller Design with Application to Autonomous Quadrotor Unmanned Aerial Vehicle

Quadrotor unmanned aerial vehicle (UAV) is an underactuated multi-input and multi-output (MIMO) system which has nonlinear dynamic behavior such as high coupling degree and unknown nonlinearities. It is a great challenge to design a quadrotor control system due to these features. In this paper, the contribution is focused on the backstepping-based robust control design of the quadrotor UAV. Firstly, the dynamic model of the aerial vehicle is mathematically formulated. Then, a robust controller is designed for the stabilization and tracking control of the vehicle. The developed robust control system comprises a backstepping and a proportional-derivative (PD) controller. Backstepping is a recursive design methodology that uses Lyapunov theorem which can guarantee the stability of the nominal model system, while PD control is used to attenuate the effects caused by system uncertainties. For the problem of determining the backstepping control parameters, particle swarm optimization (PSO) algorithm has been employed. In addition, the genetic algorithm (GA) technique is also adopted for the purpose of performance comparison with PSO scheme. Finally, the designed controller is experimentally evaluated on a quadrotor simulation environment to demonstrate the effectiveness and merits of the theoretical development.

Intelligent adaptive backstepping control for MIMO uncertain non-linear quadrotor helicopter systems

Designing a controller for multi-input–multi-output (MIMO) uncertain non-linear systems is one of the most important challenging works. In this paper, the contribution is focused on the design and analysis of an intelligent adaptive backstepping control for a MIMO quadrotor helicopter perturbed by unknown parameter uncertainties and external disturbances. The design approach is based on the backstepping technique and uses a radial basis function neural network (RBFNN) as a perturbation approximator. First, a backstepping controller optimized by the particle swarm optimization is developed for a nominal helicopter dynamic model. Then, the unknown perturbations are approximated based on the universal approximation property of the RBFNN. The parameters of the RBFNN are adjusted through online learning. To improve the control design performance further, a fuzzy compensator is introduced to eliminate the approximation error produced by the neural approximator. Asymptotical stability of the closed-loop control system is analytically proven via the Lyapunov theorem. The main advantage of the proposed methodology is that no prior knowledge of parameter uncertainties and disturbances is required. Simulations of hovering and trajectory tracking missions of a quadrotor helicopter are conducted. The results demonstrate the effectiveness and feasibility of the proposed approach.

Enhanced Device and Circuit-Level Performance Benchmarking of Graphene Nanoribbon Field-Effect Transistor against a Nano-MOSFET with Interconnects

Comparative benchmarking of a graphene nanoribbon field-effect transistor (GNRFET) and a nanoscale metal-oxide-semiconductor field-effect transistor (nano-MOSFET) for applications in ultralarge-scale integration (ULSI) is reported. GNRFET is found to be distinctly superior in the circuit-level architecture. The remarkable transport properties of GNR propel it into an alternative technology to circumvent the limitations imposed by the silicon-based electronics. Budding GNRFET, using the circuit-level modeling software SPICE, exhibits enriched performance for digital logic gates in 16 nm process technology. The assessment of these performance metrics includes energy-delay product (EDP) and power-delay product (PDP) of inverter and NOR and NAND gates, forming the building blocks for ULSI. The evaluation of EDP and PDP is carried out for an interconnect length that ranges up to 100 μm. An analysis, based on the drain and gate current-voltage (- and -), for subthreshold swing (SS), drain-induced barrier lowering (DIBL), and current on/off ratio for circuit implementation is given. GNRFET can overcome the short-channel effects that are prevalent in sub-100 nm Si MOSFET. GNRFET provides reduced EDP and PDP one order of magnitude that is lower than that of a MOSFET. Even though the GNRFET is energy efficient, the circuit performance of the device is limited by the interconnect capacitances.

A hybrid optimal backstepping and adaptive fuzzy control for autonomous quadrotor helicopter with time-varying disturbance

In this paper, a hybrid control system called as backstepping adaptive fuzzy control system, which integrates nominal and compensation controller is developed for autonomous quadrotor helicopter with inherent time-varying disturbance. In this hybrid control system, the nominal controller based on backstepping technique is the main controller, and the compensation controller containing a fuzzy control approach is used to eliminate the effect of uncertainties caused by external disturbance. In addition, in order to relax the requirement of prior knowledge on the bound of external disturbance, an online adaptation law is derived. Asymptotical stability of the closed-loop control system is analytically proven via the Lyapunov theorem. For the problem of determining the backstepping control parameters, particle swarm optimization algorithm has been employed. Finally, the designed controller is experimentally evaluated on a quadrotor simulation environment to demonstrate the effectiveness and merits of the theoretical development.

Nonlinear control of an autonomous quadrotor unmanned aerial vehicle using backstepping controller optimized by particle swarm optimization

Quadrotor unmanned aerial vehicle (UAV) is an unstable nonlinear control system. Therefore, the development of a high performance controller for such a multi-input and multi-output (MIMO) system is important. The backstepping controller (BC) has been successfully applied to control a variety of nonlinear systems. Conventionally, control parameters of a BC are usually chosen arbitrarily. The problems in this method are the adjustment is time demanding and a designer can never tell exactly what are the optimal control parameters should be selected. In this paper, the contribution is focused on an optimal control design for stabilization and trajectory tracking of a quadrotor UAV. Firstly, a dynamic model of the aerial vehicle is mathematically formulated. Then, an optimal backstepping controller (OBC) is proposed. The particle swarm optimization (PSO) algorithm is used to compute control parameters of the OBC. Finally, simulation results of a highly nonlinear quadrotor system are presented to demonstrate the effectiveness of the proposed control method. From the simulation results it is observed that the OBC tuned by PSO provides a high control performance of an autonomous quadrotor UAV.

Power Harvesting in Wireless Sensor Networks and Its Adaptation With Maximum Power Point Tracking: Current Technology and Future Directions

Wireless sensor networks (WSNs) is one of the most effective tools in collecting data autonomously going as recently as 5–10 years ago. A low deployment and maintenance cost WSN is highly recognized as one of the more advanced Internet of Things networks that can be deployed for a series of purposes namely environmental and industrial monitoring due to the majority of such systems run on expendable power source that offers WSN with a limited service lifetime. The aim of this paper is to review existing renewable energy and prospective approaches in energy harvesting strategy as a means of having a sustainable and low maintenance operation of WSN. Additionally, recent maximum power point tracking (MPPT) of solar energy harvesting is thoroughly discussed in a new perspective of the WSN framework. Semi-pilot cell fractional open-circuit voltage (SPC-FOCV) MPPT is a fairly new concept in WSN application that features less complicated configuration with reduced hardware requirements and lower cost. Recent research findings are evaluated throughout this paper leading to the SPC-FOCV MPPT materialization. A holistic discussion is made encompassing the advantages and disadvantages of the concept, its performance compared to conventional MPPT approaches and the future insight of the technology in WSN.

Fuzzy supervisory backstepping controller for stabilization of quadrotor unmanned aerial vehicle

In this paper, a fuzzy supervisory backstepping controller (FSBC) is designed for the altitude and attitude stabilization of quadrotor unmanned aerial vehicle (UAV). The designed controller consists of a backstepping controller which can automatically select its parameters, on-line by a fuzzy supervisory mechanism. The stability criterion for the stabilization of the quadrotor is proven by the Lyapunov theorem. Numerical simulations using the dynamic model of a four degree of freedom (DOF) quadrotor UAV show the effectiveness of the approach. Besides, the simulation results indicate that the proposed design techniques can stabilize the quadrotor UAV with better performance than established linear design techniques.

Backstepping controller with intelligent parameters selection for stabilization of quadrotor helicopter

backstepping controller (IBC) is designed for the quadrotor altitude and attitude stabilization in the existence of external disturbances and measurement noise. The designed controller consists of a backstepping controller which can automatically select its parameters on-line by a fuzzy supervisory mechanism. The stability criterion for the stabilization of the quadrotor is proven by the Lyapunov theorem. Several numerical simulations using the dynamic model of a four degree of freedom (DOF) quadrotor helicopter show the effectiveness of the approach. Besides, the simulation results indicate that the proposed design techniques can stabilize the quadrotor helicopter with better performance than established linear design techniques.

GSA-based optimal backstepping controller with a fuzzy compensator for robust control of an autonomous quadrotor UAV

Purpose – The purpose of this paper is to propose a new approach for robust control of an autonomous quadrotor unmanned aerial vehicle (UAV) in automatic take-off, hovering and landing mission and also to improve the stabilizing performance of the quadrotor with inherent time-varying disturbance. Design/methodology/approach – First, the dynamic model of the aerial vehicle is mathematically formulated. Then, a combination of a nonlinear backstepping scheme with the intelligent fuzzy system as a new key idea to generate a robust controller is designed for the stabilization and altitude tracking of the vehicle. For the problem of determining the backstepping control parameters, a new heuristic algorithm, namely, Gravitational Search Algorithm has been used. Findings – The control law design utilizes the backstepping control methodology that uses Lyapunov function which can guarantee the stability of the nominal model system, whereas the intelligent system is used as a compensator to attenuate the effects cau...

Improved Fuel Economy of Through-the-Road Hybrid Electric Vehicle with Fuzzy Logic-Based Energy Management Strategy

Hybrid electric vehicle (HEV) provides drivers with uncompromised drivability while significantly reducing hazardous emissions. This is achieved through optimal power flow solution via energy management strategy (EMS) which efficiently handles energy distribution from the different energy sources of a HEV. In this paper, a through-the-road (TtR) HEV configuration with fuzzy logic-based EMS is proposed. Fuzzy logic is applied in the main control block of the vehicle with a pair of membership functions assisting the power flow controller to select the appropriate power distribution by the hybrid drivetrain based on available resources in real time. The EMS operates in hybrid mode blended control strategy to achieve minimum fuel consumption for the desired trip by prioritising the electrical drivetrain over the internal combustion engine (ICE) for power distribution to the wheels. A Simulink model was constructed in MATLAB® to represent the TtR HEV equipped with in-wheel motors (IWM) in the rear wheels. A fuzzy logic-based EMS controller has been synthesised. The power flow in the TtR HEV is decided based on current vehicle speed and the global discharge rate (GDR) value derived from the current state-of-charge (SOC) of the battery and remaining trip distance. The proposed controller performs well on standard drive cycles and offers up to 62% improvement in fuel consumption compared to the reference model which uses rule-based EMS. Comparisons against other published models are equally encouraging, especially on high average speed drive cycles with up to 19.8% improvements in fuel consumption.