John T. Economou

Energy conservation based fuzzy tracking for unmanned aerial vehicle missions under a priori known wind information

The aim of this work is to include the navigation step for the waypoint-based guidance of a UAV system and to illustrate aspects such as tracking of the reference trajectory under wind presence, while conserving total energy requirements. The mission is represented utilising graph theory tools. The mathematical modelling of an aircraft controlled by an actuator surface is presented in terms of simple analytic relationships in order to simulate the actual horizontal motion of the vehicle. Its equivalence with a Tagaki-Sugeno (T-S) fuzzy system is illustrated that can aid the control methodology involved. Additionally, the advantages of utilising such an analysis is also stressed. The model formulated is an error posture model, that depends on current and reference posture. The control law is designed through parallel distributed compensation (PDC) and the gains are computed with the help of linear matrix inequalities (LMIs). Hence stability for the system is also guaranteed provided that the state variables are bounded in a priori known compact space. Moreover the energy requirements are described. This article is contributing towards energy enhancing a UAV mission and generating safely-flyable trajectories to meet mission objectives. The control law used is calculated in the pre-flight planning and can be used in real time for any trajectory to be tracked under any environmental conditions. Provided that angular and linear velocities are bounded, the latter is feasible under the assumption that the magnitude of air speed is small compared to the ground velocity of the aerial vehicle. The methodology offers an improved visualisation to aid an analyst with the representation of a UAV mission through graph theory tools utilising energy requirements for the mission and fast computational schema using matrix analysis. A simulation example of a UAV deployed from a source to reach a destination node under windy conditions is included to illustrate the analysis. The reference trajectory used is a piecewise Bezier-Bernstein curve referred to as the Dubins path.

Modelling of skid steering and fuzzy logic vehicle ground interaction

Experimental results are applied in order to enable fuzzy logic modelling of the vehicle-ground interactions in an integrated manner. These results illustrate the complexity of modelling systematically the ground conditions and the necessity of using two variables in identifying the surface properties. Experimental results are used in the analysis from a single seat electric wheeled skid steer vehicle ARISTOS under steady-state conditions and a variety of motions and surfaces. The methodology suggested is validated for a variety of surfaces.

Fuzzy Logic control strategy for Fuel Cell/Battery aerospace propulsion system

This paper presents power control strategies for the propulsion of unmanned arial vehicle (UAV) which is driven by fuel cell/battery hybrid system. UAV propulsion system has different power requirements in order to complete its mission successfully. The different power stages in propulsion system introduce in flight taxing, take off, cruising and landing. The specific feature of the UAV is that power needed for the pay load which consists with sensors and communication devices is high compared with small manned aircraft. As the power sources, fuel cell has high energy density and battery has high power density which limited to short period. Fuel cells introduce challenges with dynamic load variations due to uncertain load requirements. These variations could cause the fuel cell to operate outside its optimum efficiency margin. In this paper, UAV Fuel Cell propulsion system is integrated with the battery source and controlled via a suitable Fuzzy Logic methodology also combined with an intelligent based approach for establishing the power converters duty-cycle modes of operation. Two power controllers are designed to adapt to the load variations and optimizes the fuel cell operation by utilizing the battery (Li-Ion) source in an effective manner. The intelligent based duty-cycle modes allows the power converters of fuel cell and battery to operate at the appropriate mode hence taking into account the systempsilas energy sources states of charge (SOC). Finally endurance of the Fuel cell powered hybrid UAV can improve significantly.

A Modular Power and Energy Management Structure for Dual-Energy Source Electric Vehicles

This paper presents a modular approach to the design and implementation of a power and energy management system (PEMS) for electric vehicles (EV). The model EV is powered by dual energy sources, consisting of batteries and ultracapacitors. Operation of the PEMS has been structured into modular hierarchical process shells. The energy management shell (EMS) handles the longer-term decisions of energy usage in relation to the longitudinal dynamics of the vehicle. The process within the power management shell (PMS) however handles the fast decisions to generate power split ratios between the batteries and ultracapacitors. Finally the power electronics shell (PES) handles the ultra fast switching functions that facilitate the active power sharing between the two sources. Within the EMS, we employ a fuzzy inference system (FIS) as an intelligent decision engine. Simulations are presented to exemplify the function of the PMS and EMS. The modular structure approach is design-implementation oriented, with the objective of contributing towards a more unified description of EV power and energy management

A neurofuzzy-controlled power management strategy for a series hybrid electric vehicle

This paper focuses on the design of the power management strategy as the key factor in improving the performance in terms of the efficiency, the range and the fuel consumption for a small-scale series hybrid electric vehicle. A complex hybrid vehicle system is considered, and a practically realisable and traceable neurofuzzy strategy for improving the vehicle efficiency is introduced. The method results in extending the vehicle’s range while deciding when to switch the internal-combustion engine on or off as a function of the state of charge of the battery and the electrical power produced from the generator. Consequently, the speed of the internal-combustion engine (i.e. the current produced) is determined as a function of the driving conditions. Suitable tests were performed in order to verify the effectiveness of the proposed strategy; the verification tests were carried out using a consolidated model which also includes real-world experimental vehicle data. The results show that, by using the proposed power management strategy, a good compromise between the efficiency, the range and the fuel consumption can be obtained in many practically useful driving conditions.

Comparison of steering geometries for multi-wheeled vehicles by modelling and simulation

This paper examines and compares two commonly used steering geometries, the Ackermann and skid steering, using mathematical models formulated in a nonlinear state space form. For the skid-steering case, a steering mapping technique has been developed, which allows transformation of the inputs from a steering voltage and voltage forward demand to appropriate wheel actuator voltages. A comparison of the two models is then made using a heuristic mapping algorithm.

Multi-agent load power segregation for electric vehicles

The complex load requirement of electric vehicles whether hybrid electric, all electric (EV) or fuel cell based can be fundamentally divided into propulsion and non-propulsion loads. Further segregation of the non-propulsion loads into multi-priority, multi-time constant electrical burdens presents a basis to classify these loads as multi agents within the vehicle power distribution network. This paper discusses the load demands of these agents in relation to the overall vehicle power demand. Due to sizing constraints of onboard energy storage systems, coupled by the requirement to meet momentary peak power needs, we investigate prioritising the activation of these agents. The approach and simulation result presented in this paper is an initial step towards ongoing investigations into agent based vehicular power and energy management schemes.

Control design for a mobile robot: a fuzzy LPV approach

Gain scheduled control is a very useful control technique for linear parameter-varying (LPV) and nonlinear systems. A disadvantage of gain-scheduled control is that it is not easy to design a controller that guarantees the global stability of the closed-loop system over the entire operating range from the theoretical point of view. Another disadvantage is that the interpolation increases in complexity as number of scheduling parameters increases. As an improvement, this paper presents a gain-scheduling control technique, in which fuzzy logic is used to construct a model representing an LPV mobile robot and to perform a control law. Linear matrix inequalities are then used to to design an fuzzy gain-scheduling controller that guarantees the global stability of the closed loop system over the entire operating range of the fuzzy model.

Takagi-Sugeno model synthesis of a quasi-linear parameter varying mobile robot

This paper presents the application of the Takagi-Sugeno (TS) model synthesis for a quasi-linear parameter varying (QLPV) four wheel differentially steered mobile robot. The focus of this paper is a mathematical description of the mobile robot model as a QLPV and application of the TS fuzzy logic framework which complements the QLPV approach. Using QLPV model data several local TS linear models were identified using recursively the subtractive clustering method with specified error criteria. The identified TS local models resulted in a family of LTI models, which are equivalent to the linearised QLPV models. The T-S blended model synthesis resulted in a close approximation to the non-linear model dynamics.

Mobile robot arm trajectory generation for operation in confined environments

This article gives a novel trajectory generation algorithm which is suitable for operating in confined and dangerous environments. A technique is developed which can, in real time, calculate the existence of a safe path for navigating a robotic manipulator arm between obstacles without collision and accurately generating an efficient path between them. A map of the environment is created in the control servo domain using existing environment data where each dimension of the map space represents one of the degrees of freedom of the manipulator. The map is a multi-dimensional space that represents the control ranges of the manipulator and which contains obstacles to be avoided. The start and desired locations of the arm end effector can be converted into this space so that a path can be generated between them. The space is split into a graph of nodes, and a Dijkstra’s shortest path algorithm is used to generate a safe trajectory. If a successful trajectory can be found, then the arm desired location is achievable and the list of nodes that make up the trajectory form a set of control requirements that can be followed to drive the arm to its desired geometry.