In this paper, the authors propose a consensus algorithm for continuous-time multi-agent systems. Using the proposed algorithm, the states of all agents converge to solutions of a convex optimization problem. The constraint of the optimization problem is a condition for the consensus of the outputs, and the objective function is the sum of costs for steady-states of all agents. A Lagrange multiplier method is a hint to design the proposed algorithm. The algorithm is usable for multi-agent systems with a limitation of information exchanges.
This paper, studies cooperative control problems with a multi-UAV (Unmanned Aerial Vehicle) system expressed as a fourth-order system using a consensus-based algorithm. How to model a linearized model of UAVs like quadrotors as a fourth-order system is described, and then a formation control algorithm for the fourth-order system is proposed after formulating a problem. The proposed control law is based on a consensus algorithm, and a leader-follower structure is also applied to the control law so that the leader can provide the quadrotors with commands such as their desired states. And then, The study shows that the proposed control algorithm can guarantee accurate formation keeping when fundamental assumptions about the network composed of the multiple UAVs are satisfied. Finally, the proposed approach is validated by some simulations.
In this paper, the author considers the problem of designing H∞ formation control of multi-agent systems that are composed of distributed parameter systems of hyperbolic type. Although distributed parameter systems are infinite dimensional processes, the proposed control schemes are constructed from finite dimensional compensators. The control strategies are derived as solutions of certain H∞ control problems, and we can regard the artificial error terms in the potential functions and the effects of neglected infinite-dimensional modes as external disturbances to the processes. It is shown that the desirable formations are achieved approximately via adaptation schemes.
For autonomous robots, the ability to manipulate unknown objects is pivotal to successfully operate in a complex environment. This paper presents a novel multi lighting two-step method for unknown objects segmentation using albedo characteristics of object shape. The authors address the task of detecting unknown multi colored objects of interest, specifically ones that are being seen for the first time and located in the proximity of unidentified obstacles. Illumination at different wavelengths and angles are projected by a robot, thus acquiring additional information about the scene and exploiting it for successful unknown objects segmentation. By analyzing shades and reflections of a scene's objects, we were able to form and identify true edges and visually separate items of interest. The proposed method does not require predefined models of target objects and assumes no previously assigned targets pose. The technique was validated by presenting promising experimental results using an autonomous robot manipulator successfully performing unknown objects segmentation from cluttered environments.
To control a compass biped robot walking in a complex environment, it is necessary to adjust its step length and walking speed for every step. Therefore, the computation time to calculate the suitable gait for each step, which is called on-demand computation time in this paper, should be short enough to walk continuously. Recently, the double generating functions method for finite time linear quadratic optimal control problems was proposed, whose advantage is that it can generate a parametrization of optimal trajectories for different boundary conditions and different time periods. It is very useful to the on-demand computation of optimal gait for the real biped robots walking in a complex environment. This paper applies the double generating functions method to a compass biped robot walking on the level ground. Considering the energy consumption, we generate a family of reference optimal gaits and inputs for different boundary conditions (the step length and the walking speed) for the linearized model by employing the double generating functions method. The simulation result shows that the modeling error caused by the linear approximation is small enough when the compass biped robot walks with a suitable step length and walking speed. This implies that the optimal gaits and inputs for the linearized system can be used as the optimal gaits and inputs for the original nonlinear system.
We consider the problem of optimal design of smoothing splines with constraints. Main concern is on constraints on derivatives over intervals as arising in monotone splines. The splines are constructed using normalized uniform B-splines as the basis functions. The authors show that the l-th derivative of the spline of degree k is obtained by using B-splines of degree k-l with the control points computed as the l-th difference of original control point sequence. This yields systematic treatment of equality and inequality constraints over intervals on derivatives of arbitrary degree, and the problem is formulated as convex quadratic programming. Thus the method is useful in various applications. The effectiveness is demonstrated by numerical examples of approximations of probability distribution function and concave function, and trajectory planning with the constraints on velocity and acceleration.
Many of practical design specifications are provided by finite frequency properties described by inequalities over restricted finite frequency intervals. In this paper, the authors consider a characterization of the finite frequency domain inequalities (FFDIs) for n-dimensional systems from a view point of a dissipation theory using quadratic differential forms (QDFs), which are useful algebraic tools for the dissipation theory based on the behavioral approach. The QDFs allow us to derive a clear characterization of the FFDIs using some inequality analogous to dissipation inequality with a compensation rate and an inequality of an integral of the supply rate with a matrix integral quadratic constraint as a main result. This characterization leads to a physical interpretation in terms of the dissipativity for subbehavior with some rate constraints. The authors also show how to resolve a difficulty on the expression of a compensation rate peculiar to n-dimensional systems. The results of this paper can be regarded as a finite frequency version for the characterizations of frequency properties over the entire frequency domain due to Pillai and Willems (2002).
This paper presents an improved Incremental Conductance - Maximum Power Point Tracking (INC-MPPT) algorithm based on fuzzy logic for photovoltaic (PV) systems. The demonstrative PV system consists of the solar array with a nominal power of 320W, a non-inverting buck-boost converter, and a resistive load. The PV system's objective is to seek efficiently the maximum power point (MPP) of the solar array in varying weather conditions. To do this, the proposed fuzzy-based INC-MPPT algorithm is designed with two sub-controllers. Wherein, in the first one, a novel fuzzy logic controller (FLC) is proposed to enhance the effectiveness of the conventional INC-MPPT. Its aim is determining rapidly and accurately the optimal voltage, where the solar array operates at the MPP. The other is a PI anti-windup controller, and it regulates the operating PV array's voltage to the optimal voltage computed in advance. Simulations show that the suggested algorithm fulfills well the listed goal even when the solar irradiance and temperature change suddenly. Furthermore, comparisons of simulation results, obtained from the presented algorithm, the conventional INC, and an existing fuzzy-based INC-MPPT, illustrate advantages of the proposed algorithm in terms of fast response speed, high accuracy, and small oscillation. The feasibility and efficacy of the suggested algorithm are also verified by experiments.