Search

This paper presents a novel control architecture for operational space control when the end effector or the robotic chain is kinematically constrained. Particularly, we address kinematic control of robots operating in the presence of obstacles such as point, plane, or barrier constraints imposed on a point on the manipulator. The main advantage of the proposed approach is that we are able to control the end-effector motion in the normal way using conventional operational space control schemes, and by re-writing the Jacobian matrix we also guarantee that the constraints are satisfied. The most challenging problem of obstacle avoidance of robotic manipulators is the extremely complex structure that arises when the obstacles are mapped from the operational space to joint space. We solve this by first finding a new set of velocity variables for a point on the robot in the vicinity of the obstacle, and on these new variables we impose a structure which guarantees that the robot does not hit the obstacle. We then find a mapping denoted the Constrained Jacobian Matrix from the joint variables to these new velocity variables and use this mapping to find a trajectory in joint space for which the constraints are not violated. We present for the first time the Constrained Jacobian Matrix which imposes a kinematic constraint on the manipulator chain and show the efficiency of the approach through experiments on a real robot.

This work presents an optical flow composite guidance strategy for UAV navigation in unknown outdoor environments while seeking a predefined goal point. The proposed guidance logic is based on the relative optical flow in the right, left, and center regions of image. The resulting guidance strategy command uses balance and inverse strategies to navigate through different obstacle scenarios. Simulation studies are carried out in 3 D environment created in Virtual Reality Modelling Languages toolbox, MATLAB ® . In terms of initial heading and control efforts, the proposed composed strategy shows a better performance as compared to individual balance and inverse strategies.

This paper reports and discusses the numerical controller design for an unconventional Micro Aerial Vehicle (MAV) having coaxial counter rotating propellers and lateral navigation mechanism of shifting the center of gravity of the MAV to tilt the rotor's plane and get a horizontal component of the thrust. Detailed Simulink model for the MAV's dynamics and the controller is presented and discussed. The model is supposed to be a way point command executer and an illustration of orbit and the point to point navigation command is given as a proof of concept.

In this work, a visual control system for magnetically-driven, automated protein crystal harvesting is proposed. The system consists of a rod-shaped microrobot, a magnetic actuation system and a visual control system. The rolling motion of the microrobot on a surface is induced by a rotating magnetic field. As the robot is submerged in a low Reynolds number liquid this motion creates a vortex above the robot which enables the non-contact transportation of protein crystals towards an extraction point. We present the micro-agent, the actuation system and the visual control system to achieve this automated procedure.

This paper addresses the trajectory optimization for precision landing of parafoil assisted high altitude recovery modules. The problem is approached from an optimal control perspective. A simplified kinematic model of the parafoil payload system is considered. The angular velocity of the parafoil in the horizontal plane serves as the control input to the system. A multi objective performance index is formulated so as to ensure that the system achieves flared landing as closely as possible to a target point or region from a specified initial state with minimum control effort. The resulting optimal control problem with control and terminal constraints is solved numerically using the direct multiple shooting method to get the optimal trajectories that minimizes the performance index function. Subsequently, the effect of terminal payload delivery objectives on the optimal trajectories is looked into. This study is performed with a broad objective for obtaining preliminary results for achieving autonomous precision landing for a real world implementation of a high altitude recovery system.

Motivated by practical applications in satellite formations and antenna arrays etc., the problem of targeting a linear array of point sources at one common point of interest is formulated and then solved using a novel distributed control strategy. These point sources are located collinearly and the two at the two ends orient readily to the target point. The other point sources have to rely on sensing the changes of the relative orientation angles of their nearest neighbors to adjust their own orientations; we rigorously prove that under our control law using only local information, their orientation lines will intersect at the same point of concurrency as the two point sources at the two ends. The crucial idea behind our designed control law is the intuitive argument from plane geometry that reducing the differences between the distances to the baseline of the pairwise intersection points of the orientation lines helps realizing concurrent targeting. This idea is further utilized to construct the key argument in our analysis about the boundedness and the exponential convergence speed of the orientation angles. Numerical simulations are used to validate the effectiveness of our control strategy.

As one of key technologies in photovoltaic converter control, Maximum Power Point Tracking (MPPT) methods can keep the power conversion efficiency as high as nearly 99% under the uniform solar irradiance condition. However, these methods may fail when shading conditions occur and the power loss can over as much as 70% due to the multiple maxima in I – P curve in shading conditions v.s. single maximum point in uniformly solar irradiance. In this paper, a Real Maximum Power Point Tracking (RMPPT) strategy under Partially Shaded Conditions (PSCs) is introduced to deal with this kind of problems. An optimization problem, based on a predictive model which will change adaptively with environment, is developed to tracking the global maxima and corresponding adaptive control strategy is presented. No additional circuits are required to obtain the environment uncertainties. Finally, simulations show the effectiveness of proposed method.

Landing on undisturbed surface of water is one of the most specific and complex elements of piloting seaplanes. The main reason for this is the complexity of visual assessment of altitude. Analysis showed that most of the existing methods and systems for contactless measurement of distance cannot be used for measuring flight altitude with required accuracy near water surface. Radio altimeters have the most suitable characteristics, however, for economic reasons, they are not installed on all seaplanes. Lack of accurate instrument information about the most important navigation parameter negatively affects the safety of flights. Above facts point to the importance of finding new methods of measuring flight altitude near undisturbed surface of water. For solving this problem, the paper focuses on analyzing the applicability of time-of-flight cameras, which are based on the Photonic Mixer Device (PMD) technology. The paper presents preliminary experimental results that confirm the possibility of using a static PMD camera for altitude measurement near undisturbed surface of water. Further, during landing the vertical velocity component and angular velocity of an aircraft do not have high values. The horizontal velocity component does not affect the view of range images in case of undisturbed water surface. Therefore an inference is made that PMD cameras can be used for measuring altitude of flight near undisturbed water surface.

The paper deals with the determination of controller parameters for multivariable systems by means of parameter optimization. In order to reduce the computational burden, the starting point in the optimization is determined through information provided by the feedback controller parameterized using the multivariable internal model controller (IMC) computed from the plant's p/q moment approximant and the original plant's multi-loop controller parameter magnitudes. Thereafter, controller parameter optimization proceeds to completion using the MATLAB optimization toolbox which is widely available. A potency of this technique is that all performance specifications and stipulated constraints are easily accommodated in the problem formulation thus facilitating complete problem solution in one go. The technique has been found to be effective for all plants considered so far and generally produces closed loop systems with favorable characteristics when compared to systems designed by similar methods.

A general approach for approximating the nonlinear dynamics of an aircraft model by a linear parameter varying (LPV) model of fixed structure (e.g. affine, polynomial, rational,…) is presented, which is a prerequisite for the application of several LPV based analysis and synthesis techniques. Starting from a given trim-point in the flight envelope, the approach tries to maximize the size of the region (flight envelope) around this trim point, for which the resulting LPV model describes the nonlinear model with sufficient accuracy. For this purpose the sensitivities of the model error with respect to an expansion of the flight envelope in the direction of specific parameters are used to get an optimal overall expansion strategy, which then yields a large validity region for the LPV model.

A new three-dimensional nonlinear path-following guidance law is proposed using differential geometry. The guidance law is designed based on the look-ahead angle and the radially shifted distance which gives an additional degree of freedom to generate acceleration command for precise path-following. A modified Frenet-Serret frame is utilized to describe the desired path, and the closest projection point is taken as a reference point on the desired path. To implement the guidance law for the vehicle in a flow-field, a command modifying logic is proposed to generate the sideways command which is orthogonal to the flow-relative velocity. Numerical simulations are performed to verify the performance of the proposed guidance law.

A model reduction for a compressible flow with combustion is performed using the POD-Galerkin procedure. The model parameters are determined by an optimization. The obtained reduced-order model is incorporated in a model predictive controller and results are shown from a closed-loop CFD-simulation. The controller influences the fuel mass-flow boundary condition to control the temperature at a certain point within the computational domain.

An effective reactive collision avoidance algorithm is presented in this paper for Unmanned Ariel Vehicles(UAVs) based on stereovision sensing of the obstacles. Vision sensors detection range is limited and state measurements are noisy. Unscented Kalman Filter(UKF) based sensor state estimation is proposed to extract the accurate information from the sensors through a geometrical formulation. This formulation is capable of estimating the range information of the obstacle. The first order differential of the nonlinear sensor dynamics is used to compute obstacle's velocity estimate. In the case of imminent collision, UAV is guided towards an 'Aiming point' which is computed for point obstacle with safety ball and using the collision cone approach. The velocity vector of the UAV is steered away towards this point by using 'Differential Geometry Guidance'(DGG). Simulations show that this guidance strategy is quite effective in avoiding popup obstacles within a very short time and hence can be useful for reactive collision avoidance.

It has been recognized that model predictive control (MPC) approach can be successfully applied to the signal control of the urban traffic systems. In this research, we mainly focus on dealing with the uncertainty of the inflow to the traffic network based on the MPC method. The stochastic process describing the uncertainty and chance constraints are embedded to the mathematic programming problem to prevent the congestion happening on the arteries. A modified MPC algorithm is also developed to solve the novel problem under studies. The simulation results show that the proposed model is closer to the real situation than the deterministic ones. The novel MPC algorithm strictly keeps the traffic flows below the limit of the arteries. Moreover, from computation point of view, the proposed method requires shorter computation time that may meet the real-time control requirement.

In this paper a guidance law has been proposed to solve applications related to coverage of a pre-defined area using a mobile robot. In many situations, it may not be possible to measure all the information due to practical limitations or from computational point of view. So, we have used only range information to solve the above specified problem. The target point is considered to be stationary in this particular case. Now, for any initial condition for which the velocity vector of the robot is perpendicular to the line of sight with respect to the stationary target, the autonomous agent displays interesting behaviours which have been analysed theoretically. All the analytical results obtained here have been validated through MATLAB simulations also.

In recent literature, explicit model predictive control (e-MPC) has been proposed to facilitate implementation of the popular model predictive control (MPC) approach to fast dynamical systems. e-MPC is based on multi-parametric programming. The key idea in e-MPC is to replace the online optimization problem in MPC by a point location problem. After locating the current point, the control law is simply computed as an appropriate linear function of the states. A variety of approaches have been proposed in literature for the point location problem. In this work, we present a novel approach based on linear machines for solving this problem. Linear machines are widely used in multi-category pattern classification literature for developing linear classifiers given representative data from various classes. The idea in linear machines is to associate a linear discriminant function with each class. A given point is then assigned to the class with the largest discriminant function value. In this work, we develop an approach for identifying such discriminant functions from the hyperplanes characterizing the given regions as in multi-parametric programming. Apart from being an elegant solution to the point location problem as required in e-MPC, the proposed approach also links two apparently diverse fields namely e-MPC and multi-category pattern classification. To illustrate the utility of the approach, it is implemented on a hypothetical example as well as on a quadruple tank benchmark system taken from literature.

In this article, we introduce the Integrated Route Planning Problem (IRPP) , which deals with the assignment of touristic itineraries, composed of activities characterized by their capacity and opening time, to visitors that may have variable time availabilities and different levels of preference for the activities. We show that the IRPP is challenging from the computational point of view, present a mathematical programming formulation of the problem and discuss aspects of a solution method based on the column generation technique. We also show how some tools of probabilistic planning and agent-based modeling may be used to address important aspects of the problem.

In this paper, we consider the problem of position regulation of a class of underactuated rigid-body vehicles that operate within a gravitational field and have fully-actuated attitude. The control objective is to regulate the vehicle position to a manifold of dimension equal to the underactuation degree. We address the problem using Port-Hamiltonian theory, and reduce the associated matching PDEs to a set of algebraic equations using a kinematic identity. The resulting method for control design is constructive. The point within the manifold to which the position is regulated is determined by the action of the potential field and the geometry of the manifold. We illustrate the performance of the controller for an unmanned aerial vehicle with underactuation degree two–-a quadrotor helicopter.

In this paper, we aim to determine the optimal dimensions of a flow rack automated storage and retrieval system (AS/RS) using a single machine for storage and retrieval operations. These optimal dimensions are evaluated for minimum expected travel times of the storage and retrieval machine. Enumeration technique is used on a large range of system configurations going from 100 to 20000 storage segments in the rack. Optimization is carried out on six travel time expressions dealing with single and dual cycle travel times. Two dwell point positions are considered. Finally numerical results are studied to investigate the influence of the travel time expressions on the optimal dimensions as well as the shape factor of the system rack.

The objective of this work is to use low precision laser sensor and create reasonably precise 3D reconstructions. The 3D reconstruction is executed by combining several point clouds obtained from different viewpoints. The proposed method was developed with three main steps: point cloud registration, error compensation and surface reconstruction. The ICP algorithm is improved to execute the point cloud registration: dynamic distance threshold, weighted distance, rigid body restriction and color information. It is shown that using this improved ICP, the number of point correspondences to evaluate the quadratic error converges to a value. The quadratic error can be determined independently of scene complexity. The point cloud errors are compensated using the consensus surface algorithm with signed distance. The surface is reconstructed using the marching cubes algorithm. Several results are shown to demonstrate the reliability of the proposed method.

In this paper, a merging path generation method based on model predictive control (MPC) method is proposed to optimize the merging point, and the merging path of the merging vehicle, while the motion of the main lane vehicle is optimized at the same time. To simplify the optimization problem which is used to generate the merging trajectory, the longitudinal movement of the merging vehicle is related to the lateral movement of it. To reproduce and make full use of the cooperative driving behavior in merging, the motions of the two relevant vehicles are optimized at the same time. A variable which enables the translation of the merging trajectory of the merging vehicle is introduced into the state of the system. So that when it is necessary to translate the merging trajectory of the merging vehicle for some reason, for example, keeping safe distance, the merging trajectory would be translated and thus the merging point can be optimized. In consideration of the upper bounds of the accelerations and lower bounds of the decelerations that actual vehicles can produce, during merging the accelerations and decelerations of both the relevant vehicles are restricted to appropriate ranges. A computer simulation of three typical merging cases, whose initial conditions are set according to the data drawn from actual merging scene, was conducted on a personal computer to verify the effectiveness of the proposed method. It is shown that the computer simulation results for all the three cases are reasonable. The computational time for all of the three cases is much shorter compare to the time step; therefore the proposed method is quite probable to be implemented on actual vehicles.

A parameter estimation method for an online flotation process simulator is described. The applications of an online process simulator include soft sensor implementations, process trajectory predictions and advisory feedback to the operator, which have potential to improve the process efficiency and minimize the detrimental effect of disturbances on the process or the environment. The online simulator uses a detailed and dynamic model of an actual industrial flotation process, and therefore accurately corresponds to the process phenomena present at the plant. Parameter estimation is required for the flotation kinetics in order for the simulator to adapt to changes in the process conditions. A relatively generic parameter estimation algorithm is developed and tested with a dual simulator setup. The particular requirements and limitations of adapting an online simulator are discussed, and modifications to well-known estimation algorithms are presented as a possible method of meeting the requirements and overcoming the limitations. The results show that online parameter estimation and simulation model tracking is possible with the chosen method, and point out areas of further development for application when the simulator is used alongside the real process.

The grasping and stabilization of a tumbling, non-cooperative target satellite by means of a free-flying robot is a challenging control problem, which has been addressed in increasing degree of complexity since 20 years. A novel method for computing robot trajectories for grasping a tumbling target is presented. The problem is solved as a motion planning problem with nonlinear optimization. The resulting solution includes a first maneuver of the Servicer satellite which carries the robot arm, taking account of typical satellite control inputs. An analysis of the characteristics of the motion of a grasping point on a tumbling body is used to motivate this grasping method, which is argued to be useful for grasping targets of larger size.

OpenMVL Project is considered by author as the starting point of discussion and the first executed step in the direction of the new standard for tools for modeling and simulation of complex dynamical systems (CDS). OpenMVLShell is the open source software for comparison different approaches to the problem of designing modern environments for CDS modeling and simulation first of all. However it may be used in Education too as its analog OpenModelica. OpenMVLShell is based on Object-Oriented Modeling (OOM) approach and uses Model Vision Language (MVL) suggested by MvStudiumGroup. MVL is object-oriented language for modeling and simulation of hierarchical multi-component systems with event-driven behavior based on differential-algebraic equations (hybrid systems). Possibilities of OpenMVLShell are illustrated by two examples. The first one demonstrates how to use OpenMVLShell for comparison different DAE solves. The second one illustrates using OpenMVLShell in “Modeling and Simulation” course as instrument of active learning.

Recently, physical modelling of human and animal organs has become very popular. The objective of this invention was to construct a physical lung model – an apparatus comprising an artificial lung and an automatic self-controlled system working on a principle of a negative feedback loop. This contribution is focused mainly on the function of the control system with a special attention paid to the sensor network. The control system is equipped with sensors measuring various physical variables (e.g. air flow, pressure difference) necessary for a proper system operation. Sampling frequency, sensorstype and their accuracy play an important part in the quality of measurement and directly affect the accuracy of the control response. The physical lung model and the sensor system have been successfully built and fully completed from the hardware and software's point of view. In the future, this lung model is expected to be used in different testing applications, e.g. in studies comparing physiological and pathological breathing patterns or observing effects of different aerosols on the lung tissue.

A new approach for controlling a system of multiple agents by choosing a time-varying density function is presented, employing optimal coverage ideas. In this approach, we specify a time-varying density function that represents where it is that want the agents to monitor, and how important it is for each point to be covered. A new algorithm is presented under which the agents track the time-varying density function while providing optimal coverage of the density function. Results from robot implementation show that the proposed algorithm guides the agents well over the chosen density functions, and that the effectiveness of the coverage is higher than other comparable algorithms.

An automatic control of a DRI plant has been performed in order to provide the best possible working point, to obtain the target level 2 process set points (DRI metallization, carburation, productivity) in function of Iron Ore and Reducing gas compositions. The target has been obtained by means of a Process Reconstruction Model (PRM) that provides continuous static estimation of plant measurements, efficiency analyses, virtual sensor and diagnostics. An Extended Kalman Filter (EKF) has been used to provide continuous robust dynamic estimations of the output process measurements. Finally the EKF feeds a Linear Quadratic Gaussian (LQG) regulator that performs, in real time, the calculation of Optimal Controls (minimum energy, maximum gas quality level 1 set points), in function of level 2 sets. The suite has been successfully applied to a DRI Energiron Plant.

Our purpose in this paper is to provide an introduction to direct adaptive control in infinite dimensions and to prove the remarkable simplicity of infinite dimensional adaptive control even in infinite dimensions. However, we will show that the mathematical foundations are much more stringent in infinite dimensional theory. Consequently, this paper will emphasize some important fundamentals and will not be comprehensive. Much more can be done, but here we hope to provide a starting point for further exploration. Given a linear continuous-time infinite-dimensional plant on a Hilbert space and disturbances of known and unknown waveform, we show that there exists a very simple stabilizing direct adaptive control law with certain disturbance rejection and robustness properties. The closed loop system is shown to be exponentially convergent to a neighborhood with radius proportional to bounds on the size of the disturbance. The plant is described by a closed densely defined linear operator that generates a continuous semigroup of bounded operators on the Hilbert space of states. To illustrate the use of the control law, we apply the result to a partial differential equation example of an unstable diffusion system, but other applications are possible.

This paper is concerned with the identification of nonlinear processes which are parameterized as linear parameter varying (LPV) models. The LPV models are represented as blended linear models by using ADAptive LINear Element (ADALINE) structure. Firstly, ADALINE is employed to represent local models describing the system's behaviour at each fixed working point. Then, a novel method is proposed to estimate parameters of weight functions such that the global LPV model can be built by interpolating the local ones. The adaptation algorithm is discussed and extensive simulation studies are conducted to verify the effectiveness of this method.

Analytical techniques are presented for the motion planning and control of a 10 degree-of-freedom biped walking robot. From the Denavit-Hartenberg method and Newton-Euler equations, joint torques are obtained in terms of joint trajectories and the inverse dynamics are developed for both the single-support and double-support cases. Physical admissibility of the biped trajectory is characterized in terms of the equivalent force-moment and zero-moment point. This methodology has been used to obtain stability of walking biped robot Archie developed in IHRT. A simulation example illustrates the application of the techniques to plan the forward-walking trajectory of the biped robot.

The paper analyzes a generalization of the Braess paradox, where adding a new connection in a road network may lead to delayed time of arrival due to violation of the balance in the traffic flow. Regulation by the police is introduced, which in fact conducts arbitrating over the preferences of the passengers. In this way, each predefined regulation is a discrete function on the time to pass through the road network. The domain of the task is shrunk in four stages. An optimal Nash solution is found by searching the agreement point that maximizes the Nash utility criterion. An illustrative example is constructed with 40 vehicles, divided into five groups depending on the utility functions of the passengers in each vehicle.

This paper presents a formation tracking strategy based on local potential functions and the leader-followers scheme for a group of point robots moving in the space. A leader robot is chosen to follow a prescribed trajectory whilst the rest, considered as followers, are formed in an open chain configuration. The formation convergence and the path following, including chaotic trajectories, are guaranteed if every follower robot measures exactly the velocity of the next robot and the leader knows the velocity of the marching path. Then, we analyze the preservation of the formation using approximations of the velocities of robots and the chaotic path. These restrictions appear in real implementations in robots equipped by position sensors only and where the velocities functions are approximated by online numerical methods.

In this paper the authors present a hexapod robot structure. For the leg has been developed a simple simulation interface to ease the analysis of the motion. The control part is an experimental one and uses Matlab and Arduino Duemilanove development board. The movement of the leg tip along a predefined trajectory was made using a walking algorithm. Moving the leg tip from one point to another is made in two steps: stance phase and swing phase. The communication protocol is also presented. In order to control the robotic leg the direct kinematical model and inverse kinematical model were used. Also the dynamic model using Lagrange method was developed and implemented using SimMechanics toolbox and ode45 function from Matlab. The paper includes some experimental results related to the leg control and some simulations regarding dynamic model.

This paper proposes a methodology to analyze steady-state voltage stability from the point of view of reactive power generation limits. Two main aspects are focused: 1) the identification of the critical points of reactive power generation as the power network loadability increases; and 2) the classification of these loadability levels from the point of view of bifurcation theory. It is shown how the formulation of the power flow equations in rectangular coordinates can be exploited, particularly to deal with the reactive power generation limits in order to find limit-induced bifurcation points. Simulation results obtained with the IEEE 24-bus test system are used to illustrate the performance of the proposed methodology.

In this paper, a fault tolerant control method for linear parameter varying (LPV) systems using a virtual actuator and a virtual sensor is proposed. The basic idea of the method is to insert a reconfiguration block, which consists of an LPV virtual actuator and an LPV virtual sensor, between the plant and the nominal controller such that the fault tolerant goal is achieved without re-designing the nominal controller. The role of the reconfiguration block is to transform the signals from the faulty system such that its behavior is similar to the nominal system from the point of view of the controller and to transform the output of the controller for the faulty system such that the stability and performance goals are preserved. In this paper, we consider the weak fault-hiding goal and stability of the closed loop system. Input to state stabilizing LPV gains of the virtual actuator and sensor are found by solving linear matrix inequalities (LMIs). We show that separate design of these gains guarantees the closed loop input to state stability of the closed loop reconfigured system.

In this paper we revisit the explicit MPC representation and related notions. We point to the special structure of the constraint matrices and exploit it in order to provide novel results. We give an upper bound for the collection of admissible active sets with use in the mixed integer representation of the KKT problem and a partial recursive description of the explicit partitioning of the MPC problem. The results are tested over illustrative examples.

Accurate control of the engine air system is essential to comply with emission legislation. Typically the real emission targets, like NO x , cannot be measured directly at a production car and thus different intermediate candidates, like gas mass flows or oxygen concentrations, are used. In general the candidate choice is a key factor for emissions, because each of them can only be determined with certain accuracy and consequently a feedback control based on a specific quantity is only capable of reaching a point within the accuracy range. Moreover, a slight deviation of one candidate may, due to the nonlinear relations between emissions and candidates, lead to high NO x deviations. In a previous work four candidates for EGR valve control were analyzed and a scheduling scheme according to their NO x tracking performance under uncertainties was proposed. Within this work the approach is extended by the application of a multi reference online MPC control strategy which takes also dynamical properties into account. To this end a scheduling between the candidates based on the static and dynamic analysis in the previous work is applied, where the idea is to adapt the inherited objective function during runtime. The proposed dynamic reference scheduling strategy was implemented on a simulation model of a passenger car Diesel engine and satisfying results were obtained.

Three-dimensional digital heat distribution maps are needed to assess the energy efficiency of real estates. The availability of such maps are of great importance for reducing the ecological footprint of houses, buildings, and factories. Designing estates has reached the point, where so-called Passivhaus buildings make extensive use of the intrinsic heat from internal sources such as waste heat from lighting, white goods, and other electrical devices, but without using dedicated heaters. In our approach for creating high-precise heat distribution maps a robot is equipped with a 3D laser scanner, a thermal camera, and a color camera. Data from all the sensors are combined to model the environment precisely. This paper describes the setup of the sensors and the processing of the acquired data, including the automatic co-calibration needed to fulfill this task.

We investigate the solvability of the reverse Stackelberg game. Here, a leader player acts first by presenting a leader function that maps the follower decision space into the leader decision space. Subsequently, the follower acts by determining his optimal decision variable. Such a game setting can be adopted within a multi-level optimization approach for large-scale control problems like road tolling. However, due to the complexity of the general game, results often rely on specific examples. As a starting point towards developing a systematic approach for the use of reverse Stackelberg games in control, a characterization of cases is given in which the desired leader equilibrium can be achieved by an affine leader function. Here, we focus on the single-leader single-follower deterministic, static (one-shot) case. This characterization follows a geometric approach and extends the special cases considered in the existing literature to also incorporate the more general case in which nonconvex and nonsmooth sublevel sets apply.

This paper studies the parameter-dependent characteristics of a gene switching model that consists of dual positive feedback loops. Developing a predictive mathematical model for large regulatory networks in biological systems would be useful for their analysis and design. A parameter estimation scheme is proposed based on the observation of the steady-states for a gene switching model. Deterministic and stochastic stability are studied for this model, as well as other important system behaviors such as convergence rate to a stable equilibrium point, hysteresis induced by two time scales of the system model, and noise sensitivity with respect to the system parameters. Sensitivity of system performance indices with respect to the system parameters are analyzed in terms of H ∞ - and H 2 -norms of the linearized system model with their closed-form solutions. The presented qualitative and quantitative studies of the system characteristics enable the synthesis of a robust gene regulatory network that achieves desired static and dynamic responses.

We propose a simple but efficient control strategy to manipulate objects of unknown shape, weight, and friction properties – prerequisites which are necessary for classical offline grasping and manipulation methods. Instead, the proposed control strategy employs estimated contact point locations, which can be obtained from modern tactile sensors with good spatial resolution. The feasibility of the strategy is proven in simulation experiments employing a physics engine providing exact contact information. However, to motivate the applicability in real world scenarios, where only coarse and noisy contact information will be available, we also evaluated the performance of the approach when adding artificial noise.

The chaotic systems have been studied starting from the early 1960s by the help of Lorenz system. Since then, other chaotic attractors, such as the Chua's system, the Lu's system and so on, have been developed. In 1999, based on an engineering feed-back anti-control approach, Chen provided a new system (see Chen (1999) for details). In this paper we will present some dynamical and geometrical properties of Chen's system from the Poisson geometry point of view.

In this paper, the robust control of constrained discrete-time linear time-varying or uncertain systems affected by bounded additive disturbance is considered. The main idea is to use an interpolation technique between several local unconstrained robust optimal controls. From the implementation point of view, the scheme is shown to be computationally attractive, at each time instant a quadratic programming (QP) problem being solved on-line. The proofs of recursive feasibility of the optimization procedure and ISS stability of the closed loop system are given.

An efficient potential flow solver is applied to marine vessel path-planning for cluttered environments. The approach is computationally light. This is achieved by employing a simplified potential flow solution as well as generating the path online. The method relies on a simple guidance law that; (A)Regulates a target point to maintain a constant stream-function value, (B)Employ rudder control to steer a vessel towards this target. The utility of potential flow for path-planning is discussed. Simulation results are presented, showing the promise of the above approach.

An algorithm called Finite Receding-Horizon Incremental-Sampling Tree (RH-IST) will be presented. It contains elements from the well-known Rapidly Exploring Random Tree algorithm and receding-horizon framework to provide a computationally inexpensive planning algorithm for vehicles with limited sensing capability in uncharted environments. To resemble a finite planning horizon property in the algorithm, a deterministic configuration space sampling is conducted on the surface of a sphere around the root of the tree. To produce near optimal trajectories, samples are connected according to their cumulative costs, resulting in an optimization heuristic. In the tree construction phase, each branch of the tree consists of the trajectory of a fixed-wing unmanned aerial vehicle. The steering method necessary to construct the branches comprises of a closed-loop time-domain simulation on guidance level with a non-linear 3-D mass point representation of the plant, a path following controller and a reference path based on Bezier-splines. The feasible best branch of the RH-IST is chosen to provide the input for the execution of the plant based on the evaluation of a cost-functional. Simulations demonstrate the adaptive behavior of the RH-IST in a 3-D urban environment with static and unknown obstacles.

Linear MPC has been a major success story in industry. Arguably this is due to its capacity to deal with constraints and its seamless treatment of multivariable problems. Nonlinear MPC has also been extensively studied and advocated as a practical control strategy for processes whose dynamics are inherently nonlinear. In this paper we examine two industrial case studies to illustrate the power of nonlinear MPC in practice. We also use these case studies to point to future research challenges and opportunities.

In the presented work, a hybrid micro-grid simulator has been developed from the end-user point of view. The hybrid model includes a number of selectable elements for the production and storage of energy, making a (smart) use of the available renewable energy resources based on a demand-response principle. The main role of the simulator is to model the communication with the common energy network on case of surplus or deficit of energy production.

A timing control model is presented as a possible way to control a Human-Robot system executing a collaborative task in which a high level of synchronization among the agents is desired. The influencing parameters that affect the process of synchronization were analyzed. The performance of the model was evaluated based on costs of the waiting times of each of the agents at the spatial point of handover. The model was applied to two case-studies of dynamic H-R collaborative scenarios. Results indicate that for certain scenarios, the timing control model is preferable over sensor based control. The timing control model provides a balance between speed and moderate accuracy requirements.

The paper deals with the control of nonlinear processes with 2DOF-PI controllers. A new and practical tuning method, based on recent ideas of immersion and invariance, is proposed for the dynamic adjustment of the set-point weight. The main attractive feature of the proposal is the possibility of assigning a reduced-order linear dynamics for the tracking response beyond the nonlinear characteristics of the process and the amplitude of the set-point changes. From a practical point of view the adjustment is performed by a simple sliding mode regime that accepts a straightforward implementation.

The well-known potential field method for obstacle avoidance in the scope of mobile robots is discussed in this paper. Particular attention is on the car-like mobile robots, which impose practical limitations on the application of potential field method due to its limited speed and curvature in motion. Along with the review of some recent studies on this topic, we point out the necessity of implementing a nonholonomic motion planner and propose some extensions to other potential-field-related methods to deal with the constraints of car-like robots. Two exemplary scenarios based on our extensions are simuated to prove their feasibility in application.