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The design of a variable structure model reference robust control without a prior knowledge of high frequency gain sign is presented. Based on an appropriate monitoring function, a switching scheme for some control signals is proposed. It is shown that after a finite number of switching, the tracking error converges to zero at least exponentially for plants with relative degree one or converges exponentially to a small residual set for plants with higher relative degree, and the input disturbance can be completely rejected without affecting the tracking performance.

The direct adaptive control of planar robot manipulators through visual servoing is considered. A solution is developed for image-based visual systems to allow tracking of a desired trajectory, when both camera calibration and robot dynamics are uncertain. In order to solve the MIMO parameter adaptive problem without using image velocity information, the adaptive camera calibration is formulated as a relative degree two MIMO adaptive control problem. A recently proposed Lyapunov/passivity-based adaptive control for relative degree two MIMO system based on SDU factorization is applied. The resulting adaptive calibration is then combined with an adaptive motion controller for the manipulator, which takes into account its uncertain nonlinear dynamics. The overall stability of the adaptive visual servoing system can be proved thanks to the explicit Lyapunov-like functions of both adaptation schemes.

In this work, we address the problem of kinematic modeling and control design of a multifingered robot hand. Each robot finger is modeled as a parallel manipulator and its kinematic constraints are computed from empirical analysis due to the inherent mechanical complexity of the mechanism. The motion control problem for a grasped object is solved by using the kinematic control approach, which is able to ensure the asymptotic stability of the output tracking error and the prehension of the object. The kinematics-based control scheme is designed to include the contact model in the hand Jacobian matrix, allowing for the simultaneous control of the object position as well as the relative position between the fingers. Experiments are carried out with a three-fingered robot hand executing grasping and manipulation tasks of soft objects. Practical results are shown to illustrate the performance and effectiveness of the proposed methodology.

In this work, we present a control and obstacle collision avoidance method for redundant robot manipulators operating in partially structured environments in the presence of humans. The control algorithm is based on the concept of artificial potential fields and it uses the pseudo-inverse of the Jacobian matrix with a weighting factor for the mechanical joint limits, taking advantage of the robot redundancy for the purpose of obstacle avoidance and control goal achievement. The detection algorithm uses a depth sensor based on the structured light to obtain a 2-1/2-D description of the surroundings from a point cloud. Repulsive fields are created around the detected obstacles, allowing for the robot to perform the task of interest without collisions. A filtering methodology based on geometric elements is presented to filter the RGB-D scene captured by the depth sensor, eliminating the robot body and the obstacles located outside its workspace. Experimental results, obtained with a Motoman DIA10 robot and a Microsoft KinectTM, illustrate the feasibility of the proposed scheme.

In this paper a control theoretical solution is presented for the direct adaptive visual dynamic control of planar manipulators using a fixed camera, when both camera calibration and robot dynamics are uncertain. The proposed scheme is developed for image-based look-and-move visual systems to allow tracking of a moving target. In order to solve the multivariable parameter adaptive problem, the recently proposed Immersion and Invariance (I&I) method is used. The scheme is then combined with an adaptive controller for the manipulator, taking into account its nonlinear dynamics. This leads to an overall stable adaptive visual servoing system. An extension of the I&I approach for 3 D visual servoing is shown to be possible by a convenient control reparametrization associated with a proper image-based depth control scheme.

A unit vector based output-feedback model-reference adaptive controller (UV-MRAC) for a class of uncertain multivariable nonlinear systems is proposed. This paper generalizes a previous controller which can be applied to plants with uniform relative degree one. Here, the relative degree can be arbitrary. The resulting sliding mode controller is applicable to plants with nonlinear state dependent disturbances which are possibly unmatched with respect to the plant input. The closed loop system has global or semi-global exponential stability with respect to some small residual set and the controller is free of peaking phenomena which could appear in high gain observer based schemes.

A model-reference variable structure controller based on a high gain observer (HGO) is proposed and analyzed. For single-input-single-output (SISO) linear plants with relative degree larger than one, the switching law is generated using the HGO state while the modulation function in the control law is generated using signals from state variable filters free of high gains. This scheme achieves global exponential stability with respect to a small residual set and does not generate the peaking phenomena.

This note considers the robust output tracking problem using a model-reference sliding mode controller for linear multivariable systems of relative degree one. It is shown that the closed loop system is globally exponentially stable and the performance is insensitive to bounded input disturbances and parameter uncertainties. The strategy is based on output-feedback unit vector control to generate sliding mode. The only required a priori information about the plant high frequency gain matrix K/sub p/ is the knowledge of a matrix S/sub p/ such that -K/sub p/S/sub p/ is Hurwitz which relaxes the positive definiteness requirement usually needed by other methods.

This paper presents a unit vector based output-feedback model-reference adaptive control (UV-MRAC) for uncertain multi-input-multi-output (MIMO) linear systems. Some features of this new algorithm are: (i) Less restrictive a priori knowledge about the high frequency gain matrix of the plant is required. (ii) It applies to plants of any uniform relative degree. (iii) The closed loop system is exponentially stable with respect to some small residual set. (iv) The controller is free of peaking.

The paper describes an automatic dynamic positioning system for remotely operated underwater vehicles (ROVs) using a mechanical passive arm for position measurement that is suitable for inspection and intervention tasks requiring precise positioning. Good dynamic performance in tracking was also attained, particularly with the variable structure model reference adaptive control strategy.

Abstract Safety and efficient operation are imperative factors to offshore production sites and a main concern to all Oil & Gas companies. A promising solution to improve both safety and efficiency is to increase the level of automation on the platforms by introducing intelligent robotic systems. Robots can execute a wide variety of tasks in offshore environments, including monitoring and inspection, diagnosis and maintenance, process production intervention, and cargo transport operations. In particular, considering the distance of offshore platforms from the Brazilian coast, such technology has great potential to increase safety by decreasing the number of onboard personnel, simplify logistics, and reduce operating costs of Brazilian facilities. The use of robots can also allow proactive integrity management and increase frequency and efficiency of platform inspection. DORIS is a research project which endeavors to design and implement a mobile robot for remote supervision, diagnosis, and data acquisition on offshore facilities. The proposed system is composed of a rail-guided mobile robot capable of carrying different sensors through the inspected environment. The robot can also analyze sensor data and identify anomalies, such as intruders, abandoned objects, smoke, fire, and liquid leakage. The system is able to read valves and make machinery diagnosis as well. To prove the viability of the proposed system, an initial prototype is developed using a Roomba robot with several onboard sensors and preliminary tests have been performed in a real environment similar to an offshore platform. The tests show that the robot is capable of indicating the presence or absence of objects in a video stream and mapping the local area with laser sensor data during motion. A second prototype has been built to test the DORIS mechanical design. This prototype is used to test concepts related to motion on a rail with straight, curved, horizontal, and vertical sections. Initial results support the proposed mechanical concept and its functionalities. Introduction During the last decade, several Oil & Gas companies, research groups, and academic communities have shown an increased interest in the use of robotic systems for operation of offshore facilities. Recent studies project a substantial decrease in the level of human operation and an increase in automation used on future offshore oil fields (Skourup and Pretlove, 2009). Today, robotic systems are used mainly for subsea tasks, such as mapping the seabed and performing inspection tasks on underwater equipment, risers, or pipelines using Remotely Operated Vehicles (ROVs) or Autonomous Underwater Vehicles (AUVs). Topside operations, on the other hand, have not yet adopted robotized automation as a solution to inspection and operation tasks. From (2010) points out the potential increase in efficiency and productivity with robot operators rather than humans, given that robots work 24 hours per day and 7 days per week, are less prone to errors, and are more reliable. Another highlighted point is the improvement Health, Safety, and Environment (HSE) conditions, as robots can replace humans in tasks performed in unhealthy, hazardous, or confined areas. In the specific Brazilian case, the Oil & Gas industry is growing at a high pace, mainly due to the recent discoveries of big oil fields in the pre-salt layer off the Brazilian coast. These oil reservoirs are located farther than 300 km from the shore and at depths of 5000 to 7000 km. These factors, especially the large distances, motivate the development of an offshore production system with a high degree of automation based on advanced robotics systems.

Mobile robots and vehicles with active articulated elements are well suited to drive on irregular and rough terrain because they are able to adjust their center of mass and decrease the risk of tip over or other accidents. The articulated mechanism, however, is usually constrained by the actuator's bandwidth, making it difficult for the vehicle to compensate for abrupt changes in terrain profile. In this paper we propose a terrain model-based control method to improve stability when traversing terrains with varying slopes, depressions, and rises. A model predictive control approach takes into account actuator bandwidth to anticipate interactions with the terrain and control the articulated elements to prevent tipping over. The method is applied to an autonomous orchard platform where workers stand while conducting production tasks on the trees, adjusting the platform height and increasing its stability. The feasibility is illustrated via numerical simulations performed with the MD Adams/Car software and Matlab, with both artificial and natural terrain data. Challenging yet realistic terrain profiles are considered to demonstrate the control effectiveness in preventing the platform from tipping over and therefore improving the workers' and the vehicle's safety.

In this work, we consider the optimization problem of ocean wave energy absorption in oscillating-body systems. A solution based on discrete-time extremum seeking control is proposed to improve the mean extracted power by a power take-off (PTO) mechanism under different wave climates. Two different PTO mechanisms are considered: a linear and a non-linear one. Simulation results, obtained from an oscillating-body system subject to incident regular and irregular waves, are presented to show the feasibility of the proposed scheme.

Inertially stabilized platforms are subject of interest to different engineering areas, such as telecommunications, robotics and military systems. The objective is to maintain the attitude of a desired object constant despite the movements of a host vehicle, in order to point towards a chosen direction. This paper deals with the platform stabilization problem in the case where only two gyroscopic sensors are available. In this case, certain restrictions to vehicle dynamics are considered. Sensor positioning and sensitivity analysis to unmodeled motion are also discussed. An extension to the visual tracking problem is presented using a class of algorithms for fast digital image registration.

Urucu, deep in the Amazon forest, is the largest in-land natural gas and oil producing site in Brazil. The oil and gas extracted in Urucu travel some 700 km in a pipeline through the jungle until they reach Manaus, where they are refined and consumed. Exploration of the site is conditioned on the Brazilian oil company Petrobras monitoring a 400 km section of the pipeline for any signs of water contamination. The Environmental Hybrid Robot (EHR) was conceived to automate this humongous monitoring task, currently executed in extremely small scale by scientists and locals on foot and small boats. This paper describes the design, analysis, and testing of the EHR’s second-generation locomotion mechanism, based on a two-DOF parallel mechanism. The new mechanism allows for independent control of wheel position and orientation relative to the terrain, thus significantly increasing the robot’s mobility. Additionally, it allows for control of the vehicle’s velocity-torque curve by adjusting the radius of curvature of the wheel at the point of contact with the soil. We present the forward and differential kinematic models of the new mechanism and, based on them, a differential kinematics-based position and orientation control method. We illustrate the methodology with experimental results.

This chapter briefly describes the main results developed by the authors in the area of output feedback sliding mode control. For the sake of simplicity, the focus is maintained on uncertain single-input-single-output (SISO) nonlinear systems, although several results have been extended to the control of multi-input-multi-output (MIMO) systems. For the considered class of nonlinear systems, linear growth restriction on the unmeasured states is assumed, while less restrictive conditions are imposed to the growth of nonlinearities depending on the measured output. We present different tracking controllers for plants with arbitrary relative degree. We consider several approaches to overcome the relative degree obstacle: linear or variable structure lead filters, high-gain observers with constant or dynamic gain, global hybrid estimation schemes combining lead filters or observers with locally exact differentiators based on high-order sliding mode. Global or semi-global stability properties can be proved either for asymptotic exact tracking or for tracking within a small residual error. Some experimental results are presented to illustrate the applicability of the control schemes in real systems.

The problem of rigid robot manipulator tracking control without measurement of joint velocities and in the presence of parametric uncertainty is considered. A control strategy is proposed based on the recently developed I/O VS-MRAC (Input/Output Variable Structure Model-Reference Adaptive Control) scheme. Theoretical and simulation results indicate that the strategy leads to remarkable tracking error transient behavior, stability and performance robustness with respect to parameter uncertainty, nonlinearities and disturbances.

The active control of reconfigurable mobile robots based on multi-objective optimization is considered. Different control strategies to improve the robot mobility are presented. Traction is considered in terms of the contact forces distribution among wheels, while stability is defined by a potential energy margin. Thus, considering the conflicting control objectives associated with these strategies, a multi-objective optimization approach is formulated to determine a set of optimal solutions which allows to establish a tradeoff solution. A planar robot is considered to illustrate the proposed multi-objective optimization approach.

In this paper an adaptive formation control for nonholonomic mobile robots with unknown dynamic parameters is proposed. The control scheme is based on a saturated artificial potential function which allows a decentralized formation control design including collision avoidance. For each agent, a cascaded control strategy combines an adaptive motion controller which takes into account its uncertain nonlinear dynamic, and a kinematic control, leading to an overall globally stable adaptive system. The proposed control strategy is a velocity free scheme, where the control law only depends on the neighbors position information.

This work addresses the hybrid position-force control problem for robot manipulators performing inter-action tasks on constraint surfaces with regular curvature. A novel hybrid control law, based on an orientation-dependent term, is proposed to circumvent the performance degradation owing to modeling uncertainty, particularly with respect to the geometry of the constraint surface. As in our previous work, the effect of the robot dynamics can be included by using a cascade control strategy. In contrast, instead of only position, the presented approach provides complete robot posture control. Simulation results illustrate the performance and feasibility of the proposed control scheme.

This paper considers the problem of optimizing the efficiency of the primary conversion on an ocean wave energy hyperbaric converter. This wave energy converter (WEC) is an oscillating-body linked to a hyperbaric chamber. By solving the equation of motion of the power take-off mechanism, it is shown that optimal conditions for improving the absorbed power relate the buoy parameters and the chamber pressure with the incident waves. This work also proposes an algorithm for finding the best time interval that the body remains locked when latching control strategy is used to enhance the efficiency of the WEC. Numerical simulations are presented in order to evaluate the optimum conditions of the hyperbaric WEC and the performance of the proposed algorithm.

In this work, a hydraulic turbine speed governor is proposed in view of its application in an isolated electric generation system based on an ocean wave energy converter (WEC). The proposed strategy is based on cascade closed-loop control combined with feedforward of load disturbances. The main characteristics of a WEC are presented and a dynamic model of the generating unit is developed. Experimental results illustrate the performance of the proposed control scheme.

This paper proposes a new analysis technique called "ISS regulator approach" to show that a simple causal PD controller plus feedforward using only position measurements solves the global output feedback tracking control problem of robot manipulators with arbitrarily small damping. To this end, we first show that a causal PD regulator leads to a global input-to-state stable system with respect to a bounded input disturbance. Then, using this fact we prove that the addition of a feedforward compensation renders the overall error system uniformly globally asymptotically stable. In addition, we present a possible extension of the proposed method to other classes of Euler-Lagrange systems.

A global output-feedback tracking controller based on a hybrid estimation scheme is proposed for a class of uncertain nonlinear systems. The hybrid estimation scheme combines a general estimator, that provides an input-to-state practical stabilizing control law with a locally exact differentiator, based on higher-order sliding modes. The key idea is to design a switching law to select between both estimators in such a way that stability properties are preserved and in addition exact tracking is achieved. As an example, the proposed control scheme is shown to solve the global output feedback tracking control problem of a class of uncertain Euler-Lagrange systems.

This chapter addresses the problem of model-reference sliding mode control of uncertain linear multi-input-multi-output systems by output-feedback. After a brief revision of the main approaches and results available in the literature, a new method to solve the problem is presented using unit vectors as switching functions to induce sliding modes. It is named Unit Vector Model-Reference Adaptive Control (UV-MRAC).

An output-feedback sliding mode controller is proposed for a class of single-input-single-output (SISO) uncertain nonlinear systems. The sliding surface is generated using the state of a high gain observer (HGO) while the control signal amplitude is generated from a norm observer free of peaking. The proposed scheme achieves global tracking with respect to a small residual set by means of a time varying observer gain (HGO) synthesized from measurable signals.

This paper points out some misleading statements of the above-mentioned paper (Kwan, 1996) and shows that the proposed controller can be easily derived, under less restrictive conditions, from a previously proposed output feedback variable structure control scheme.

This paper presents a control strategy for robot manipulators to perform 3D cartesian tracking using visual servoing. Considering a fixed camera, the 3D cartesian motion is decomposed in a 2D motion on a plane orthogonal to the optical axis and a 1D motion parallel to this axis. An image-based visual servoing approach is used to deal with the nonlinear control problem generated by the depth variation without requiring direct depth estimation. Due to the lack of camera calibration, an adaptive control method is used to ensure both depth and planar tracking in the image frame. The depth feedback loop is closed by measuring the image area of a target object attached to the robot end-effector. Simulation and experimental results obtained with a real robot manipulator illustrate the viability of the proposed scheme. Este trabalho apresenta uma estratégia de controle para robôs manipuladores realizarem rastreamento cartesiano 3D utilizando servovisão. Considerando uma câmera fixa, o movimento cartesiano 3D é decomposto em um movimento 2D sobre um plano ortogonal ao eixo óptico e em outro movimento 1D paralelo ao mesmo eixo. Uma abordagem de servovisão baseada em imagem é utilizada para tratar o problema de controle não-linear, gerado pela variação de profundidade, sem a necessidade de estimar esta medida. Devido à ausência de calibração da câmera, um método de controle adaptativo é utilizado para assegurar rastreamento planar e de profundidade nas coordenadas da imagem. A malha de controle de profundidade é fechada através da medição da área da imagem de um objeto fixado no efetuador do robô. Simulação e resultados experimentais, obtidos com um robô manipulador real, ilustram a viabilidade do esquema proposto.

This work considers a hybrid force and vision control system for robotic manipulators using a force sensor and a fixed uncalibrated camera. A method is proposed to combine direct force control and adaptive visual servoing to perform tasks on unknown smooth surfaces, in the presence of uncertainties in the camera-robot system parameters. The considered task involves the visual tracking of a moving target, while the end-effector tip exerts a controlled contact force on the surface. Simulation results are presented to illustrate the performance of the proposed scheme

Neste trabalho, propõe-se um controlador por modelo de referência e modo deslizante baseado em observador de alto ganho. Para plantas monovariáveis com grau relativo maior do que um, a lei de chaveamento é gerada utilizando-se o estado observado, enquanto que a função de modulação da lei de controle utiliza sinais gerados por filtros de estado. Mostra-se que este esquema possui estabilidade global e exponencial em relação a um conjunto residual arbitrariamente pequeno. Além disso, o sistema em malha fechada não apresenta o fenômeno de peaking que pode prejudicar o desempenho. Resultados experimentais ilustram o bom desempenho obtido pelo algoritmo em condições realistas. A model-reference variable structure controller based on a high gain observer (HGO) is proposed and analyzed. For single-input-single-output plants with relative degree larger than one, the switching law is generated using the observed state while the modulation function in the control law is generated using signals from state variable filters. With this scheme, global exponential stability with respect to an arbitrarily small residual set is achieved. Moreover, the closed loop system shows no peaking phenomena which could degrade the performance. Experimental results illustrate the good performance obtained with the algorithm in actual conditions.

The problem of rigid robot manipulator tracking control without measurement of joint velocities and in the presence of parametric uncertainty is considered. A control strategy is proposed based on the VS-MRAC (variable structure model-reference adaptive control) scheme. Theoretical and experimental results on a PUMA 560 indicate that the strategy leads to remarkable tracking error transient behavior, stability and performance robustness with respect to parameter uncertainty, nonlinearities and disturbances.

Newton based path planning has been successfully applied to driftless nonlinear systems. It has been extended to feedback implementation in a form similar to nonlinear model predictive control. The closed loop system is shown to be asymptotically stable under suitable conditions which are reasonable for driftless systems but restrictive for systems with drift. This work presents a modification of the algorithm that improves its applicability to systems with drift. The key idea is to update the predicted control sequence to reduce the M+1-step-ahead error instead of the M-step ahead error, where M is the length of the prediction horizon. A numerical examples is included to illustrate the efficacy of the proposed approach.

Neste trabalho, considera-se um sistema robótico servovisual usando câmera fixa e não-calibrada. Dois controladores adaptativos são propostos para rastrear visualmente um alvo em movimento na presença de incertezas nos parâmetros da câmera. A primeira estratégia adaptativa utiliza o método de Hierarquia de Controle para solucionar o problema de controle adaptativo multivariável. A segunda é baseada na simetrização via fatoração da matriz de controle. Os algoritmos adaptativos são robustos na medida em que apresentam reduzida sensibilidade à incertezas cinemáticas. Resultados experimentais com um manipulador em movimento planar ilustram a robustez e a viabilidade dos esquemas propostos. In this work, a robotic visual servoing system using a fixed and uncalibrated camera is considered. Two adaptive controllers are proposed for visually tracking a moving target in the presence of camera parameters uncertainties. The first adaptive strategy uses the Hierarchy of Control method to solve the multivariable adaptive control problem. The second one is based on the symmetrization via factorization of the control matrix. The adaptive algorithms are robust in the sense that they present reduced sensitivity to kinematic uncertainties. Experimental results with a manipulator in a planar motion illustrate the robustness and viability of the proposed schemes.

In this paper, we consider the design of a variable structure model reference adaptive control (VS-MRAC) for plants with relative degree one without the knowledge of the sign of the plant high frequency gain. A switching method for the control signal, based on an appropriate monitoring function, is proposed. As a result, we show that after a finite number of switchings, the tracking error converges to zero at least exponentially. Interestingly enough, if the initial conditions of some states of the closed-loop system are zero, we show that at most one switching is needed.

This paper considers the localization and navigation of a mobile robot. The control strategy is based on a nonlinear model predictive control technique that utilizes the Newton method. The robot localization is obtained using information from odometric and ultrasonic sensors through a Kalman filter. Simulation and experimental results illustrate the efficacy of the proposed method.

The problem of direct adaptive visual servoing of robot manipulators is considered. A solution is developed for image-based look-and-move visual systems to allow tracking of a desired trajectory, when both camera calibration and robot dynamics are uncertain. In order to solve the multivariable parameter adaptive problem, the recently proposed immersion and invariance (I&I) method is used. The scheme is then combined with a robust motion controller for the manipulator, which takes into account its uncertain nonlinear dynamics and leads to an overall stable adaptive visual servoing system. The effectiveness of the proposed strategy is illustrated through simulations and experimental results.

A control-theoretical solution is presented for the direct adaptive visual control of planar manipulators using a fixed camera, when both camera calibration and robot dynamics are uncertain. The proposed scheme is developed for image-based look-and-move visual systems to allow tracking of a moving target. In order to solve the multivariable parameter adaptive problem, the proposed immersion and invariance (I&I) method is used. The scheme is then combined with the adaptive controller for the manipulator, taking into account its nonlinear dynamics and leading to an overall stable adaptive visual system. Simulations and experimental results are also presented for the proposed strategy.

An output-feedback sliding mode controller is proposed for uncertain plants with relative degree higher than one in order to achieve asymptotic exact tracking of a reference model. To compensate the relative degree, a lead filter scheme is proposed such that global stability and asymptotic exact tracking are obtained. The scheme is based on a convex combination of a linear lead filter with a robust exact differentiator, based on second order sliding modes.

In this work, a new 2D adaptive visual positioning/tracking controller is considered. The proposed scheme is developed for image-based eye-to-hand systems to perform position and tracking tasks on the 3D environment, when both camera calibration and robot parameters are uncertain. The symmetric-diagonal-upper (SDU) factorization method is adopted to solve the adaptive multivariable control problem. The positioning/tracking controller is first developed for the Cartesian robot case, and then extended to the general robotic system. Simulation results are also presented for the proposed strategy.

This paper extends the application of the recently proposed satisficing control strategy to passive systems. The satisficing control is based on notions of satisficing decision theory. In this framework a set of asymptotically stabilizing control laws can be parameterized in order to obey an instantaneous cost-benefit inequality. Using the nonlinear version of the Kalman-Yacubovitch-Popov property this technique can be extended to passive systems. To illustrate the novelty of this approach, the paper considers the attitude control problem of a rigid body.

Addresses the design, implementation and experimental evaluation of a sliding control scheme for uncertain systems which is free of chattering and results in smooth control signals. The proposed approach relies on an appropriate prediction error. It is shown that, in practice, successful performance of the basic sliding controller requires adequate filtering of the measured signal. A particular Kalman-filter, independent of the plant itself, is shown to yield very satisfactory experimental results obtained on a 1 d.o.f. helicopter-like mechanism.

A class of iterative methods have recently been proposed for the path planning of nonholonomic systems. These methods warp an initial path iteratively to an acceptable final path by using Newton-Raphson or gradient type of algorithms. Once a path is found off-line, a feedback controller is then used to follow the path. In this paper, we propose a modification of these off-line methods to transform them directly into a feedback controller. The main idea is to couple the iteration variable to the actual time, thus the control is executed during the path iteration, before the convergence. We show that this scheme guarantees the closed loop asymptotic stability when the system model is known, and possesses certain robustness when the model information is imperfect. By using interior penalty functions, inequality constraints can also be handled by the algorithm. Simulation results are included, showing promise of the approach.

It is well known that the linear feedback of the quaternion of the attitude error and the angular velocity globally stabilizes the attitude of a rigid body. In this note, we show that the angular velocity feedback can be replaced by a nonlinear filter of the quaternion, thus removing the need for direct angular velocity measurement. In contrast to other approaches, this design exploits the inherent passivity of the system; a model-based observer reconstructing the velocity is not needed. An application of the proposed scheme is illustrated for the robot control problem. Simulation results are included to illustrate the theoretical results.

This paper considers the stabilization problem for control-affine nonlinear systems. A gradient-based numerical method is proposed to solve this problem. A receding horizon strategy, from model predictive control, is used to yield a feedback control law. We show that, under certain weak assumptions, the closed-loop system is asymptotically stable. The full swing-up of the cartpole system shows the possibility of considering fast nonlinear systems.

The I/O VS-MRAC redesign and stability analysis presented earlier for the case of plants with relative degree n* ? 2 (ACC'92), is generalized to the case of arbitrary n*. The redesign is based on the explicit consideration of input disturbances which, in addition to actual external disturbances, may include the disturbance originated by the uncertainty of the plant high frequency gain (HFG). This leads to considerably simpler and less restrictive stability analysis, as well as to more effective controller design for external disturbance rejection. It is shown that the overall error system is globally exponentially stable with respect to some small residual set, in agreement with the remarkable performance exhibited by the proposed controller. Simplified I/O VS-MRAC schemes are also discussed.

A robot visual servoing system using a fixed camera is considered. Two adaptive controllers are proposed for visually tracking a moving target in the presence of camera uncertainties. The first adaptive strategy is based on an original method proposed by Hsu and Aquino (1999). The key idea is to use a hierarchy of control methods to solve the adaptive control problem. The adaptive algorithm is robust in the sense that it has reduced sensitivity to kinematic uncertainties. The second adaptive law is based on the symmetrization of the control matrix. Experimental results with a robot manipulator in a planar motion illustrate the robustness and viability of the proposed scheme.

This paper generalizes and validates experimentally the applicability of a recently developed output-feedback sliding mode tracking controller based on a hybrid switching compensator. The proposed controller can deal with plants of arbitrary relative degree by using a locally exact differentiator of higher order based on second order sliding modes (SOSM). Global stability and asymptotic exact tracking are achieved by combining, through switching, a standard lead filter with the SOSM exact differentiator. Simulations and experimental results are presented to illustrate the applicability of the hybrid scheme in real conditions.

This paper presents a novel control architecture for controlling a moving remote center of motion in addition to the end-effector motion during robotic surgery. In minimally invasive surgery, it is common to require that the point at which the robot enters the body, called the incision point or the trocar, does not allow for any lateral motion. It is generally considered that no motion should be applied to this point in order to avoid inflicting damage to the patient's skin. However, in surgery, the patient's body may be moving, for example due to breathing or the beating of the heart. In order to compensate for this motion—or if we for some other reason want to leverage the possible motion of the incision point to improve performance in any other way—we derive a new framework which allows us to actively control the motion both at the incision point and the end effector. The novelty of the approach lies in the possibility of controlling both the incision point and the end effector to follow a trajectory, and that we find a Jacobian matrix that satisfies the velocity constraints in both the end-effector and the incision point frames. This allows us to formulate a framework that is not only suited for control, but also for analyzing the condition number of the Jacobian and avoid any singular configurations that may arise either as a result of the constrained motion or the manipulator geometry. The approach is verified experimentally on a redundant industrial manipulator.

This paper considers a recently proposed sliding control scheme for uncertain systems which is free of chattering and results in smooth control signals. The proposed approach relies on an appropriate prediction error. The complete error system is globally and exponentially stable with respect to some small residual set. Under certain assumptions the system further achieves the ideal sliding mode. The case of linear plants of relative degree n*/spl ges/1, considering input disturbance and unmodeled dynamics, is completely analysed. Experimental results using a Kalman filter to minimize the quantization noise, illustrate the remarkable behavior of the proposed scheme.