L.F. Baptista

Stochastic Systematic Search Algorithms for Satisfiability

Abstract Abstract This paper proposes the utilization of randomized backtracking within complete backtrack search algorithms for Prepositional Satisfiability (SAT). Given that state-of-the-art SAT algorithms often randomize variable selection heuristics, and given that randomization is not naturally applicable to the identification of necessary assignments, our approach provides a fully stochastic, but complete, search algorithm for SAT. In addition, different organizations of randomized backtracking are described and compared. Moreover, we relate randomized backtracking with a more general form of backtracking, referred to as unrestricted backtracking. Finally, experimental results provide empirical evidence that randomized backtracking can be a competitive technique in solving hard real-world instances of SAT.

Fuzzy predictive algorithms applied to real-time force control

Abstract This paper proposes combining a classical impedance controller with a fuzzy predictive algorithm. This algorithm calculates the optimal virtual trajectory that is given to the impedance controller. This control strategy allows for the inclusion of a non-rigid environment, represented by a nonlinear model, in the control design in a straightforward way. Thus, improving the global force control performance. In order to reduce the oscillations of the optimized reference position, a fuzzy scaling machine is included in the force control strategy. The performance of the force control scheme is illustrated for an experimental two-degree-of-freedom robot. A real-time implementation of the fuzzy predictive algorithm revealed better performance in terms of force control than the classical force control algorithms.

An open architecture for position and force control of robotic manipulators

In this paper a low cost open architecture PC-based axis controller for robotic applications is described. The system is based on an available low price commercial PC interface board and the controller software runs totally on a Pentium 100 MHz computer in order to control a robotic axis. The driving kernel of software for supervisory, management and control was developed in a high level language (C++) and enables the analysis of several position and force/position control strategies for research and practical applications. The experimental results for the given position and force trajectories reveal promising position and force tracking results for the experienced control algorithms.

An experimental testbed for position and force control of robotic manipulators

In this paper a low cost open architecture PC-based controller for a planar two-degree of freedom robotic manipulator is described. The system is based on an available low price commercial PC interface board and all of the control software runs on a Pentium 100 MHz computer to control the arm. The driving kernel of software for supervisory, management and control was developed in a high level language (C++) and enables the analysis of several position and force/position control strategies for research and practical applications. The preliminary experimental results for the given tracking trajectories reveal an accurate position tracking performance and have shown also good force control results in the presence of a non-rigid environment, with conventional digital PID controllers.

Force control of rigid manipulators: a comparative study for non-rigid environments

This paper presents a comparative study between two force control strategies for robotic manipulators in the presence of nonrigid environments. The first approach combines two distinct control methodologies in the force control scheme: a model based predictive control (MBPC) and the impedance controller. The predictive controller generates, in an optimal way, the reference positions and velocities to the impedance controller which results in a desired force profile. The main advantage of this control strategy is to provide an easy inclusion of the environmental model in the controller design and thus to improve the global control system performance, especially in nonrigid environments. Moreover, since the impedance and the environmental models are linear, the solution of an unconstrained optimization procedure can be analytically obtained with moderate computational burden. The second approach is an impedance control scheme modified in order to obtain a force controller. The performance of both force control schemes is illustrated for a two degree-of-freedom PUMA 560 robot, whose end-effector is forced to move along a frictionless surface located on the horizontal plane. The simulation results obtained reveal an accurate force tracking performance for the predictive control approach in comparison to the impedance force controller in the presence of nonrigid time varying stiffness environments.

Force Control of Robotic Manipulators Using a Fuzzy Predictive Approach

This paper proposes a force control strategy for robotic manipulators considering a non-rigid environment described by a nonlinear model. This approach uses a fuzzy predictive algorithm to generate, in an optimal way, the reference or virtual position to the classical impedance controller in order to apply a desired force profile on the environment. The main advantage of this control strategy is the possibility of including a nonlinear model of the environment in the controller design in a straightforward way, improving the global force control performance, especially in non-rigid environments. Moreover, in order to reduce the oscillations on the optimized reference position a fuzzy scaling machine is included on the force control strategy. The performance of the force control scheme is illustrated for a two degree-of-freedom PUMA 560 robot, which end-effector is forced to move along a flat surface located on the vertical plane. The simulation results obtained with the fuzzy control scheme reveal significant improvement in the force tracking performance, when compared to the impedance control with force tracking in non-rigid environments.

INTERACTION CONTROL EXPERIMENTS FOR A ROBOT WITH ONE FLEXIBLE LINK

One of the major drawbacks of flexible-link robot applications is its low tip precision, which is an essential characteristic for applications with interaction control with a contact surface. In this work, interaction control strategies considering rigid and flexible contact surfaces are applied on a two degrees of mobility flexible-link manipulator. The interaction strategies are based on the closed-loop inverse kinematics algorithm (CLIK) to obtain the angular references to the joint position controller. The control schemes were previously tested by simulation and further implemented on the flexible-link robot. The obtained experimental results exhibit a good force tracking performance, especially for a rigid surface, and reveal the successful implementation of these control architectures for a robot with one flexible link.

Force and Position Control of Robotic Manipulators: An Experimental Approach

Abstract In this paper robot force control experiments in non-rigid environments are presented. The force control algorithms implemented in real-time are an impedance based control scheme with force tracking capability and a hybrid force/position control algorithm. A two-degree-of-freedom planar manipulator and a non-rigid contact surface were constructed for purposes of control evaluation. A low cost PC-based real-time software architecture was developed to fully control the robotic setup. The obtained results for a given force/position task reveal the successful implementation of the force control algorithms in real-time, especially for the impedance controller with force tracking.

Model predictive algorithms based on fuzzy discrete alternatives

The application of model predictive algorithms to nonlinear processes results in a non-convex optimization problem for computing the optimal inputs. The optimization problem can be solved by using discrete search techniques, such as branch-and-bound, which has been applied to predictive control. However, for computational reasons a small number of possible discrete inputs must be used, which results in poor control performance. A possible solution to this problem is the use of adaptive input alternatives based on fuzzy rules, which were proposed previously to solve this problem in predictive control problems. The paper generalizes fuzzy discrete alternatives to predictive algorithms. An example of a robot were the position references are derived using the fuzzy-rule based optimization is presented, revealing good control performance.

A force control approach of robotic manipulators with neural network compensation

Abstract In this paper, the application of an impedance control scheme to drive a robot with a desired force profile in the presence of non-rigid environments is considered. However, for the usual impedance based control schemes, fine control of the contact force especially for non-rigid environments is difficult to obtain. Moreover, unmodeled robot model effects and environment uncertainties, can seriously affect the force tracking performance. In this paper, the nonlinear uncertainties in a model-based impedance controller are compensated by an on-line neural network compensation scheme. Further, a predictive algorithm is designed to compute the optimized virtual trajectory for the impedance controller in order to precisely track the desired force profile. The performance of the proposed control scheme is illustrated by simulation for a three degree-of-freedom PUMA 560 robot, which end-effector is forced to move along a surface located on the vertical plane. Simulation results reveal an accurate force tracking performance for the proposed force control scheme, in the presence of non-rigid environments.