Contact force control in the robot end-point

AbstractThe end-point stiffness of a robot kinematic chain represents the crucial problem in force control. Within the period of the force generation, regardless of the drive type, due to inherent torque feedback the oscillations of the controlled force appear. For technologies with the constant contact force generation, the paper presents an effective linear control structure taking the physical limitation of the system’s inner variables into account. A numerical model of one degree of freedom verifies the proposed control algorithm

Intelligent active torque control for vibration reduction of a sprayer boom suspension system

The most usual way of protecting crop from diseases is by using chemical method whereby mixture of chemicals and water are sprayed onto crop via nozzles. These nozzles are located consistently along a boom structure oriented perpendicular to the direction of motion to cover large areas. The most important factor on spray distribution pattern is spray boom vibration. Thus, suspension control aims to attenuate the unwanted vibration and should provide improvements in term of distribution uniformity. In this study, a combination of passive and active suspension was considered to create superior performance. A passive suspension was employed to control undesired vertical motion of sprayer boom structure while the roll movement of spray boom was reduced via active suspension. The active suspension system of sprayer was implemented by applying robust active torque control (ATC) scheme that integrates artificial intelligence (AI) methods plus another feedback control technique utilizing proportional-integral-derivative (PID) control. The proposed control system basically comprises of two feedback control loops; an innermost loop for compensation of the disturbances using ATC strategy and an outermost loop for the computation of the desired torque for the actuator using a PID controller. Two AI methods employing artificial neural network (ANN) and iterative learning (IL) were proposed and utilized to compute the estimated inertial parameter of the system through the ATC loop. The research proposes two main control schemes; the first is a combination of ATC and ANN (ATCANN) while the other is ATC and IL (ATCAIL). The suspension system was first modeled and a number of farmland terrains were simulated as the main disturbance components to verify the robustness of the system and sprayer boom dynamic performance related to distribution uniformity. The simulation results both in frequency and time domains show the effectiveness of the proposed ATC schemes in reducing the disturbances and other loading conditions. The control schemes were further implemented experimentally on a developed laboratory spray boom suspension test rig

A Hybrid Intelligent Active Force Controller for Articulated Robot Arms Using Dynamic Structure Neural Network

The key feature of this paper is the application of a robotic control concept - Active Force Control (AFC). In this type of control, the unknown friction effect of the robotic arm may be compensated by the AFC method. AFC involves the direct measurement of the acceleration and force quantities and therefore, the process of estimating the system 'disturbance' due to friction becomes instantaneous and purely algebraic. However, the AFC strategy is very practical provided a good estimation of the inertia matrix of articulated robot arm is acquired. A dynamic structure neural network - Growing Multi-experts Network (GMN) is developed to estimate the robot inertia matrix. The growing and pruning mechanism of GMN ensures the optimum size of the network that results in an excellent generalization capability of the network. Active Force Control ( AFC) in conjunction with GMN successfully reduces the velocity and position tracking errors in spite of robot joint friction. The embedded GMN is capable of coupling the inertia matrix estimation online that clearly enhances the performance of AFC controller. The robustness and effectiveness of the new hybrid neural network-based AFC scheme are demonstrated clearly with regard to two link articulated robot and a simulated two-degree of freedom Puma 560 robot