The safe disposal of nuclear waste in radioactive environment urgently needs cost-effective approaches. Toward this goal, this article developed a method to external force estimation based on the identified model without force sensors. Firstly, the mathematical model including joint friction was obtained and transformed into the linear combination of unknown parameter to be estimated. Secondly, the unknown parameters were identified based on the improved particle swarm optimization algorithm, the identification procedure was implemented by optimizing the excitation trajectories to excite joint motion and sampling relevant data. Identified results were compared with the biogeography-based optimization algorithm and the cuckoo search algorithm. Then, the identified dynamic parameter was applied to external force estimation. Finally, the verification of external force estimation has been carried out using the Kinova Jaco2 robot manipulator, and the experimental results showed that the external forces by the proposed method could be estimated with an root mean square error of 0.7 N.
Robotic systems with force sensing have great potential for use in radioactive environments. In this study, a modified observer-based method was developed to calculate the unknown external force without adding a redundant sensor and achieve the fault detection in the presence of a force sensor. A dynamic model of a serial robotic manipulator was built and the design procedure for a modified disturbance observer (MDO) was established. The output of observer was then used to suppress the disturbance and generate the fault signature. Moreover, the stability analysis shows that the convergence of the observer error is ultimately bounded. Simulation results under the step and composite sinusoidal disturbance torque demonstrate the performance of the force estimation and disturbance rejection. The results, obtained using the Kinova Jaco 2 robot manipulator, show that the estimated errors of the external force in X-Y-Z direction are bounded within ±0.5 N, ±2 N, and ±3 N, respectively. Finally, the effectiveness of fault detection is also verified by the experiment results.
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