The problem of fault detection and isolation (FDI) in cooperative manipulators is addressed in this paper. Four FDI procedures are developed to deal with free-swinging joint faults, locked joint faults, incorrectly measured joint position, and incorrectly measured joint velocity. Free-swinging and locked joint faults are isolated via neural networks. For each arm, a Multilayer Perceptron (MLP) is used to reproduce the dynamics of the fault-free robot. The outputs of each MLP are compared to the actual joint velocities in order to generate a residual vector which is then classified by an RBF network. The remaining faults are isolated based on the kinematic constraints imposed on the cooperative system. Results obtained via simulations and via an actual cooperative manipulator robot are presented.

Neste artigo, o problema de controle robótico baseado em modelo com atenuação de distúrbios (ou problema de controle H<FONT FACE=Symbol>¥</FONT> robótico), apresentado em Chen et all (1994), é estendido para robôs manipuladores subatuados. O acoplamento dinâmico entre os elos é usado para controlar todas as juntas livres do manipulador. Uma solução explícita global é encontrada resolvendo um problema minimax definido através de uma equação de Bellman-Isaacs gerada pela teoria dos jogos. Resultados experimentais, obtidos com o manipulador UArm II, considerando as configurações totalmente atuada e subatuada, são apresentados.

In this paper, the model-based robotic control problem with disturbance attenuation (or robotic H<FONT FACE=Symbol>¥</FONT> control problem), presented in Chen et all (1994), is extended to underactuated manipulators. The dynamic coupling between the links is used to control all manipulator's free joints. A global explicit solution is found solving a minimax Bellman-Isaacs equation, generated via differential game theory. Experimental results, obtained from UArm II manipulator, considering fully actuated and underactuated configurations, are presented.