When a fault-tolerant robot fails, its fault responsive system detects and identifies the failure. During the recovery process, reconfiguration of the system isolates the fault, and a new system model and a suitable controller attempt to completely compensate for the faulty condition without interrupting the robot's operation. In this paper, the authors address the recovery process for fault-tolerant serial robots when they experience actuator failure. For this purpose, the authors consider three controllers based on PID feedback, sliding control, and parameter adaptation methods. It is shown that the sliding control implemented with a boundary layer reduces the system errors efficiently when the errors are large, and the controller behaves like an ordinary PID feedback as the errors get smaller. Additionally, when failures cause uncertainty in system parameters, inclusion of parameter identification capability in the controller design is suggested. Although the work is valid for a general robot, simulation results are presented on a four-axis robot.>

An accelerated learning algorithm (ABP-adaptive back propagation) is proposed for the supervised training of multilayer perceptron networks. The learning algorithm is inspired from the principle of "forced dynamics" for the total error functional. The algorithm updates the weights in the direction of steepest descent, but with a learning rate a specific function of the error and of the error gradient norm. This specific form of this function is chosen such as to accelerate convergence. Furthermore, ABP introduces no additional "tuning" parameters found in variants of the backpropagation algorithm. Simulation results indicate a superior convergence speed for analog problems only, as compared to other competing methods, as well as reduced sensitivity to algorithm step size parameter variations.

A robust control method which can overcome the disturbances and parameter uncertainties of a robot manipulator is proposed. This sliding-mode-based technique provides performance robustness that guarantees the system attractiveness to a certain linear surface if the so-called matching conditions and bounds on perturbations and modeling errors are satisfied. The performance and robustness characteristics can be separated into two cascaded loops, an internal one that guarantees the surface attraction or linearization robustness and an external one that designs a controller based on the linearized system. The technique is applied to a two-bar robot with a spring at the end effector as an unknown interaction with the environment and an external load disturbance.>

A recurrent multilayer perceptron (MLP) network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. This effort is part of a research program devoted to developing real-time diagnostics and predictive control techniques for large-scale complex nonlinear dynamic systems. The identification is performed in the discrete-time domain, with the learning algorithm being a modified form of the back-propagation (BP) rule. The recurrent dynamic network (RDN) developed is used for the identification of a simple power plant boiler with known nonlinear behavior. Results indicate that the RDN can reproduce the nonlinear response of the boiler while keeping the number of nodes roughly equal to the relative order of the system. A number of issues are identified regarding the behavior of the RDN which are unresolved and require further research. Use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic patterns

A novel artificial neural network model based on P. Kanerva's (MIT Press, 1988) sparse distributed memory (SDM) is presented. The model possesses many major advantages over the original SDM. It is adaptive in the sense that the memory cells are called into service or released from service by a global mechanism. Since memory cells are utilized depending on the load on the memory, the actual number of memory cells needed to implement the ASDN (adaptive SDM) is significantly smaller than what is required for the original SDM. The storing and retrieval procedures are much simpler, and analysis of the best match problem can be carried out in a deterministic setting. All the advantages of the original SDM are retained while the main drawback of the original SDM, namely, the huge number of physical memory locations, is removed. The concept of time-varying intensity of memory is introduced, and customized metrics for determining distance between two data objects are allowed