The increased demand on the performance and efficiency of industrial robots, has led to the design of sophisticated control systems. Such control systems require an accurate dynamic model of the system. A commonly used method of modeling an industrial robot, involves the description of a set of dynamic equations, relating actuator torques to loads and accelerations. These equations are generally quite complex and inconvenient for implementation on digital computers.

Another method often used for identification, is the ‘indirect method’, in which the transfer function is obtained in two steps. The discrete time model is first derived from samples of the input and output measurements, which is then transformed to the continuous-time model. A limitation of this method is that it requires the excitation to be of the ‘persistently exciting’ type, thus precluding the application of simple inputs like the step signal.

This paper describes a ‘direct’ method for identification of an ‘industrial robot’ from samples of input and output observations. Results of modeling an industrial robot and two simulations are presented. One of the simulations, and the industrial robot uses the step input as excitation. The other example was excited with an exponential input.

A method for planning minimum time joint trajectories for robot manipulators is discussed. The minimum time trajectory planning problem for manipulators is one of the minimum time control problems of non-linear systems. The optimal input torque/force is of a bang-bang type, except for the singular control derived from the Maximum Principle. An algorithm for generating a bang-bang control is proposed. In the algorithm, the switching time vector is updated to decrease the final state error. The proposed algorithm is applied to a simple manipulator with two links, and the solution by this algorithm is compared with the sub-optimal solution obtained by another approximate method.

A novel approach to control synthesis of biped locomotion mechanisms is suggested. The synthesis is carried out in two stages: the stage of nominal regimes (the synthesized control has to ensure the realization of gait in the absence of any disturbance), and the stage of perturbed regimes (the control has to eliminate deviations from the nominals under an additional condition of preserving the stability of the overall System). At the level of perturbed regimes, the proposed control synthesis should ensure the compensating movements such to bring the System ot its nominal during the gait. However, the compensating movements can, as a side–effect, induce the undesirable inertial forces which can influence the mechanism overall stability. To avoid this, it is suggested such a control which ensures that the acceleration of compensating movements does not exceed a value given in advance. In addition, the case is considered when an additional correction of the zero moment point ZMP position is accomplished by different mechanism joints.

Navigation and collision avoidance are major areas of research in mobile robotics that involve varying degrees of uncertainty. In general, the problem consists of achieving sensor based motion control of a mobile robot among obstacles in structured and/or unstructured environments with collision-free motion as the priority. A fuzzy logic based intelligent control strategy has been developed here to computationally implement the approximate reasoning necessary for handling the uncertainty inherent in the collision avoidance problem. The fuzzy controller was tested on a mobile robot system in an indoor environment and found to perform satisfactorily despite having crude sensors and minimal sensory feedback.