The material presented in this monograph is a logical continuation of research results achieved in the control of manipulation robots. This is in a way, a synthesis of many-year research efforts of the associates of Robotics Department, Mihailo Pupin Institute, in the field of dynamic control.of robotic systems. As in Vol. 2 of this Series, all results rely on the mathematical models of dynamics of active spatial mechanisms which offer the possibility for adequate dynamic control of manipula­ tion robots. Compared with Vol. 2, this monograph has three essential new character­ istics, and a variety of new tasks arising in the control of robots which have been formulated and solved for the first time. One of these novelties is nonadaptive control synthesized for the case of large variations in payload parameters, under the condition that the practical stability of the overall system is satisfied. Such a case of control synthesis meets the actual today's needs in industrial robot applications. The second characteristic of the monograph is the efficient adaptive control algorithm based on decentralized control structure intended for tasks in which parameter variations cannot be specified in advance. To be objective, this is not the case in industrial robotics today. Thus, nonadaptive control with and without a particular parameter variation is supplemented by adaptive dynamic control algorithms which will cer­ tainly be applicable in the future industrial practice when parametric identification of workpieces will be required.

Inhalt

1 Computer-Assisted Generation of Robot Dynamic Models in Analytical Form.- 1.1. Introduction.- 1.2. A closed form dynamic model of a robotic manipulator.- 1.3. Analytical representation of the variables of dynamic model.- 1.4. Construction of real-time program code.- 1.5. Example.- References.- 2 Non-Adaptive Control of Manipulation Robots with Variable Parameters.- 2.1. Introduction.- 2.2. Mathematical model of manipulation robots and control task definition.- 2.2.1. Mathematical model.- 2.2.2. Control task definition.- 2.3. Survey of non-adaptive control algorithms.- 2.3.1. Optimal control synthesis.- 2.3.2. Optimal regulator.- 2.3.3. "Inverse problem" technique.- 2.3.4. Force feedback.- 2.3.5. Decoupled control.- 2.3.6. Decentralized control.- 2.3.7. Other approaches.- 2.4. Synthesis of local controllers.- 2.4.1. Optimal servosystems.- 2.4.2. Robust servo-controllers.- 2.4.3. Synthesis of local controller by pole-placement method.- 2.4.4. Local control task.- 2.5. Stability analysis of manipulation robots.- 2.6. Global control synthesis.- 2.7. Example.- Conclusion.- References.- Appendix 2A Analysis of the Influence of Actuator Models Complexity on Manipulator Control synthesis.- Appendix 2B Practical Stability of Manipulation Robots.- 3 Adaptive Control Algorithms.- 3.1. Introduction.- 3.2. Centralized adaptive control of robot manipulators.- 3.2.1. Self-tuning control strategy.- 3.2.2. Centralized reference model-following control.- 3.2.3. Indirect centralized adaptive control.- 3.3. Decentralized adaptive control strategy for mechanical manipulators.- 3.3.1. Self-tuning local controllers.- 3.3.2. Decentralized reference model-following control.- 3.3.3. Indirect decentralized adaptive control.- References.- 4 Computer-Aided Control Synthesis.- 4.1. Introduction.- 4.2. Software package for computer aided synthesis.- 4.2.1. Block for setting data on manipulator structure.- 4.2.2. Block for calculating desired kinematic trajectory.- 4.2.3. Block for setting dynamic parameters.- 4.2.4. Block for setting data on actuators.- 4.2.5. Block for synthesis of nominal dynamics.- 4.2.6. Block for setting control task.- 4.2.7. Block for local control synthesis.- 4.2.8. Block for stability analysis of decentralized control.- 4.2.9. Block for global control synthesis.- 4.2.10. Block for adaptive control synthesis.- 4.2.11. Block for discrete-time control synthesis.- 4.2.12. Block for simulation.- 4.3. Example.- Conclusion.- References.- 5 Implementation of Control Algorithms.- 5.1. Introduction.- 5.2. Concept of general purpose controller.- 5.3. Numerical complexity of control laws.- 5.3.1. Numerical complexity of dynamic models.- 5.3.2. Numerical complexity of non-adaptive decentralized control laws.- 5.3.3. Numerical complexity of adaptive control laws.- 5.4. Parameter identification.- 5.5. Microprocessor implementation of non-adaptive control laws.- References.