A decentralized adaptive control system (ACS) for a four motor-generator four-wheel drive hybrid electric vehicle (HEV) is designed and its ability to deal with unknown tire dynamics, changing road surfaces, and vehicle loading is evaluated. A system composed of four separate adaptive controllers is designed to control the vehicle's speed, steering, side slip, and energy management system. A nonlinear simulation model for the vehicle dynamics and its power train components is developed and used to evaluate the performance of the ACS, while the vehicle is simultaneously turning and accelerating or braking under varying loading and icing conditions.

Unlike the many soft computing applications where it suffices to achieve a "good approximation most of the time," a control system must be stable all of the time. As such, if one desires to learn a control law in real-time, a fusion of soft computing techniques to learn the appropriate control law with hard computing techniques to maintain the stability constraint and guarantee convergence is required. The objective of the paper is to describe an adaptive dynamic programming algorithm (ADPA) which fuses soft computing techniques to learn the optimal cost (or return) functional for a stabilizable nonlinear system with unknown dynamics and hard computing techniques to verify the stability and convergence of the algorithm. Specifically, the algorithm is initialized with a (stabilizing) cost functional and the system is run with the corresponding control law (defined by the Hamilton-Jacobi-Bellman equation), with the resultant state trajectories used to update the cost functional in a soft computing mode. Hard computing techniques are then used to show that this process is globally convergent with stepwise stability to the optimal cost functional/control law pair for an (unknown) input affine system with an input quadratic performance measure (modulo the appropriate technical conditions). Three specific implementations of the ADPA are developed for 1) the linear case, 2) for the nonlinear case using a locally quadratic approximation to the cost functional, and 3) the nonlinear case using a radial basis function approximation of the cost functional; illustrated by applications to flight control.

An adaptive control system for a 4 motor/generator/4 wheel drive hybrid electric vehicle is designed and its ability to deal with unknown tire dynamics, changing road surfaces and vehicle loading is evaluated. A system composed of four separate adaptive controllers is designed to control the vehicle's speed, steering, side slip and energy management system.

A simple, physically insightful method for dynamic modeling of manipulators containing closed kinematic chains is presented. Founded on D'Alembert's principle, the method centers around a transformation from the well-understood dynamics of serial and open-chain mechanisms to closed-chain dynamic robot models. The framework for the closed-chain dynamic robot model fosters straightforward extensions of serial robot engineering activities in the areas of digital simulation, real-time control, parameter identification, and optimal path planning to closed-chain manipulators. The algorithms needed to realize these extensions are detailed, and the important concepts are highlighted with an example.>

In 1983, the computer program called algebraic robot modeler (ARM) was implemented to generate symbolically complete closed-form and recursive dynamic robot models. To enhance computational efficiency for dynamic simulation and real-time control, the systematic organization of these symbolic models was introduced in 1985 to remove repetitive calculations within and across equations and thereby generate customized robot dynamics algorithms. The systematic organization procedure of ARM is detailed and its performance compared with documented organizations. It is pointed out that the systematic organization procedure, and the exploitation of kinematic and dynamic manipulator structures through symbolic modeling, make ARM-generated customized algorithms the most computationally efficient manipulator dynamics algorithms of those compared.>

In 1983, the computer program Algebraic Robot Modeler (ARM) was implemented to generate symbolically complete closed-form and recursive dynamic robot models. Then, in 1985, heuristic rules for the systematic organization of dynamic robot models were incorporated in ARM to reduce the computational requirements of forward and inverse dynamics calculations. For the robotics and control engineering communities, the dynamic coefficients and the complete closed-form ARM-generated symbolic dynamic model of the six degree-of-freedom (DOF) Puma robot (which has not heretofore appeared in the literature) are displayed. The performance of ARM is compared with recently introduced computational robot dynamics software. In spite of the analytic and numeric complexities of the complete closed-form dynamic model of the Puma robot, our findings indicate that 1) ARM-generated customized closedform algorithms can compute the inverse dynamics (i.e., the joint torques from the joint positions, velocities, and accelerations) twice as fast as the general-purpose recursive Newton-Euler algorithm, and 2) the ARM-generated customized recursive inverse dynamics algorithm can be evaluated twice as fast as the customized closed-form inverse dynamics algorithm.

The computer program ARM (Algebraic Robot Modeler) has been implemented to generate symbolically the forward solution and complete Lagrangian dynamic robot model for control engineering applications. Development and application of this versatile dynamic modeling and control engineering tool are highlighted in this paper. The Q matrix formulation is employed to develop nested iterative algorithms for the symbolic computation of the inertial, centrifugal and Coriolis, and gravitational components of the Lagrangian dynamic robot model. The computational requirements are enumerated as functions of the number of degrees-of-freedom of the manipulator. Automatic generation of the centrifugal and Coriolis force vector dominates the computational load for both state-of-the-art and futuristic robots. The forward solution and complete dynamic model for the three degree-of-freedom positioning system of the Puma robot are exhibited to illustrate the capabilities of Arm. On-going enhancements to ARM are then summarized.

The authors have implemented the computer program Algebraic Robot Modeler (ARM) to generate symbolically the forward solution and complete dynamic robot model for control engineering applications. ARM incorporates theQ-matrix formulation of the Lagrangian (Lagrange-Euler) dynamic robot model. The symbolic formulation of dynamic robot models is extended in two directions:Q-matrices and the Bejczy theorem are applied to linearize symbolically the Lagrangian dynamic robot model about a nominal trajectory; and a methodology is developed to generate symbolically the trajectory sensitivity models of a manipulator with respect to the kinematic and dynamic link parameters. The classical sensitivity point method is thereby extended to include dynamic robot models. Control engineering applications of the linearized and sensitivity models are described. The properties and structural characteristics of these models are identified, and the computational requirements for their implementation in ARM are estimated. The results are illustrated through the double pendulum.