Parameter identification and control of linear discrete-time systems

The parameter-adaptive self-organizing control of linear discrete-time systems is considered by designing dynamic feedback controllers which depend on the estimates of the parameters provided by an appropriate identifier. Two stochastic approximation algorithms for consistent identification of feedback systems are investigated and a condition of identifiability is presented. Then two controllers, one based on "overall" and another based on "per-interval" optimization, both depending on the output of the identifier, are discussed and their evaluations relative to the optimal are compared in illustrative examples.     

Optimal control of global entropy for environmental systems

It has been accepted worldwide that environmental decay, like pollution, the greenhouse effect, or even traffic congestion, has been a result of the work produced to improve the quality of life, thus producing waste measured by entropy in the thermodynamic sense. The author proposes the use of optimal control theory to optimize the effects of human intervention in societal, ecological, economic, environmental and other current problems of our modern society by minimizing the maximum global entropy that represents their decay. An analytic method is presented to demonstrate the reduction of the human participation     

Coordination specification for CIRSSE robotic platform system using Petri net transducers

A formal model based on Petri net transducers has been developed to specify the coordination and communication among the various task units in the CIRSSE platform system for robotic construction in space stations. The specification guarantees a mechanism of coherent control and communication for the effective cooperation among the different task units, and outlines the major steps toward the integration of the robotic platform system. The model is based on a coordination structure consisting of one dispatcher and three coordinators representing, respectively, the motion, vision, and gripper units of the platform system. The coordination structure insures some desired process properties for the system, such as boundedness, liveness, and reversibility, and easier translation from the formal specifications to the program codes based on Petri net transducer models. The model also assists with the system development in many ways, including (i) reducing the number of errors introduced while converting specifications to codes; (ii) assisting the developers in program implementation and verification (iii) allowing quicker adaptation to changed specifications; and (iv) allowing easier testing of the results for specification modifications. Therefore, it provides a useful tool for the design, simulation, performance evaluation, and implementation verification of the CIRSSE robotic platform system.     

The control of a prosthetic arm by EMG pattern recognition

An electromyographic (EMG) signal pattern recognition system is constructed for real-time control of a prosthetic arm through precise identification of motion and speed command. A probabilistic model of the EMG patterns is first formulated in the feature space of integral absolute value (IAV) to describe the relation between a command, represented by motion and speed variables, and location and shape of the corresponding pattern. The model provides the sample probability density function of pattern classes in the decision space of variance and zero crossings based on the relations between IAV, variance, and zero crossings established in this paper. Pattern classification is carried out through a multiclass sequential decision procedure designed with an emphasis on computational simplicity. The upper bound of probability of error and the average number of sample observations are investigated. Speed and motion predictions are used in conjunction with the decision procedure to enhance decision speed and reliability. A decomposition rule is formulated for the direct assignment of speed to each primitive motion involved in a combined motion. A learning procedure is also designed for the decision processor to adapt long-term pattern variation. Experimental results are discussed in the Appendix.     

Entropy formulation of optimal and adaptive control

The use of entropy as the common measure to evaluate the different levels of intelligent machines is reported. At the execution level, the design of the desirable control can be expressed by the uncertainty of selecting the optimal control that minimizes a given performance index. By choosing a density function over the set of admissible controls to minimize the differential control entropy, it can be shown that the optimal control problem is equivalent to the problem of minimization of the assigned entropy function with respect to the association control. The adaptive control problem can be analyzed by considering the same entropy over extended space that includes the uncertain parameters. It is shown that the optimal entropy is decomposed into three terms: the optimal control term with given parameters, the parameter identification term, and the equivocation term which accounts for the active transition of dual control. The equivocation when calculated can serve as a measure of optimality of the adaptive control algorithms that involve only distinct identification and optimal control algorithms. An upper bound can be used instead, when the equivocation is hard to calculate. An example illustrates the method     

Optimal and suboptimal motion planning for collision avoidance of mobile robots in non-stationary environments

An optimal control formulation of the problem of collision avoidance of mobile robots moving in terrains containingmoving obstacles is presented. A dynamic model of the mobile robot and the dynamic constraints are derived. Collision avoidance is guaranteed if the minimum distance between the robot and the objects is nonzero. A nominal trajectory is assumed to be known from off-line planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. Furthermore, time consistency with the nominal plan is desirable. Two solutions are obtained: (1) A numerical solution of the optimization problem and a perturbation type of control to update the optimal plan and (2) A computationally efficient method giving near optimal solutions. Simulation results verify the value of the proposed strategies and allow for comparisons.     

Intelligent control for urban traffic systems

This paper proposes a hierarchically intelligent control procedure to resolve certain aspects of the urban traffic management problem. Three levels of the hierarchical system: the linguistic organization, the coordinator, and the on-line controller are responsible, respectively, for the management of traffic in city, neighborhood, and single intersection. Traffic situations are partitioned into five different categories, ranging from sparse to immobile traffic. For each category (except the immobile traffic which is not probed in this paper) a suitable control scheme is introduced. The control at the lowest level, single intersection, will be exerted by optimizing an assigned subgoal. Each coordination level will manage the interaction among the single intersections within its jurisdiction. A learning algorithm is incorporated at this level in order to reduce considerably the need for extensive a priori numerical computations.