Look-ahead Capabilities Research Articles

Pre-compensation of servo contour errors using a model predictive control framework

Methods for pre-compensating contour errors in servo systems by adding components of the predicted contour error to the reference position command have recently been proposed in the literature. Such methods are very effective when the curvatures of the desired path are small but their performance degrades at locations of sharp curvature because they lack look-ahead capabilities. This paper presents an improved method for pre-compensating contour errors in servo systems by modifying reference position commands using a model predictive control framework. The pre-compensation value at any given location along the desired path is defined as a weighted average of contour errors within a prediction horizon, and the weights are selected to minimize the sum of squares of the estimated contour errors over the chosen prediction horizon. Constraint enforcement functionalities are also built into the proposed method to ensure that the pre-compensated reference commands stay within specified velocity and acceleration limits. Simulations and experiments are used to compare the performance of the proposed method to a recently proposed pre-compensation approach which lacks look-ahead and constraint enforcement capabilities. Significant improvements in contouring accuracy over the existing method are demonstrated.

An advanced optical system for laser ablation propulsion in space

We propose a novel space-based ablation driven propulsion engine concept utilizing transmitted energy in the form of a series of ultra-short optical pulses. Key differences are generating the pulses at the transmitting spacecraft and the safe delivery of that energy to the receiving spacecraft for propulsion. By expanding the beam diameter during transmission in space, the energy can propagate at relatively low intensity and then be refocused and redistributed to create an array of ablation sites at the receiver. The ablation array strategy allows greater control over flight dynamics and eases thermal management. Research efforts for this transmission and reception of ultra-short optical pulses include: (1) optical system design; (2) electrical system requirements; (3) thermal management; (4) structured energy transmission safety. Research has also been focused on developing an optical switch concept for the multiplexing of the ultra-short pulses. This optical switch strategy implements multiple reflectors polished into a rotating momentum wheel device to combine the pulses from different laser sources. The optical system design must minimize the thermal load on any one optical element. Initial specifications and modeling for the optical system are being produced using geometrical ray-tracing software to give a better understanding of the optical requirements. In regards to safety, we have advanced the retro-reflective beam locking strategy to include look-ahead capabilities for long propagation distances. Additional applications and missions utilizing multiplexed pulse transmission are also presented. Because the research is in early development, it provides an opportunity for new and valuable advances in the area of transmitted energy for propulsion as well as encourages joint international efforts. Researchers from different countries can cooperate in order to find constructive and safe uses of ordered pulse transmission for propulsion in future space-based missions.

TECHNICAL NOTE—The Adaptive Knapsack Problem with Stochastic Rewards

Given a set of items with associated deterministic weights and random rewards, the adaptive stochastic knapsack problem (adaptive SKP) maximizes the probability of reaching a predetermined target reward level when items are inserted sequentially into a capacitated knapsack before the reward of each item is realized. This model arises in resource allocation problems that permit or require sequential allocation decisions in a probabilistic setting. One particular application is in obsolescence inventory management. In this paper, the adaptive SKP is formulated as a dynamic programming (DP) problem for discrete random rewards. The paper also presents a heuristic that mixes adaptive and static policies to overcome the “curse of dimensionality” in the DP. The proposed heuristic is extended to problems with normally distributed random rewards. The heuristic can solve large problems quickly, and its solution always outperforms a static policy. The numerical study indicates that a near-optimal solution can be obtained by using an algorithm with limited look-ahead capabilities.

Task Sequencing for High-Level Sensor-Based Control

Classical sensor-based approaches tend to constrain all the degrees of freedom of a robot during the execution of a task. In this paper, a new solution is proposed. The key idea is to divide the global full-constraining task into several subtasks, which can be applied or inactivated to take into account potential constraints of the environment. Far from any constraint, the robot moves according to the full task. When it comes closer to a configuration to avoid, a higher level controller removes one or several subtasks, and activates them again when the constraint is avoided. The last controller ensures the convergence at the global level by introducing some look-ahead capabilities when a local minimum is reached. The robot accomplishes the global task by automatically sequencing sensor-based tasks, obstacle avoidance, and short deliberative phases. In this paper, a complete solution to implement this idea is proposed, along with several experiments that prove the validity of this approach

An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem

This paper introduces a new neighborhood search technique, called dynasearch, that uses dynamic programming to search an exponential size neighborhood in polynomial time. While traditional local search algorithms make a single move at each iteration, dynasearch allows a series of moves to be performed. The aim is for the lookahead capabilities of dynasearch to prevent the search from being attracted to poor local optima. We evaluate dynasearch by applying it to the problem of scheduling jobs on a single machine to minimize the total weighted tardiness of the jobs. Dynasearch is more effective than traditional first-improve or best-improve descent in our computational tests. Furthermore, this superiority is much greater for starting solutions close to previous local minima. Computational results also show that an iterated dynasearch algorithm in which descents are performed a few random moves away from previous local minima is superior to other known local search procedures for the total weighted tardiness scheduling problem.

Knowledge elicitation in scheduling FMS: Towards a hybrid intelligent system

The paper presents preliminary information on a methodology for developing a hybrid intelligent system for scheduling FMS. For this purpose a computer simulator for FMS was developed with interactive and look-ahead capabilities. The results have indicated that the protocol analysis with a GOMS model is a useful tool to elicit knowledge for FMS scheduling in a real-time interactive environment. Furthermore, the elicited knowledge was transferred into a computer as a knowledge-based system for FMS scheduling. The hybrid intelligence, which is a combined model of human intelligence and machine intelligence, includes knowledge-based systems, traditional algorithms and human decision making.

Toward a hybrid intelligent system for scheduling and rescheduling of FMS

Abstract The paper gives preliminary information on the methodology for developing and validating the use of hybrid intelligence for scheduling and rescheduling FMS. For this purpose a computer simulator for FMS was developed with interactive and look-ahead capabilities. During the subjects' performance of the task both performance data and verbal protocols were collected. These were compared with traditional dispatching rules. Results indicate the effectiveness of using hybrid intelligence for scheduling FMS. The hybrid intelligence, which is a fusion model of human intelligence and machine intelligence, includes expert systems (from verbal protocol), traditional algorithms and human decision-making.