Chee Khiang Pang's research while affiliated with Singapore Institute of Technology (SIT) and other places

Intelligent and accurate determination of the position and orientation, or pose, of a workpiece which is manually placed is essential for automating fabrication tasks such as welding. In this paper, a novel algorithm based on minimizing the area of a boundary enclosing partial scan data points is proposed for detecting both the pose and assembly of tubular joints with the aid of reference ideal models. The proposed algorithm can also be applied to tubular joints with non-cylindrical cross sections. The fit-up information obtained can be used to determine whether realignment is required or combined with the pose information to re-plan paths for subsequent tasks. The focus of existing state-of-the-art is on objects with features, and the localization of featureless objects such as generic tubular joints using partial and sparse scan data remains a challenge. The proposed algorithm is applied to an actual robotic welding system to locate a tubular workpiece. Experiment results using the scan data as ground truth show that root mean square error is less than 1% of the pipe diameters, considering both brace and chord components with diameters greater than 200 mm.

Robotic multi-pass welding for thick and shape-varying weld geometry is a challenging problem. To achieve good weld quality, desired welding profile for the whole welding bevel is necessary, which requires the welding inputs to be changed appropriately in real-time. In this paper, a welding profile control with data-driven fast input allocation (PC-FIA) algorithm is proposed for robotic multi-pass welding on a shape-varying weld geometry, namely, TYK pipe-to-pipe joint. Firstly, the H∞ control algorithm is used for the welding profile control in order to suppress the error propagation during the multi-pass welding. Secondly, the welding input parameters including torch traveling speed and weaving parameters are allocated using a max−min optimization based on the identified weld input constraint from a data-driven approach. Experimental results show that the weld profile using the proposed method achieves 60% decrease of root-mean-square error with respect to the planned reference, as compared to the case of without using the proposed PC-FIA method. In addition, the allocated weaving parameters ensure that the welding inputs are always maintained within the polyhedral constraint and the whole welding quality is acceptable by industry standard.

We solve the problem of decentralized uniform finite‐time stabilization for a large‐scale network of nonlinear systems in triangular form which are not feedback linearizable and whose input–output maps are not invertible. For this, we provide a new recursive design to satisfy the conditions of a certain modification of the small‐gain theorems for the case of the uniform finite‐time stability of large‐scale networks. In the general case, we consider the general triangular form systems whose input–output links are surjections only. In addition, we consider the special case of the triangular form systems with polynomial integrators, which is also needed as the first step of the proof of our main result. In the latter case, the design becomes constructive, the decentralized stabilizers can be designed explicitly, and this is demonstrated by examples.

This paper develops a novel Lyapunov function candidate for control of the three-dimensional (3-D) overhead crane, which yields a nonlinear controller to inject active damping. Different from the existing passivity-based controls that employ either the angular displacement or its integral as passive elements, the proposed controller incorporates both of them in a new coupled-dissipation signal, thus significantly enhancing the closed-loop passivity. Owing to the improved passivity, the proposed controller ensures the effective suppression of payload oscillations and robustness. Moreover, the control design is extended with the hyperbolic tangent function to prevent overdriving the trolley. The asymptotic stability is guaranteed by LaSalle’s invariance principle. The transit performance of the closed-loop system, including robustness, is validated by numerical simulations.

Multiple‐conditioned welding monitoring is a challenging issue in complex robotic welding tasks. In practice, the monitoring system has to be sensitive to different welding conditions (WCs) and weld quality changes. In this paper, a swing high temperature sensor system is used in order to obtain the temperature distribution curve under different WCs. A sigmoid feature extraction (SFE) method is proposed to obtain the geometric features of the temperature distribution curve, and a weld monitoring algorithm is proposed for multiple‐conditioned welding tasks using multi‐layer perceptron (MLP) classifier and Kalman filter‐based Gaussian probability density function (PDF) prediction for the probabilistic weld quality estimation. When there are unknown WCs, the proposed method uses an efficient incremental learning for the MLP and an online maximum likelihood estimation for the Gaussian models of the unknown WCs. The experimental results show that the proposed framework can accurately reflect the weld quality changing in both single‐WC and multiple‐WC tasks. In addition, the proposed adaptive updating methodology can achieve comparable performance with unknown WCs, as compared to the results with knowing all the WCs.

This paper studies the scheduling problem for the manufacturing systems with uncertain job duration, and the possibility of planning due-date quotations for critical manufacturing tasks given a fixed contingency budget. We propose a due-date quotation model to measure the risk of delay in the manufacturing process in terms of the allocated contingency budget. The risk of delay is measured in the same unit as its corresponding milestone factor such that the decision makers could directly visualize and quantify the level of risks in units of hours or days. In addition, the proposed model possesses various great properties required by a convex risk measure and it represents a minimized certainty equivalent of the overall expected risk in achieving the manufacturing due-dates. Extensive computational experiments are conducted to evaluate the model performance. The results show that our proposed model, compared to various existing methods, provides a much more balanced performance in terms of success rate of due-date achievement, due-date quotation shortfall, as well as, robustness against uncertainties. The practical applicability of the proposed models are also tested with the job scheduling problem in a real stamping industry application.

This paper studies two-stage disruption exposure minimization problems, motivated by the supply disruption issues in the energy and water supply systems. In particular, we address the ambiguity in both the probability distribution and risk preference of decision-makers towards disruption exposures. First, we propose a two-stage distributionally robust model with adjustable uncertainty sets, which solves a supply system solution with the least possible disruption exposures. We show that this two-stage robust disruption exposure model can be reduced to a computationally attractive single-stage mixed-integer linear program. We then propose an extended almost-robust disruption guarantee model to account for the ambiguity in the risk preference of decision-makers. We demonstrate that this almost-robust guarantee model can reveal clear preferences of most decision-makers under limited distribution information, which however does not resort to any particular disutility function specification and can be solved efficiently using a binary search algorithm. A decision support framework is also developed to guide users on how to apply the proposed disruption exposure models. Finally, we apply the proposed models to a distributed energy supply system design problem. Numerical results show that our models significantly outperform a risk-neutral model in hedging against a broad set of supply distributions. Moreover, the almost-robust guarantee model exhibits its advantages in hedging against high disruption levels, and performs the best under the vast majority of distributions regarding all tested statistical criteria.

We propose a model to solve a project scheduling problem where resource assignments and activity schedules need to be determined to achieve a set of due-date requirements as well as possible. The concept of activity duration tolerance levels is introduced to describe the longest activity durations over which the due-dates are guaranteed to be achieved. Based on this, we propose a due-date achievement model using a proposed performance measure termed as the activities exposure level, which accounts for the total amount of durations exceeding tolerance levels for a given scheduling solution. We describe various properties and characteristics of the proposed scheduling optimization model and its practical implications. We show that the associated stochastic scheduling problem with resource constraints can be equivalently computed as the solution of a modest-sized mixed-integer linear program using the sample average approximation approach. Computational results show that our proposed models perform well when compared to different standard approaches across various performance measures.

Self-sensing actuators use a single piezoelectric element as actuators and sensors simultaneously. This paper proposes the asymmetric indirect-driven self-sensing actuation (AIDSSA) circuit to realize the concept of self-sensing in piezoelectric-actuated systems. Unlike traditional circuits relying on differential amplifiers, the AIDSSA circuit is constructed with only op-amps and uses negative feedback to reject the common-mode interferences from the control command. The new circuit requires simpler conditions of component matching and is able to sense the mechanical responses with a uniform gain and without a phase lag. The actuator is able to achieve full-stroke actuation while sensing is performed, because AIDSSA introduces no undesirable dynamics into the control loop. For the first time, the sensing and actuation transfer functions in self-sensing actuators have become fully decoupled at all frequencies. The investigation takes the form of an industrial application of hard disk drives, and demonstrates the usefulness the circuit in complex positioning systems. Experimental results show that the position error variance, a measure of disturbance rejection capability, has been improved by about 15% in the track-following mode relative to the same servo before modifications.

We propose a method for uniform decentralized stabilitzation for a class of large-scale networks of nonlinear switched systems in lower-triangular form with unknown arbitrary switchings. In contrast to the class of networks with the same structure of interconnections that was considered in [1], the dynamics of each subsystem of our network satisfies more general conditions and its input-output maps are not necessarily right invertible, for instance, they can have dead zones. Another difference from [1] is that the current result is proved by a different approach. Instead of using a small-gain theorem in terms of ISS Lyapunov functions as in [1], we use the trajectory-based small-gain theorem for switched systems proved in [2].