Robustness analysis of Petri nets for assembly/disassembly processes with unreliable resources

In most application of Petri nets, resources are modeled as tokens. Unreliable resources pose challenges as existing Petri net theory is deficient in analyzing the impacts of the perturbation due to resource failures. One strategy to analyze a perturbed system is to study its robustness with respect to uncertainties. In this paper, we study the robustness of a class of controlled Petri nets called controlled assembly/disassembly Petri net (CADPN) for assembly/disassembly processes with unreliable resources. The number of tokens in CADPN is not conservative to capture the effects of resource failures. Based on the CADPN model, we characterize different types of tolerable resource failures allowed for a nominal marking of a live CADPN. We show that liveness of a CADPN can still be preserved under tolerable resource failures.     

Modular product platform configuration and co-planning of assembly lines using assembly and disassembly

Formation of products platforms is carried out during the planning stage and very often separately from the planning of corresponding assembly lines. There is a dearth of literature which considers the different aspects of fully integrating platform design, product family formation, assembly line design, delayed product differentiation, and new concepts of mass customization. A Modular Product Platform Configuration model which uses assembly and disassembly for configuring product variants and Co-Planning of products platforms (MPCC) and their assembly Lines is presented. It is used to co-plan the common platform components and the associated product families simultaneously with the planning of its corresponding mixed-model assembly line. Using both assembly and disassembly to customize the product family platform in order to generate product variants is not commonly discussed in literature. It is defined as the formation of platforms for use to derive multiple products by including many components not shared by every product. The platform is then customized by assembling or disassembling components to form different product variants. The model is formulated using mixed integer mathematical programming to minimize the number of assembly stations and cycle time. Two case studies are used for verification and demonstration. They illustrated the ability of the MPCC model to integrate the planning of product platform, product families and the number of assembly stations required to assemble and disassemble components from mass-assembled product platforms to derive new product variants.     

Intelligent adaptive observer-based optimal control of overhead transmission line de-icing robot manipulator

In cold season, wet snow ice accretion on overhead transmission lines increases wind load effects which in turn increases line tension. This increased line tension causes undesirable effects in power systems. This paper discusses the design of an observer-based boundary sliding mode control (BSMC) for 3 DOF overhead transmission line de-icing robot manipulator (OTDIRM). A robust radial basis functional neural network (RBFNN) observer-based neural network (NN) controller is developed for the motion control of OTDIRM, which is a combination of BSMC, NN approximation and adaptation law. The RBFNN-based adaptive observer is designed to estimate the positions and velocities. The weights of both NN observer and NN approximator are tuned off-line using particle swarm optimization. Using Lyapunov analysis the closed loop tracking error was verified for a 3 DOF OTDIRM. Finally, the robustness of the proposed neural network-based adaptive observer boundary sliding mode control (NNAOBSMC) was verified against the input disturbances and uncertainties.     

Smooth control of an articulated mobile robot with switching constraints

The paper describes a smooth controller of an articulated mobile robot with switching constraints. The use of switching constraints associated with grounded/lifted wheels is an effective method of controlling various motions; e.g. the avoidance of a moving obstacle. A model of an articulated mobile robot that has active and passive wheels and active joints with switching constraints is derived. A controller that accomplishes the trajectory tracking of the robot’s head and subtasks using smooth joint input is proposed on the basis of the model. Simulations and experiments are presented to show the effectiveness of the proposed controller.     

Projection operator-based robust adaptive control of an aerial robot with a manipulator

This paper describes a quadcopter manipulator system, an aerial robot with an extended workspace, its controller design, and experimental validation. The aerial robot is based on a quadcopter with a three degree of freedom robotic arm connected to the base of the vehicle. The work aims to create a stable airborne robot with a robotic arm that can work above and below the airframe, regardless of where the arm is attached. Integrating a robotic arm into an underactuated, unstable system like a quadcopter can enhance the vehicle's functionality while increasing instability. To execute a mission with accuracy and reliability during a real-time task, the system must overcome the inter-coupling effects and external disturbances. This work presents a novel design for a robust adaptive feedback linearization controller with a model reference adaptive controller and hardware implementation of the quadcopter manipulator system with plant uncertainties. The closed-loop stability of the aerial robot and the tracking error convergence with the robust controller is analyzed using Lyapunov stability analysis. The quadcopter manipulator system is custom developed in the lab with an off-the-shelf quadcopter and a 3D-printed robotic arm. The robotic system architecture is implemented using a Jetson Nano companion computer for autonomous onboard flight. Experiments were conducted on quadcopter manipulator system to evaluate the autonomous aerial robot's stability and trajectory tracking with the proposed controller.     

Optimal design and workspace analysis of a mobile welding robot with a 3P3R serial manipulator

This paper presents the design optimization of a mobile welding robot based on the analysis of its workspace. A welding robot has been developed to be used inside the double-hull structure of ships, and it shows good welding functionality. But there is a need to optimize the kinematic variables ensuring that the required welding functions inside the ships are satisfied. The task-oriented workspace, which is the workspace enabling specific rotations, has been defined in order to validate the welding ability of the robot, and incorporating the required rotational capabilities. To calculate the workspace, a geometric approach is adopted which considers the pitching and yawing angles simultaneously. Based on the workspace analysis, a scenario is compiled for considering a mass reduction, and a ratio between the design parameters and the workspace, with constraints on the workspace margins. The proposed optimization procedure is composed of two steps of coarse and fine searching. In the coarse searching step, a feasible parameter region (FPR) is defined, which satisfies the geometrical design constraints, and can be obtained without any considerations of the objective functions. In the fine searching step, the design parameters are determined by using the optimization technique of the conjugate gradient method in the overall FPRs. The suggested approach to calculating the task-oriented workspace, and the procedure of optimal design, are expected to be applied to general industrial robots.     

Modeling,tracking, vibration and balance control of an underactuated mobile manipulator (UMM)

This paper presents an underactuated mobile manipulator (UMM) and focuses on solving modeling, tracking, and vibration- and balance-control problems. Although the study has been directed at warehousing applications, the developed techniques are general and can be applied to other applications. The derivation of equations of motion of the UMM, disturbance analysis, and model validation are investigated to reveal the actual system dynamics. Additionally, a simple but effective strategy is also developed to solve the equilibrium point and balance problem. Based on the dynamic model, two control architectures are proposed: Model Predictive Control (MPC) and MPC+Proportional-Integral (PI) with integral actions, respectively, and they can also be applied to other robotic systems. Compared to other MPC-based control strategies, the proposed controllers require less effort to implement in practice. Finally, simulations, experiments, and robustness verification are conducted and discussed, and the results are satisfactory.     

双闭环策略下非完整轮式机器人鲁棒自适应运动/力协调控制

非完整轮式移动机器人的路径跟踪,需要在保证机器人姿态跟踪精度的同时,增强其地面适应性能.为实现这种运动/力的协调控制目标,本文提出双闭环的控制系统结构:外环能够增加运动精度,内环则可以增强机器人对地面动态摩阻的适应性.同时,考虑到地面摩阻的慢时变性,本文通过构造观测器对其进行估计.在具体算法实现方面,采用反步法在外环构建运动控制器:而在内环,则是应用积分型的滑模技术设计力控制器与观测器.最后,对控制系统进行仿真,仿真结果证明所提出控制方法的有效性.     

Motion control and obstacle avoidance of a mobile robot with an onboard manipulator

Navigation and control of autonomous mobile vehicles with onboard manipulator systems are currently being investigated for intelligent manufacturing applications. A systematic approach for modeling and base motion control of a mobile vehicle with an onboard robot arm is presented. Feedback linearization is used to take into account the complete dynamics with non-holonomic constraints, yet methods from potential field theory are incorporated to provide resolution among possibly conflicting performance goals (e.g. path following and obstacle avoidance). The feedback linearization provides an inner loop that accounts for possible motion of the onboard arm. The two cases of maintaining a desired course and speed, and following a desired Cartesian trajectory are considered. The outer control loop is designed using potential field theory, with the two objectives of homing and avoiding an obstacle. This simple result obtained using potential functions provides very naturally the necessary intelligence for online resolution of conflicting performance objectives. It gives capabilities to these autonomous vehicles for maintaining a desired course and speed or tracking a Cartesian trajectory, avoiding obstacles during the course of travel, and initiating new online path planning when the size of the object is large so that unnecessary wandering in the work space is avoided.     

Robust neural network–based tracking control and stabilization of a wheeled mobile robot with input saturation

This paper presents a robust neural network–based control scheme to deal with the problem of tracking and stabilization simultaneously for a wheeled mobile robot subject to parametric uncertainties, external disturbances, and input saturation. At first, a new error‐state transformation scheme is designed by introducing some auxiliary variables as an additional virtual control signals to reduce the adverse effect caused by the underactuation. These variables can change their structures for different desired trajectories to be tracked. Then, a robust control law is proposed combining with a kinematic controller and a dynamic controller, while a three‐layer neural network system is applied to approximate model uncertainties. Stability analysis via the Lyapunov theory shows that the proposed controller can make tracking errors converge to bounded neighborhoods of the origin. Finally, some simulation results are illustrated to verify the effectiveness of the proposed control strategy.     

On trajectory and force tracking control of constrained mobile manipulators with parameter uncertainty

This paper studies the trajectory and force tracking control problem of mobile manipulators subject to holonomic and nonholonomic constraints with unknown inertia parameters. Adaptive controllers are proposed based on a suitable reduced dynamic model, the defined reference signals and the mixed tracking errors. The proposed controllers not only ensure the entire state of the system to asymptotically converge to the desired trajectory but also ensure the constraint force to asymptotically converge to the desired force. A detailed numerical example is presented to illustrate the developed methods.