scholarly journals Energy optimal control of mobile manipulators subject to compensation of external disturbance forces

2022 ◽  
Vol 167 ◽  
pp. 104550
Author(s):  
Mirosław Galicki
Keyword(s):  
Optimal Control ◽  
External Disturbance ◽  
Mobile Manipulators

Maximum load carrying capacity of mobile manipulators: optimal control approach

Robotica ◽  
2009 ◽  
Vol 27 (1) ◽  
pp. 147-159 ◽  
Author(s):  
M. H. Korayem ◽  
A. Nikoobin ◽  
V. Azimirad
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Carrying Capacity ◽  
Nonholonomic Constraints ◽  
Maximum Load ◽  
Mobile Manipulators ◽  
Load Carrying Capacity ◽  
Maximum Payload ◽  
Load Carrying

SUMMARYIn this paper, finding the maximum load carrying capacity of mobile manipulators for a given two-end-point task is formulated as an optimal control problem. The solution methods of this problem are broadly classified as indirect and direct. This work is based on the indirect solution which solves the optimization problem explicitly. In fixed-base manipulators, the maximum allowable load is limited mainly by their joint actuator capacity constraints. But when the manipulators are mounted on the mobile bases, the redundancy resolution and nonholonomic constraints are added to the problem. The concept of holonomic and nonholonomic constraints is described, and the extended Jacobian matrix and additional kinematic constraints are used to solve the extra DOFs of the system. Using the Pontryagin's minimum principle, optimality conditions for carrying the maximum payload in point-to-point motion are obtained which leads to the bang-bang control. There are some difficulties in satisfying the obtained optimality conditions, so an approach is presented to improve the formulation which leads to the two-point boundary value problem (TPBVP) solvable with available commands in different softwares. Then, an algorithm is developed to find the maximum payload and corresponding optimal path on the basis of the solution of TPBVP. One advantage of the proposed method is obtaining the maximum payload trajectory for every considered objective function. It means that other objectives can be achieved in addition to maximize the payload. For the sake of comparison with previous results in the literature, simulation tests are performed for a two-link wheeled mobile manipulator. The reasonable agreement is observed between the results, and the superiority of the method is illustrated. Then, simulations are performed for a PUMA arm mounted on a linear tracked base and the results are discussed. Finally, the effect of final time on the maximum payload is investigated, and it is shown that the approach presented is also able to solve the time-optimal control problem successfully.

Adaptive Integrated Control for Omnidirectional Mobile Manipulators Based on Neural-Network

2011 ◽  
pp. 310-325
Author(s):  
Xiang-min Tan ◽  
Dongbin Zhao ◽  
Jianqiang Yi ◽  
Dong Xu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Integrated Control ◽  
External Disturbance ◽  
Torque Control ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Modeling And Control ◽  
Control Approach ◽  
Proposed Model

An omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. However, modeling and control of such systems are very challenging because of their complicated mechanism. In this paper, an unified dynamic model is developed by Lagrange Formalism. In terms of the proposed model, an adaptive integrated tracking controller, based on the computed torque control (CTC) method and the radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.

Adaptive Integrated Control for Omnidirectional Mobile Manipulators Based on Neural-Network

2009 ◽  
Vol 3 (4) ◽  
pp. 34-53 ◽  
Author(s):  
Xiang-min Tan ◽  
Dongbin Zhao ◽  
Jianqiang Yi ◽  
Dong Xu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Integrated Control ◽  
External Disturbance ◽  
Torque Control ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Modeling And Control ◽  
Control Approach ◽  
Proposed Model

An omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. However, modeling and control of such systems are very challenging because of their complicated mechanism. In this article, an unified dynamic model is developed by Lagrange Formalism. In terms of the proposed model, an adaptive integrated tracking controller, based on the computed torque control (CTC) method and the radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.

Gauge-Looper integrated optimal control for hot strip finishing mills based on sliding mode control

2021 ◽  
Vol 118 (4) ◽  
pp. 413
Author(s):  
Yin Fang-Chen ◽  
Shi Hong-Wei
Keyword(s):  
Optimal Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Control Strategies ◽  
External Disturbance ◽  
State Space Model ◽  
Integrated System ◽  
Market Demand ◽  
Mode Control ◽  
Hot Strip

Gauge-Looper and tension are controlled independently in the conventional control strategies, which are not able to deal with the external disturbance. Many researchers have proposed and applied a variety of control schemes for this problem, but the increasingly strict market demand for strip quality requires further improvements. This work describes a sliding mode control (SMC) strategy that realizes the optimal control of a automatic Gauge control and Looper control integrated system. Firstly, a state-space model of Gauge-Looper integrated system was established based on a 1700 mm tandem hot mill. Then, In order to achieve the desired dynamical performance of Gauge-Looper integrated system, the sliding mode surface and control law of SMC controller was designed. Simulation experiments for a traditional PID controller and the proposed SMC controller were conducted using MATLAB/Simulink software. The simulation results show that when the system is disturbed, the traditional controller produces a Gauge fluctuation of 0.136 mm and tension fluctuation of 1.177 MPa, which is unacceptable in hot strip mills. The SMC controller restricts the Gauge fluctuation to less than 0.047 mm, and the tension fluctuation is less than 0.382 MPa in all cases.

Fuzzy Logic Based Intelligent Control and Optimal Control of a Rotary Inverted Pendulum System

2002 ◽  
Author(s):  
Devendra P. Garg ◽  
Piyush Jain
Keyword(s):  
Optimal Control ◽  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Inverted Pendulum ◽  
External Disturbance ◽  
Control Input ◽  
Linear Quadratic ◽  
Inference System ◽  
Pendulum System ◽  
Inverted Pendulum System

In this paper a fuzzy logic based control strategy has been applied to balance an inverted pendulum. An inverted pendulum in itself is a highly unstable system since there is always some external disturbance present that makes it to fall. Therefore, to maintain it in an upright position, a control input is needed, that could be in the form of torque, moment or force. Here an inverted pendulum with a rotary arm is chosen for study. The arm is driven in the direction of fall of the pendulum. This action attempts to stabilize the pendulum in the upright configuration. The fuzzy control action controls the torque applied to the actuator and thus controls the pendulum. A fuzzy inference system is constructed using Matlab. This is used to compute the output torque for a given set of angle and velocity. The same system is subjected to a Quadratic Optimal Control scheme, and the simulation results obtained from the fuzzy control system and Linear Quadratic Regulator control (LQR) scheme are compared.

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