Neural network-based control of flexible-link manipulators

The problem of modeling and control of flexible-link manipulators has received much attention in the past several years. There are a number of potential advantages arising from the use of light-weight flexible-link manipulators, such as faster operation, lower energy consumption, and higher load-carrying capacity. However, structural flexibility causes many difficulties in modeling the manipulator dynamics and guaranteeing stable and efficient motion of the manipulator end-effector. Control difficulties are mainly due to the non-colocated nature of the sensor and actuator position, which results in unstable zero dynamics. Further complications arise because of the highly nonlinear nature of the system and the difficulty involved in accurately modeling various friction and backlash terms. Control strategies that ignore these problems generally fail to provide satisfactory closed-loop performance. This dissertation presents experimental evaluation on the performance of neural network-based controllers for tip position tracking of flexible-link manipulators. The controllers are designed by utilizing the output redefinition approach to overcome the problem caused by the non-minimum phase characteristic of the flexible-link system. Four different neural network schemes are proposed. The first two schemes are developed by using a modified version of the "feedback-error-learning" approach to learn the inverse dynamics of the flexible manipulator. The neural networks are trained and employed as online controllers. Both schemes require only a linear model of the system for defining the new outputs and for designing conventional PD-type controllers. This assumption is relaxed in the third and fourth schemes. In the third scheme, the controller is designed based on tracking the hub position while controlling the elastic deflection at the tip. In the fourth scheme which employs two neural networks, the first network (referred to as the output neural network) is responsible for specifying an appropriate output for ensuring minimum phase behavior of the system. The second neural network is responsible for implementing an inverse dynamics controller. Both networks are trained online. Finally, the four proposed neural network controllers are implemented oil a single flexible-link experimental test-bed. Experimental and simulation results are presented to illustrate the advantages and improved performance of the proposed tip position tracking controllers over the conventional PD-type controllers in the presence of unmodeled dynamics such as hub friction and stiction and payload variations

Optimal characteristics determination of engine mounting system using TRA mode decoupling with emphasis on frequency responses

It is possible to improve vehicle vibration by tuning the parameters of engine mounting system. By optimization of mount characteristics or finding the optimal position of mounts, vibration of the engine and transmitted force from the engine to the chassis can be reduced. This paper examines the optimization of 6-degree-of-freedom engine mounting system based on torque roll axis (TRA) mode decoupling, so that TRA direction coincides with one of the natural modes of vibration. This is achieved by determination of optimal location and stiffness of mounts. In order to find feasible results, physical constraints are taken into account in optimization process. A detailed procedure of optimization problem is explained. Finally, by comparing the frequency and time responses of the optimal design with the original configuration, it is concluded that TRA decoupling is a proper objective function in engine mounting optimization and can greatly improve the vibration behavior of the engine. Achieving decoupled system, the optimal configuration has a better chance of placing dominant natural frequency below the operation range. Also, the forces transmitted through the mounts are reduced noticeably in the optimal design

Cluster consensus of general fractional-order nonlinear multi agent systems via adaptive sliding mode controller

In this paper cluster consensus is investigated for general fractional-order multi agent systems with nonlinear dynamics via adaptive sliding mode controller. First, cluster consensus for fractional-order nonlinear multi agent systems with general form is investigated. Then, cluster consensus for the fractional-order nonlinear multi agent systems with first-order and general form dynamics is investigated by using adaptive sliding mode controller. Sufficient conditions for achieving cluster consensus for general fractional-order nonlinear multi agent systems are proved based on algebraic graph theory, Lyapunov stability theorem and Mittag-Leffler function. Finally, simulation examples are presented for first-order and general form multi agent systems, i.e. a single-link flexible joint manipulator which demonstrates the efficiency of the proposed adaptive controller

Model-based force/position control of cooperative manipulation systems

This paper presents a centralized force/position controller for a heavy object manipulation in a multi-manipulator cooperative system. System dynamics of cooperative manipulating tasks comes from complex interaction of the object with robot manipulators and the environment. In this paper, focussing on the interaction effects in the system as well as by noting the role of imposed kinematics and force constraints, manipulator coordination and minimizing internal forces a pre-designed impedance behaviour between manipulator end effectors and the object is developed. The stability of the feedback system is then presented through passivity theorem and simulation results are finally provided with three manipulators handling the object supporting the relevance of the theoretical results

Decentralized force and motion control of multiple cooperative manipulators

Decentralized force and motion control of a heavy object with lathing tools, in a cooperative multiple manipulator system is studied in this paper. Interaction of the object with robot manipulators and the environment and interaction of manipulators with each other that produce internal forces in the object are complexities in the system dynamics of cooperative manipulation tasks. In this paper, impedance behaviour between manipulator end-effectors (EE) and the object is developed to achieve springy links that lead to impedance effect between the object and the environment. Then, by focusing on the interaction effects in the system as well as the role of imposed kinematics and force constraints, each manipulator will have its own controller for coordination tasks, environment force regulation and internal forces minimization, without force/torque sensors in the EE. The relevance of the theoretical findings is illustrated using simulation results involving three robots manipulating a common object

A novel simultaneous fault detection and control approach based on dynamic observer

The problem of simultaneous fault detection and control (SFDC) for linear continuous-time systems is addressed in this paper. A mixed H 2/H ∞ formulation of the SFDC problem using dynamic observer is presented. In essence, a single unit called detector/controller is designed where the detector is a dynamic observer and the controller is a state feedback controller based on the dynamic observer. Hence, the detector/controller unit produces two signals, i.e., the detection and control signals. It is shown that the dynamic observer can be used effectively to tackle the drawbacks of the existing methods of SFDC design. Indeed, the idea presented in this paper is based on applying the advantages of dynamic observers, which leads to some sufficient conditions for solvability of the SFDC problem in terms of LMI feasibility conditions. Simulation results illustrate the effectiveness of the proposed design technique. © ICIC International 2012