| Ice coating in power networks imposes heavy load upon transmission lines and could result in trip, disconnection, power-tower collapse and power interruption, which has posed a serious challenge to many state grids. Adopting robot deicing has advantages avoiding risk of casualties, electricity supply being cut off and transfer load. The deicing robot can be used for line inspection when there is no need for deicing. The prospective will be more expansive in the future.The kernel of deicing robot control is to how to realize the precise control for obstacle-crossing of deicing robot hand. Because deicing robot manipulators is highly nonlinear, highly coupled, and time-varying system, moreover, it works under poor working conditions so it is hard and complex to control. Many conventional control techniques of the robotic manipulators include the computed torque control, the adaptive control, the sliding-mode control, etc. Where the adaptive control has a fixed structure and adaptable parameters and is very effective in coping with structured uncertainties and maintaining a uniformly good performance over a limited range, but it does not solve the problem of unstructured uncertainties. The sliding-model control is a robust nonlinear control scheme that is effective in overcoming the uncertainties and has a fast transient response. However the control effort is discontinuous and creates chattering which may excite the high frequency dynamics, etc.From the last two decades to today, the development of intelligent control for robotic manipulators has received considerable interest. The most popular intelligent control approaches are the neural network control, the fuzzy control, etc which are used to approach a nonlinear function to arbitrary accuracy. This feature is used in the controller to model the complex processes and compensate for the unstructured uncertainties. However, the intelligent control techniques for robotic manipulator remain a very challenging area of research. With the ability and knowledge, through this dissertation the author will contribute the journal and readers the contents of the following:1. For the most part of the robot manipulator control in the published literature actuator dynamic are typically excluded from the robot dynamic behaviors to simplify the control system. But, actuator dynamic perform an important part of the complete robotic dynamics, especially in the factors of high-velocity moment, highly varying loads, friction, and actuator saturation. Thus, there exist some interactions between robot and actuator dynamics that cannot be neglected. So, to deal with this problem, the proposed control system is developed for De-icing robot manipulator including based on neural network controller which is the first contribution of this dissertation.2. The second contribution is to design an intelligent control system scheme for the position control of an n-link robot manipulator by using neural-fuzzy-network controller to compensated uncertainness dynamic model and external disturbance via capability self-learning of neural network and human intuitive.3. Recently, iterative learning control is a relatively but well-established area of study in control theory which can be categorized as an intelligent control methodology, is an approach for improving the transient performance of system that operates repetitively over a fixed time interval. A new method based on a combination of the advantages of several control methods into a hybrid one is proposed for De-icing robot manipulator for repetitive task to achieve favorable tracking performance. The architecture of this hybrid control method is defined as follows:(1) the control is a learning process through several iterations of off-line operations of a manipulator,(2) the control structure consists of two parts:a PD feedback part and a feed-forward learning part using the torque profile obtained from the previous iteration, and (3) the gains in the PD feedback law are adapted according to the gain switching strategy with respect to the iteration.4. The fourth contribution, we propose a novel self-structured organizing single-input cerebellar model articulation controller (S-CMAC) for three-link De-icing robot manipulator to achieve the high-precision position tracking. This control system combines advantages of S-CMAC and it does not require prior knowledge of a certain amount of memory space, and the self-organizing approach demonstrates the properties of generating and pruning the input layers automatically. The developed self-organizing rule of S-CMAC is clearly and easily used for real-time systems. Moreover, the developed system is solely used to control the plant and no conventional or compensated controller. The online tuning laws of CMAC parameters are derived in gradient-descent method.5. The fifth contribution, we propose a novel robust adaptive wavelet fuzzy CMAC (WFCMAC) control system for three-link De-icing robot manipulator to achieve the high-precision position tracking. This control system combines advantages of fuzzy inference system with CMAC and wavelet decomposition capability and the adaptive single input fuzzy compensator which is designed to deal with the approximation errors between the estimating WFCMAC and the ideal controller to the stability of system is guarantied.6. Finally sixth contribution, by combining the S-CMAC control scheme, capability of the wavelet decomposition property, by including a delayed self-recurrent unit in the association memory space. We propose a novel single-input recurrent wavelet CMAC (S-RWCMAC) based supervisory control system which is presents a dynamic WCMAC with single-input for three-link De-icing robot manipulator to achieve the high-precision trajectory tracking. This control system consists of an adaptive S-RWCMAC, a supervisory controller and an adaptive robust controller. The S-RWCMAC is the main controller and the adaptive robust controller is used to dispel the effect of the approximation error. The online tuning laws of S-RWCMAC parameters are derived in gradient-descent learning method which can be caused the instability controlled system, especially in the transient period. So, the supervisory controller is appended to the adaptive S-RWCMAC to force the system states within a predefined constraint set, if the adaptive S-RWCMAC can not maintain the system states within the constraint set. Then, the supervisory controller will work to pull the states back to the constraint set and otherwise is not.7. The finally contribution proposes novel architecture and mathematical model of De-icing robot which can be effectiveness application in practical.The proposed control systems are applied through the two-link and the novel De-icing robot manipulator. Finally, the simulation and experimental results show that the tracking characteristics of the proposed control systems archive high-precision tracking position. |
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