Research On Intelligent Line-grasping Control For Power Transmission Line De-icing Robot

Ice coating in high voltage power networks imposes heavy load upontransmission lines and could result in trip, disconnection, power-tower collapse andpower interruption, which has posed a serious of damages to economy. Adopting robotdeicing has advantages of avoiding casualties, temporal power failure and power loadtransferring. Furthermore, de-icing robot can be used for line inspection when there isno need for de-icing. For the above reasons, de-icing robot has good prospects.De-icing robot works on the flexible transmission line and need to cross variousobstacles on transmission line. Some external factors, e.g. strong wind, and someinternal factors, e.g. mechanical vibration could make the de-icing robot fail to grasptransmission line. Thus, it is of great difficulty for autonomous line-grasping ofde-icing robot. Conventional controlling methods, like PID control havedisadvantages due to low control precision, over-complexity and low real-timecapability. It is one of the key technologies for de-icing robot to design simple, robust,easy-realizable and real-time line-grasping control methods that satisfying controlaccuracy. This dissertation focuses on autonomous line-grasping control problem. Themain contributions are as follows:1. This dissertation analyses the obstacle crossing problem of de-icing robot andproposes kinematics and dynamics model of a three-link de-icing robot arm accordingto its structural characteristics. This kinematics model and dynamics model are usedin this dissertation and can be used in related research work.2. This dissertation proposes one type of discrete-space line-grasping controlmethods based on traditional reinforcement learning. Considering traditionalreinforcement learning methods can on-line study and is easy to implement, thisdissertation proposes line-grasping control methods based on Q-learning andSARSA-learning. Then by combing eligibity traces the line-grasping control methodsbased on Q(λ)-learning and SARSA(λ)-learning algorithm are proposed. Theproposed methods are evaluated and compared, experiment results show that thesemethods based on traditional reinforcement learning are effective and might adaptharsh environment, because the target point can be approximated in simulation aftersome times iterative computations.3. This dissertation proposes one type of continuous-space line-grasping control method based on reinforcement learning. Traditional reinforcement learning methodshave inevitable problems and learning efficiency is low for large and continuousspace. To overcome this limitation, after an equivalent cissoids model is deductedfrom transmission line model to facilitate the computation, one type of line-graspingcontrol KNN-SARSA(λ) methods of de-icing robot which combine the k-nearestneighbor algorithm and reinforcement learning are proposed. The proposed methodscan produce continuous-state-discrete-action and continuous-state-continuous-action.Simulations results show that these methods can solve the continuous representationproblem of state and action in two-dimensional space, with great generalizationability and learning efficiency.4. This dissertation proposes an adaptive learning control method for trajectorytracking of de-icing robot manipulator in an iterative operation mode. De-icing robotought to repetitively adjust line-grasping actions according to the position errors.Based on these characteristics, the proposed method consists of a classical PDfeedback structure and an additional robust adaptive updated term designed to copewith the non-repeated disturbances and unknown parameters. The controlimplementation is simple for the knowledge is not needed, and the only requirementon the PD and learning gains is the positive definiteness conditions. By usingLyapunov’s method, the asymptotic convergence of the closed-loop control systemcan be achieved. The simulation and experimental results of de-icing robotmanipulator are provided to verify the effectiveness of the proposed control method.5. This dissertation proposes a control method combining the well-knowncomputer torque method which is based on the known nominal robot dynamics, with acompensating controller which is based on the RBF neural network. This schemetakes advantages of the model based control approach and uses the neural networkcontroller to compensate for the robot modeling uncertainties, derives the adaptivelaw of the neural network. The neural network is trained on line which is based onLyapunov theory, thus its convergence is guaranteed. Simulation results are providedto demonstrate performance of the scheme.6. This dissertation proposes a WNN-based robust adaptive control method forde-icing robot. The bounds of the uncertainties are not necessarily known. A WNNsystem is used to approach the unknown controlled system, and a robust controller isdesigned to compensate for approximation error of neural network and externaldisturbances. It is shown that the proposed control scheme can guarantee estimationconvergence by Lyapunov function, reduce computation of regression matrix and the impact on model uncertainty and external disturbances. As demonstrated in theillustrated simulation, the control scheme proposed in this dissertation can achieve abetter model following tracking performance than the existing results.