On The Design of The Robust Neuro-Adaptive Controller for Cable-driven Parallel Robots

Abstract

Original scientific paperIn this study, a robust neuro-adaptive controller for cable-driven parallel robots is proposed. The robust neuroadaptive control system is comprised of a computation controller and a robust controller. The computation controller containing a neural-network-estimator with radial basis function activator is the principal controller and the robust controller is designed to achieve tracking performance. An on-line tuning method is derived to tune the parameters of the neural network for estimating the controlled system dynamic function. To investigate the effectiveness of the robust adaptive control, the design methodology is applied to control a cable-driven parallel robot. Simulation results demonstrate that the proposed robust adaptive control system can achieve favorable tracking performances for the robot.Key words: Robust control, Adaptive law, neural network, Cable-driven Parallel Robot Dizajn robusnog neuro-adaptivnog regulatora za žično pogonjene paralelne robote. U ovome redu predstavljen je neuro-adaptivni regulator za žično pogonjene paralelne robote. Robusni neuro-adaptivni regulator sastoji se od regulatora zasnovanog na estimiranom modelu i robusnog regulatora. Prvi regulator sadrži estimator s neuronskom mrežom s radijalnom aktivacijskom funkcijom glavni je regulator u sustavu, a robusni je regulator dizajniran za slijedenje. Izvedena je on-line metoda podešavanja parametara neuronske mreže za estimaciju dinamike sustava upravljanja. Efikasnost sustava adaptivnog, robusnog regulatora testirana je na na žično pogonjenom paralelnom robotu. Simulacijski rezultati pokazuju da se predloženim robusnim i adaptivnim regulatorom mogu dobiti zadovoljavajuće performanse prilikom slijedenja.Ključne riječi: robusno upravljanje, adaptivni upravljački zakon, neuronska mreža, žično pogonjeni paralelni robot INTRODUCTIONIn the recent decades, robots have been utilized in vast area of industries. However, many parts of manufacturing and engineering does not put upon robots, mainly due to the weakness of conventional robots [1] [2]. For instance, in many applications workspace requirements and load carrying capacity are so much higher than what the conventional robots can provide while cost of the robot should be considered [3] [4]. Toward resolving the latter issue, new class of parallel robots were introduced [5]. Cable-Driven Parallel Robots (CDPR) are structurally similar to parallel actuated robots but with the fundamental difference that cables can only pull the End-Effector (EE) but not push it. Figure 1 represents schematically a CDPR in a general arrangement. It consists of motor, winch system and the EE. From a scientific stand point, feedback control of CDPR is lot more challenging than their counterpart parallel-actuated robots due to the cables behavior. Several efforts had been exerted on modeling and control

Mixing neural networks and the Newton method for the kinematics of simple cable-driven parallel robots with sagging cables

“…Note that as a full CDPR analysis may require millions of IK and DK solving it makes sense to look for a solving approach based on NN as the time overhead of this approach (namely the NN training) will be a small price to pay compared to the time gain for the analysis. A few works have addressed the use of NN for the kinematics of CDPR with simple cable model [11], [12], [13] but the results are very different from the one that will be obtained with real cables. Only recently has been presented a work for a planar CDPR with a realistic cable model [14].…”

Research on workspace visual-based continuous switching sliding mode control for cable-driven parallel robots

“…Moreover, closed-loop control methods can converge the errors caused by uncertainty. Various control methods are used in CDPRs, including PID [17, 18], robust control [19], reinforcement learning [20], neural networks [21], and adaptive control [22]. Sliding mode control (SMC) is an effective method for controlling nonlinear and uncertain systems and has considerable robustness to external disturbances and uncertain dynamics modelling [23].…”

Robust adaptive sliding mode control of a MEMS tunable capacitor based on dead-zone method

“…Nonetheless, application of controllers to the MEMS VRS is integral since it affects the lifelong stability and preciseness of the reference voltage. It is not blowing it out of proportion that applying efficient control algorithms to robots and other mechatronic systems to yield error decrease and improved performance is a remarkable challenge for researchers of control engineering science and technology [9]. Numerous algorithms are also focused on MEMS dynamics and control issues such as [10][11][12].…”