Abstract
Cable robots are structurally the same as parallel robots but with the basic difference that cables can only pull the platform and cannot push it. This feature makes control of cable robots a lot more challenging compared to parallel robots. This paper introduces a controller for cable robots under force constraint. The controller is based on input-output linearization and linear model predictive control. Performance of input-output linearizing (IOL) controllers suffers due to constraints on input and output variables. This problem is successfully tackled by augmenting IOL controllers with linear model predictive controller (LMPC). The effecttiveness of the proposed method is illustrated by numerical simulation.
Highlights
After a motion simulator with parallel kinematic chains was introduced in 1965 by D
Cable robots are structurally the same as parallel robots but with the basic difference that cables can only pull the platform and cannot push it. This feature makes control of cable robots a lot more challenging compared to parallel robots
This paper introduces a controller for cable robots under force constraint
Summary
After a motion simulator with parallel kinematic chains was introduced in 1965 by D. Cable robots are exceptionally well suited for many applications such as handling of heavy materials, inspection and repair in shipyards and airplane hangars, high-speed manipulation, rapidly deployable rescue robots, cleanup of disaster areas, and access to remote locations and interaction with hazardous environments [6,7,8,9,10,11,12] For these applications conventional serial or parallel robots are impractical due to their limited workspace. Linear model predictive control refers to a class of control algorithms that compute a manipulated variable profile by utilizing a linear process model to optimize a linear or quadratic open-loop performance objective subject to linear constraints over a future time horizon. A major disadvantage of cable robots is that each cable can only exert tension This constraint leads to performance deterioration and even instability, if not properly accounted for in the control design procedure. A computer control algorithm that utilizes an explicit model to predict the future response of a system is an effective tool for handling constrained control problems
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