Model-Independent Control of a Flexible-Joint Robot Manipulator

Abstract

Flexibility at the joint of a manipulator is an intrinsic property. Even “rigid-joint” robots, in fact, possess a certain amount of flexibility. Previous experiments confirmed that joint flexibility should be explicitly included in the model when designing a high-performance controller for a manipulator because the flexibility, if not dealt with, can excite system natural frequencies and cause severe damage. However, control design for a flexible-joint robot manipulator is still an open problem. Besides being described by a complicated system model for which the passivity property does not hold, the manipulator is also underactuated, that is, the control input does not drive the link directly, but through the flexible dynamics. Our work offers another possible solution to this open problem. We use three-layer neural networks to represent the system model. Their weights are adapted in real time and from scratch, which means we do not need the mathematical model of the robot in our control algorithm. All uncertainties are handled by variable-structure control. Backstepping structure allows input efforts to be applied to each subsystem where they are needed. Control laws to adjust all adjustable parameters are devised using Lyapunov’s second method to ensure that error trajectories are globally uniformly ultimately bounded. We present two state-feedback schemes: first, when neural networks are used to represent the unknown plant, and second, when neural networks are used to represent the unknown parts of the control laws. In the former case, we also design an observer to enable us to design a control law using only output signals—the link positions. We use simulations to compare our algorithms with some other well-known techniques. We use experiments to demonstrate the practicality of our algorithms.

Trajectory and Vibration Control of a Single-Link Flexible-Joint Manipulator Using a Distributed Higher-Order Differential Feedback Controller

“…hence, the following two parallel systems follow from Eqs. (12) and 13: Then, two separate HODFC systems could be designed for the system, with separate measurements for h(t) and a(t).…”

“…Over the last two decades, the control of FJMs has also benefited from strategies that seek to reduce the impact of modeling difficulties on controller performance, instead making use such nonmodel intensive strategies as genetic algorithms, particle swarm optimization methods, fuzzy logic, neural networks [11][12][13][14][15][16][17], and more recently, the so-called iPI and iPID strategies [18][19][20]. These pseudomodel based control strategies have enabled a compromise between the cost of real-time controller implementation and the need for highly accurate models in model-based control.…”

Intelligent Backstepping System to Increase Input Shaping Performance in Suppressing Residual Vibration of a Flexible-Joint Robot Manipulator

“…The reader is referred to Chatlatanagulchai in [62] and Chatlatanagulchai and Meckl in [63] for selection of design parameters, detailed derivations, and stability proofs.…”

Flexible Multibody System Linear Modeling for Control Using Component Modes Synthesis and Double-Port Approach

“…Each substructure is separately modeled and, in this way, the large flexible structure is considered to be an FMS. The dynamics of FMS are significantly important in the design, optimization, and control of many systems, such as space vehicles [3,4] or robot manipulators [5,6]. Control modeling techniques must deal with FMS substructures, including the necessary control inputs/outputs in order to recover the dynamic behavior of the whole system for different boundary conditions.…”