In this paper, a robust multi-input/multi-output (MIMO) terminal sliding mode control technique is developed for n-link rigid robotic manipulators. It is shown that an MIMO terminal switching plane variable vector is first defined, and the relationship between the terminal switching plane variable vector and system error dynamics is established. By using the MIMO terminal sliding mode technique and a few structural properties of rigid robotic manipulators, a robust controller can then be designed so that the output tracking error can converge to zero in a finite time, and strong robustness with respect to large uncertain dynamics can be guaranteed. It is also shown that the high gain of the terminal sliding mode controllers can be significantly reduced with respect to the one of the linear sliding mode controller where the sampling interval is nonzero. >

https://researchbank.swinburne.edu.au/file/8f91167c-04b4-46b4-984c-85aa2dd6096d/1/PDF (Published version).pdf

A robust MIMO terminal sliding mode control scheme for rigid robotic manipulators

This paper investigates fast finite-time control of nonlinear dynamics using terminal sliding-mode (TSM) scheme. Some new norms of fast TSM strategies are proposed, and a faster convergence rate is established in comparison with the conventional fast TSM. A novel concept of nonsingular fast TSM, which is able to avoid the possible singularity during the control phase, is adopted in the robust high-precision control of uncertain nonlinear systems. Numerical simulation on a two-link rigid robot manipulator demonstrates the effectiveness of the proposed algorithm. Copyright © 2010 John Wiley & Sons, Ltd.

Nonsingular fast terminal sliding-mode control for nonlinear dynamical systems

Terminal sliding mode control for rigid robots

A neural-network-based terminal sliding-mode control (SMC) scheme is proposed for robotic manipulators including actuator dynamics. The proposed terminal SMC (TSMC) alleviates some main drawbacks (such as contradiction between control efforts in the transient and tracking errors in the steady state) in the linear SMC while maintains its robustness to the uncertainties. Moreover, an indirect method is developed to avoid the singularity problem in the initial TSMC. In the proposed control scheme, a radial basis function neural network (NN) is adopted to approximate the nonlinear dynamics of the robotic manipulator. Meanwhile, a robust control term is added to suppress the modeling error and estimate the error of the NN. Finite time convergence and stability of the closed loop system can be guaranteed by Lyapunov theory. Finally, the proposed control scheme is applied to a robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.

Neural-Network-Based Terminal Sliding-Mode Control of Robotic Manipulators Including Actuator Dynamics

A new terminal sliding mode control of MIMO linear systems is proposed in this paper. It is shown that, by inserting a nonlinear term of the system state in the MIMO linear sliding mode, a new terminal sliding mode is developed for MIMO linear systems. A terminal sliding mode controller can then be designed to drive the system state variables to reach and retain in the terminal sliding mode. By suitably designing the parameter matrices of the terminal sliding mode, the system state variables reach the system origin in finite time and the closed loop system is infinite stable in the terminal sliding mode.

Terminal sliding mode control of MIMO linear systems

Many robotic systems would, in the future, be required to operate in environments that are highly unstructured and active, i.e., possessing means of self-actuation. Although a significant volume of results exist in model-based, robust and adaptive control literature, general issues pertinent to the performance of such control systems remain unresolved, e.g., feasibility of implementing high gain switches for control robustness. It is also pointed out that in certain applications, control switching can be very detrimental to the overall system. The primary focus of this paper is development of a new approach to control synthesis for robust robot operations in unstructured environments. To enhance control performance with full model information, we introduce the notion of terminal convergence, and develop control laws based upon a new class of sliding modes, denoted terminal sliders

Control of Nonlinear Systems Using Terminal Sliding Modes

The primary focus of this paper is development of a new approach to control synthesis for robust robot operations in unstructured environments To enhance control performance with full model information, we introduce the notion of terminal convergence, and develop control laws based upon a new class of sliding modes, denoted terminal sliders We demonstrate that terminal sliders provide robustness to parametric uncertainty without having to resort to high frequency control switching, as in the case of conventional sliders [2] In addition, stability analysis that is conducted to demonstrate terminal slider approach results in improved control performance and allows for simple robust design of control parameters Further, improved (guaranteed) precision of terminal sliders is argued for through an analysis of steady state behavior

The problem of identifying uncertain environments for stable contact control is considered. For this purpose, neural networks originally developed using terminal attractor dynamics are utilized. In the sequence, neural networks are used for learning the dynamics of an environment with which a robot establishes contact. In particular, system parameters are identified under the assumption that environment dynamics have a fixed nonlinear structure. A robotics research arm, placed in contact with a single degree-of-freedom electromechanical environment dynamics emulator, is commanded to move through a desired trajectory. The command is implemented using a compliant control strategy, where specified motion is biased with a compliance signal generated based on the error between desired and actual forces. The desired force is computed using neural network identified parameters and desired motion trajectory. >

Parameter learning and compliance control using neural networks

Many robotic systems would, in the future, be required to operate in environments that are highly uncertain and possessing means of self-actuation. In this paper, the problem of identifying uncertain environments for stable contact control is considered. In particular, system parameters are identified under the assumption that environment dynamics have a fixed nonlinear structure. A robotics research arm, placed in contact with a single dof electromechanical environment dynamics emulator, is commanded to move through a desired trajectory. The command is implemented using a compliant control strategy.

S. Gulati

Papers

Control of Nonlinear Systems Using Terminal Sliding Modes

Control of Nonlinear Systems Using Terminal Sliding Modes

Parameter learning and compliance control using neural networks

Compliance Control With Embedded Neural Elements