Sliding-mode control (SMC) has been studied extensively for over 50 years and widely used in practical applications due to its simplicity and robustness against parameter variations and disturbances. Despite the extensive research activities carried out, the key technical problems associated with SMC remain as challenging research questions due to demands for new industrial applications and technological advances. In this respect, soft computing (SC) is a rather recent development in intelligent systems which has provided alternative means for adaptive learning and control to overcome the key SMC technical problems. Substantial efforts in integration of SMC with SC have been placed in recent years with various successes. In this paper, we provide the state of the art of recent developments in SMC systems with SC, examining key technical research issues and future perspectives.

Sliding-Mode Control With Soft Computing: A Survey

This paper presents a comprehensive overview of the application of Variable Structure Systems (VSSs) with Sliding Mode (SM) methods in motion control systems. Our aim is to give implementable sliding mode design solutions for complex motion systems, actuators and supply converters. This paper provides a frame for further study of sliding mode applications in motion control systems.

Variable Structure Systems With Sliding Modes in Motion Control—A Survey

This paper surveys how some "intelligence" can be incorporated in sliding-mode controllers (SMCs) by the use of computational intelligence methodologies in order to alleviate the well-known problems met in practical implementations of SMCs. The use of variable-structure system theory in design and stability analysis of fuzzy controllers is also discussed by drawing parallels between fuzzy control and SMCs. An overview of the research and applications reported in the literature in this respect is presented.

The fusion of computationally intelligent methodologies and sliding-mode control-a survey

This paper presents an adaptive robust controller for robot manipulators using adaptive integral sliding mode control and time-delay estimation (TDE). The proposed controller employs TDE to estimate robot dynamics with uncertainties such as parameter variations and disturbances, an integral sliding surface to eliminate the reaching phase together with the noise-sensitive switching action used in the conventional sliding mode control, and adaptation gain dynamics to achieve the applicable high accuracy. Experimental studies using a programmable universal machine for assembly-type industrial robot manipulator are conducted to verify the effectiveness of the proposed control. The proposed controller is robust, chattering free, and highly accurate.

Adaptive Integral Sliding Mode Control With Time-Delay Estimation for Robot Manipulators

Neuro sliding mode control of robotic manipulators

In this paper, a synergistic combination of neural networks with sliding mode control (SMC) is proposed. As a result, the chattering is eliminated and error performance of SMC is improved. In such an approach, two parallel NNs are proposed to realize SMC. The equivalent control and the corrective term of SMC are the outputs of the NNs. The gradient descent method is used for the weight adaptation. This novel approach is applied to control of a SCARA type robot manipulator and simulation results are given.

Neuro-sliding mode control of robotic manipulators

In this paper, a novel scheme is proposed to adapt the gains of a sliding mode controller (SMC) so that the problems faced in its practical implementations as a motion controller are overcome. A Lyapunov function is selected for the design of the SMC and an MIT rule is used for gain adaptation. The criterion that is minimized for gain adaptation is selected as the sum of the squares of the control signal and the sliding surface function. This novel approach is tested on a scara-type robot manipulator. The experimental results presented prove its efficacy.

Gain adaptation in sliding mode control of robotic manipulators

Neuro-sliding mode control of robot manipulators

In the application of Sliding Mode Controllers, the main problem which is encountered is that a whole knowledge of the system dynamics (or inverse dynamics) and the system parameters is required to be able to compute the equivalent control. This is actually vely rare in practice. In this paper, a feed-forward neural network is proposed to compute the equivalent control. The weights of the net are updated such that the additional control term of the sliding mode goes to zero. Experimental studies carried out on a direct drive arm indicate that the proposed approach is a good candidate for trajectory control applications.

http://ieeexplore.ieee.org/iel4/5562/14961/00680617.pdf

Meliksah Ertugrul

Papers

Neuro sliding mode control of robotic manipulators

Neuro-sliding mode control of robotic manipulators

Gain adaptation in sliding mode control of robotic manipulators

Neuro-sliding mode control of robot manipulators

utation of the Equivalent ontrol in Sliding Made For Ro Trajectory Control Applications