Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel approach, sliding mode control with perturbation estimation (SMCPE), is presented to remove the restrictions of SMC about the a priori upperbound knowledge for the perturbations and to improve the tracking performance. It does not require the a priori knowledge of the upper bounds of the uncertainties and it offers much better tracking accuracies. Hover, SMCPE requires that the control loop speeds be reasonably faster than the perturbation dynamics. The success of SMCPE applications is closely related to the precision of the state measurements. Experimental justifications are presented from the applications of SMCPE on a SCARA-type Hitachi robot. >
['Hakan Elmali', 'Nejat Olgac']
I am writing a research paper on a idea Theory and implementation of sliding mode control with perturbation estimation (SMCPE)
[{"from": "human", "value": "I am writing a research paper on a idea Theory and implementation of sliding mode control with perturbation estimation (SMCPE)"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel approach, sliding mode control with perturbation estimation (SMCPE), is presented to remove the restrictions of SMC about the a priori upperbound knowledge for the perturbations and to improve the tracking performance. It does not require the a priori knowledge of the upper bounds of the uncertainties and it offers much better tracking accuracies. Hover, SMCPE requires that the control loop speeds be reasonably faster than the perturbation dynamics. The success of SMCPE applications is closely related to the precision of the state measurements. Experimental justifications are presented from the applications of SMCPE on a SCARA-type Hitachi robot. >"}]