Improving performance of saturation-based PID controllers for rigid robots

This paper addresses the problem of designing robust tracking control for a large class of uncertain robotic systems. A more general model of the external disturbance is employed in the sense that the external disturbance can be expressed as the sum of a modeled disturbance and an unmodeled disturbance, for example, any periodic disturbance can be expressed in this general form. An adaptive neural network system is constructed to approximate the behavior of unknown robot dynamics. An adaptive control algorithm is designed to estimate the behavior of the modeled disturbance, and in turn the robust H ∞ control algorithm is required to attenuate the effects of the unmodeled disturbance only. Consequently, an intelligent adaptive/robust tracking control scheme is constructed such that an H ∞ tracking control is achieved in the sense that all the states and signals of the closed-loop system are bounded and the effect due to the unmodeled disturbance on the tracking error can be attenuated to any preassigned level. Finally, simulations are provided to demonstrate the effectiveness and performance of the proposed control algorithm.

Section: Description Of Robotic Systemsmentioning

confidence: 99%

Section: Casementioning

confidence: 99%

See 1 more Smart Citation

Robust H_∞ Tracking Control of Uncertain Robotic Systems with Periodic Disturbances

Chang¹

2015

“…Sufficiency: If (3) holds, according to Lemma 1, we have the following matrix inequality (8). Sufficiency: If (3) holds, according to Lemma 1, we have the following matrix inequality (8).…”

Section: Linear Matrix Inequality Formulation Lemma 1 ([6] a Slmentioning

confidence: 99%

“…Proof. Sufficiency: If (3) holds, according to Lemma 1, we have the following matrix inequality (8). Multiplying both sides of the matrix in (8) by diag(P -1 , I, I, P -1 ), matrix inequality (9) follows.…”

Section: Linear Matrix Inequality Formulation Lemma 1 ([6] a Slmentioning

confidence: 99%

Guaranteed Cost Stabilization of Cellular Neural Networks with Time‐Varying Delay

He¹,

Lu²,

Tu³

2013

A Measurement‐Based Approach for Designing Fixed‐Order Controllers for Unknown Closed‐Loop Architecture

Khadraoui

Nounou

et al. 2014

This paper presents a new technique to design fixed‐structure controllers for linear unknown systems using a set of measurements. In model‐based approaches, the measured data are used to identify a model of the plant for which a suitable controller can be designed. Due to the fact that real processes cannot be described perfectly by mathematical models, designing controllers using such models to guarantee some desired closed‐loop performance is a challenging task. Hence, a possible alternative to model‐based methods is to directly utilize the measured data in the design process. We propose an approach to designing structured controllers using a set of closed‐loop frequency‐domain data. The principle of such an approach is based on computing the parameters of a fixed‐order controller for which the closed‐loop frequency response fits a desired frequency response that describes some desired performance indices. This problem is formulated as an error minimization problem, which can be solved to find suitable values of the controller parameters. The main feature of the proposed control methodology is that it can be applied to stable and unstable plants. Additionally, the design process depends on a pre‐selected controller structure, which allows for the selection of low‐order controllers. An application of the proposed method to a DC servomotor system is presented to experimentally validate and demonstrate its efficacy.