Galip Cansever

LPV gain-scheduling controller design for a non-linear quarter-vehicle active suspension system

There always exists a conflict between ride comfort and suspension deflection performances during the vibration control of suspension systems. Active suspension control systems, which are designed by linear methods, can only serve as a trade-off between these conflicting performance criteria. Both performance objectives can only be accomplished at the same time by using a nonlinear controller. This paper addresses the non-linear induced L2 control of an active suspension system, which contains non-linear spring and damper elements. The design method is based on the linear parameter varying (LPV) model of the system. The proposed method utilizes the bilinear damping characteristic, stiffening spring characteristic when the suspension deflection approaches the structural limits, mass variations and parameter-dependent weighting filters. Simulation studies both in time and frequency domain demonstrate that the active suspension system controlled by the proposed method always guarantees an agreement between acceleration (comfort) and suspension deflection magnitudes together with a high ride performance.

ROBOT TRAJECTORY TRACKING WITH ADAPTIVE RBFNN-BASED FUZZY SLIDING MODE CONTROL

Dėl skaiciavimų apsunkinimo ir dinamisko neapibrėžtumo klasikinius modeliu pagrįstus valdymo principus sunku taikyti robotų sistemoms. Siame straipsnyje pristatomas nuo modelio nepriklausomas neapibrėžto slankiojo režimo valdymas, pagrįstas neuroniniais tinklais. Kai naudojami klasikiniai slankiojo režimo kontroleriai, norint apskaiciuoti ekvivalentiską valdymą, reikia sistemos dinamikos ir sistemos parametrų. Kai robotas valdomasRBFNN pagrįstu neapibrėžtu slankiuoju režimu, RBFNN kuria-mas taip, kad imituotų ekvivalentiskus valdymo slankiuoju režimu veiksmus (SMC). Naudojant adaptyvų algoritmą RBFNN svoriai pakeiciami taip, kad sistemos būsena atitiktų slankųjį pavirsių ir slystų kartu su juo. Pradiniai RBFNN svoriai prilyginami nuliui, o vėliau koreguojami eigos metu, nereikia jokių prižiūrimų mokymosi procedūrų. Pristatomas metodas įdiegtas pramoni-niame robote (Manutec-r15) ir palygintas su PID kontroleriu. Atlikti eksperimentiniai tyrimai parodė, kad sis metodas puikiai tinka pramoninių robotų trajektorijos sekimo taikomosioms programoms vykdyti. http://dx.doi.org/10.5755/j01.itc.40.2.430

Fuzzy sliding mode controller with neural network for robot manipulators

This paper presents an approach of cooperative control that is based on the concept of combining neural networks and the methodology of fuzzy sliding mode control (SMC). The aim of this study is to overcome some of the difficulties of conventional control methods such as controllers requires system dynamics in detailed. In the proposed control system, a neural network (NN) is developed to mimic the equivalent control law in the SMC. The structure of the NN that estimates the equivalent control is a standard two layer feed-forward NN with the backprobagation algorithm. The weights of the NN are updated such that the corrective control term of the SMC goes to zero.

Three link robot control with fuzzy sliding mode controller based on RBF neural network

The purpose of this paper is to propose adaptive fuzzy sliding mode control (SMC) based on radial basis function neural network (RBFNN) for trajectory tracking problem of three link robot manipulator. A RBFNN is used to compute the equivalent control of sliding mode control. A Lyapunov function is selected for the design of the SMC and an adaptive algorithm is used for weight adaptation of the RBFNN. Simulation results of three link Scara robot manipulator verify the validity of the proposed controller in the presence of uncertainties

Stabilisation of a class of 2-DOF underactuated mechanical systems via direct Lyapunov approach

This paper represents an alternative stabilisation procedure for a class of two degree-of-freedom underactuated mechanical systems based on a set of transformations and a Lyapunov function. After simplifying dynamic equations of the system via partial feedback linearisation and coordinate changes, the stability of the system is provided with Lyapunov’s direct method. Proposed control scheme is used on two different examples and asymptotic convergence for each system is proven by means of La Salle’s invariance principle. The designed controller is successfully illustrated through numerical simulations for each example.

A stabilizing controller for PVTOL aircraft

A stabilizing controller structure is proposed for planar vertical take off and landing (PVTOL) aircraft. In order to achieve almost global asymptotic stabilization, the coupling coefficient, ε, is assumed to be in the interval 0 ≤ ε <; 1. Stability analysis is performed based on a Lyapunov function and asymptotic convergence is proven by means of La Salles invariance principle. The proposed controller is implemented by numerical simulations on PVTOL aircraft and results are presented.

Exact Model Knowledge and Direct Adaptive Controllers on Ball and Beam

This paper presents analysis and implementation of Exact Model Knowledge (EMK) and Direct Adaptive control schemes on the 4th order ball and beam system in which the dynamics of the ball position and the dynamics of the beam angle are cascaded. For the controller analysis the error dynamic equations for ball position and the beam angle are derived for both cases. Following, experimental studies are conducted based on the proposed control approaches and it is presented that constant and sinusoidal references for the ball position are tracked asymptotically.

Neural network based control of AC-AC converter for voltage sags, harmonics and EMI reduction

A Neural Network based AC-AC voltage restorer is proposed for voltage sags and PWM type active power filter is used for voltage harmonics compensation and EMI reduction. The objective is to apply the neural network switching control technique to the AC-AC voltage restorer to reduce time delays during the switching conditions and switching losses. The aim of the IGBTs used in the AC-AC voltage restorer is to test and to find the best switching frequency-power combination in the steps of the simulation. Thus, the proposed AC-AC voltage restorer has some advantages such as fast switching response, simplicity and more intelligent structure, better output waveform. Neural Network techniques have proven that they were suitable for parameter identification and control of nonlinear systems. The transient condition of the AC-AC voltage restorer is improved via the Neural Network based control technique. On the other side, the proposed strategy for elimination of voltage harmonics using PWM type DC-AC inverter part of the system as an active filter. The last objective of the system is ElectroMagnetic Interference (EMI) reduction with using this filter and voltage restorer.

A robust PID like state-feedback control via LMI approach: An application on a Double Inverted Pendulum System

This paper addresses the design method for robust PID like controllers which guarantee the quadratic stability, performance in terms of H2 and Hinfin specifications, pole locations and maximum output control. The approach is based on the transformation of the PID controller design problem to that of state feedback controller design thereby the convex optimization approaches can be adapted. Real time experimental results on a double inverted pendulum system demonstrates the validity and applicability of the proposed approach.

Adaptive Neural Network Based Fuzzy Sliding Mode Control of Robot Manipulator

A fuzzy sliding mode controller based on radial basis function neural network (RBFNN) is proposed in this paper. In the applications of sliding mode controllers the main problem is that a whole knowledge of the system dynamics and system parameters are required to be able to compute equivalent control. In this paper, an RBFNN is used to compute the equivalent control. The weights of the RBFNN are changed according to adaptive algorithm for the system state to hit the sliding surface and slide along it. The initial weights of the RBFNN set to zero, and then tune online, no supervised learning procedures are needed. Computer simulations of three link robot manipulator for trajectory tracking verify the validity of the proposed adaptive neural network based fuzzy sliding mode controller in the presence of uncertainties