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1

Two distinct methods for integration of active differential and active roll control systems

Assadian F., Aneke N.

Systems Science

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2003

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Vol. 29, nr 1

97-118

EN

The development and application of mechatronic subsystems have been dramatically increased in the automobile industry during recent years. The implementation of these subsystems, aboard vehicles, results in an increase in the vehicle performance and stability. However, until recently, these subsystems have been developed for specific objectives without considering the dynamic coupling between these systems. Hence, their potentials are not fully exploited. In this paper, the coupling effect of the active differential and the active roll control systems on the lateral and the yaw rate dynamics of a vehicle is demonstrated by deriving a simple linearized model. Then, two distinct control strategies are proposed for illustrating the benefits of integrating the aforementioned systems. The first control method is based on direct use of an optimisation method for computing an optimal mapping of the requested vehicle body forces to the actuator in-puts. The second method utilises a MIMO model based control methodology. The advantages and the drawbacks of each control strategy are discussed and the simulations results are presented.

2

Tracking control of nonlinear pneumatic systems using input/output linearization by state feedback

Wang J., Kotta U., Mangan S., Wei J.

Systems Science

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2003

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Vol. 29, nr 1

151-164

EN

The paper proposed a control design method for servo-controlled pneumatic systems, which is based on the feedback linearization theory. The pneumatic actuator system model is transformed into a linear system description first by regular static state feedback and state coordinate transformation. A servo/tracking controller is then developed for the linear model. Since there exists an inverse transformation for the new co-ordinate system, the designed servo control is finally transformed back to the original state co-ordinates and input. Two different cases are discussed in the paper - the pneumatic cylinder is driven by a single five-port proportional valve and driven by two three-port proportional valves. At the initial stage, for the convenience of finding a suitable set of new coordinates, the static friction forces are ignored. The static friction forces can be treated as uncertainties to the system for robust controller design.

3

Adaptive sliding mode backstepping output tracking controllers for feedforward uncertain systems

Koshkouei A.J., Burnham K.J.

Systems Science

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2003

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Vol. 29, nr 1

79-96

EN

Methods for control design for nonlinear feedforward uncertain systems are considered in this paper. These systems are not usually transformable to the parametric semi-strict feedback form, and it may include unmatched uncertainties consisting of disturbances and unmodelled dynamics. The design methods are based upon (i) the backstepping approach, and (ii) a combination of sliding mode and backstepping. A comparison method of the dynamic and static backstepping methods is presented by applying two methods on a gravity-flow/pipeline system.

4

A novel multi-loop control system for highly nonlinear processes

Osypiuk R.

Systems Science

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2003

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Vol. 29, nr 4

43-54

EN

The first part of the paper deals with design and properties of a general n-loop control structure based on a divided process model. In the second part, two approaches to practical implementation of control of a two-joint serial manipulator are proposed, and simulation based verification of theoretical assumptions is carried out. The proposed multi-loop Model Following Control structure (n-MFC) may find wide application in new intelligent controllers to robust control of parameter-varying process plants.

5

Cartesian space neural network control of robot manipulators

Moise A., Boutalis Y.S., Mertzios B.G.

Systems Science

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2001

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Vol. 27, nr 4

35-46

EN

It is very common that manipulators are subject to structured and/or unstructured uncertainties. Structural uncertainty is characterised by having a correct dynamical model but with parameter uncertainty due to imprecision of the manipulator link properties, unknown loads, inaccuracies in the torque constants of the actuators, and so on. Unstructured uncertainty is characterised by unmodelled dynamics. Neural Network (NN) controllers are usually introduced to generate additional inputs to compensate for disturbances due to model uncertainties. It is clear that the higher the degree of nonlinearity exists in the uncertainties, the greater benefits neural networks (NNs) can contribute. In Cartesian space control, more robust control is required due to the following problems: (i) the inverse dynamic in Cartesian space is more complicated than that in joint space; (ii) the singularity of Jacobian (J) becomes problem at hand when positions in Cartesian space are calculated from joint measurements; - more uncertainties are present. In this paper, we examine some of these issues by studying the Cartesian space robot manipulator control problem. Although NN compensation for model uncertainties has been traditionally carried out by modifying the joint torque/force of the robot, it is also possible to achieve the same objective by using the NN to modify the reference Cartesian trajectory. We present four different NN controller designs to achieve disturbance rejection for an uncertain system.