In this paper force control of a piezoelectric actuator is presented. Force control based on a force observer is emphasized; however, results with a force sensor are also presented for comparison purposes. With the help of the proposed force observer, sensorless force control and estimation of environmental forces are realized. The force observer is first compared with the force sensor for different step motions to verify the capability of the force estimation. The observer output is then used for feedback of the force information in the closed-loop system. Experiments are made to validate the method.

The paper deals with a linear belt-driven servomechanism. It proposes new position tracking control algorithm that has been designed by sliding mode control theory. The selected sliding manifold was extended by non-rigid modes of the elastic servodrive. However, the proposed control scheme retains simple and practical for implementation. The experiments presented in the paper show that it effectively suppresses vibrations and furthermore extend the closed-loop bandwidth.

In this paper general approach to motion control systems in the sliding mode framework is discussed in details. It is shown that, due to the fact that a motion control system with n d.o.f may be mathematically formulated in a unique way as a system composed of n second order systems, design of such a system may be formulated in a unique way as a requirement that the generalized coordinates must satisfy certain algebraic constraint. Such a formulation leads naturally to sliding mode framework to be applied in which sliding mode manifolds are selected to coincide with desired constraints on the generalized coordinates. Applications to robotics systems are discussed. As a comparison the experimental investigation of PZF microactuator is presented.

In this paper a sliding mode algorithm for total disturbance estimation and control of piezoelectric stack actuator is proposed. The disturbance observer is based on the lumped parameters model of mechanical motion so it allows the summary action of nonlinear hysteresis disturbance, external forces acting on the system as well as parameters variation to be estimated. Furthermore, using a nonlinear differential equation the internal hysteresis disturbance is removed from the total disturbance in an attempt to estimate the external force acting on the actuator. It is then possible to use this external force estimate as a means of observer based force control of the actuator. Simulation and experiments are compared for validating the disturbance and external force estimation technique. Experiments that incorporate disturbance compensation in a closed-loop SMC control algorithm are also presented to prove the effectiveness of this method in producing high precision motion.

In this paper, the employment of neural networks with sliding mode control in the control of a linear drive with flexible transmission element is described. Linear drives with flexible transmission elements are cheaper and also more efficient than the ones with rigid transmissions like power screw systems. Hence, these devices play an important role in the industry. A neuro-sliding mode controller cascaded with a discrete sliding mode controller is used to control the system. Neuro-sliding mode controller is used in the outer loop and produces reference for the discrete sliding mode controller which serves as a force controller, in the inner loop. The control signal of the neuro-controller is obtained by minimizing an error function which is derived from Lyapunov stability analysis. The controller performance is tested with different loading conditions and different friction torques and the results are presented.

The paper deals with position tracking control for a linear belt-driven servomechanism. It utilizes VSS theory for control design. The selected sliding manifold was extended in order to involve also non-rigid modes of the elastical servodrive. However, the proposed controller is simple and practical for implementation. The experiments presented in the paper show that the proposed control scheme effectively suppresses vibrations and furthermore extends the closed-loop bandwidth.

* Part I: Sliding mode control theory * Chapter 1: Sliding mode control * Chapter 2: Sliding mode regulator design * Chapter 3: Deterministic output noise effects in sliding mode observation * Chapter 4: Stochastic output noise effects in sliding mode observation * Part II: New trends in sliding mode control * Chapter 5: Discrete-time VSS * Chapter 6: Robustness issues of 2-sliding mode control * Chapter 7: Sliding modes, delta-modulation and output feedback control of dynamic systems * Chapter 8: Analysis of sliding modes in the frequency domain * Chapter 9: Output tracking in causal nonminimum-phase systems using sliding modes * Part III: Applications of sliding mode control * Chapter 10: Sliding mode control and chaos * Chapter 11: Sliding modes in fuzzy and neural network systems * Chapter 12: SMC applications in power electronics * Chapter 13: Sliding modes in motion control systems * Chapter 14: Sliding mode control for automobile applications * Chapter 15: The application of sliding mode control algorithms to a diesel generator set * Chapter 16: Motion control of underwater objects by using second order sliding mode techniques * Chapter 17: Semiglobal stabilisation of linear uncertain system via delayed relay control

It is a well known fact that sliding mode control (SMC) is a powerful control method ology for both linear and nonlinear systems because of its robustness to parameter changes, external disturbances and unmodelled dynamics. Besides its power, the design of sliding mode controllers needs the information of the system's state, which makes the design relatively austere in some applications where the mathematical modelling of the system is very hard and where the system has a large range of parameter variations together with unexpected and sudden external disturbances. For those applications, a controller that will provide predicted performance even if the model of the system is not very well known, is needed. That controller should also adapt itself to large parameter variations and to unexpected external disturbances. These types of controllers are generally called 'intelligent' controllers, mainly work ing on the principles of fuzzy logic, neural networks, genetic algorithms and other technologies derived from artificial intelligence. The idea of combining these intelli gent control structures with the power of sliding mode control approach has attracted much research. A recent survey on the combination of SMC and intelligent control can be found in Reference 1. In this chapter, the union of sliding mode with neural networks and fuzzy logic is examined with examples from literature, and then a new technique combining neural networks and sliding mode control is presented.

The sliding mode application in discrete-time systems can result in unwanted oscillations of the controlled variable (so called chattering). To avoid above-mentioned oscillations a new approach in the design of sliding mode control is proposed in this paper. In the proposed approach the calculation of the equivalent control is not necessary while the influence of the system uncertainty and chattering are reduced. The proposed method is applicable to linear as well as nonlinear systems. It allows the design of the control without transformation of the system description to the discrete-time form (z-domain). Upper bound of the sapling time is determined from the switching function changes during the sampling period. The systems with state observers are analyzed. Experimental and simulation results are presented to clarify the design procedure and the features of the proposed algorithm.

In this work, we suggested a new approach for the basic configuration of a mobile robot capable of being a building block of an intelligent agent. This configuration includes obstacle avoidance (OA), goal tracking (DTG) and communication implemented as competence layers. Moreover, a geometry based behavior arbitration layer is proposed for fusing those behaviors. Proposed control is tested on simulations where different scenarios are studied. Results have confirmed the high performance of the method.

In this paper we propose a neural network controller, which has a single neuron with a linear activation function, namely adaline, which uses backpropagation algorithm for online training and works as a sliding mode controller which pushes the system to a certain sliding manifold. We prove that the controller is robust to parameter changes and to the uncertainties in the disturbance and the system is always stable with zero steady state error for bounded disturbance. Different from the works done until now, in this work we do not deal with the estimation of the equivalent control but instead, feeding an appropriate error function to the network and using backpropagation, i.e. gradient descent algorithm, we directly calculate the necessary control input. Initially a controller structure is proposed and in the proceeding sections an improved version is added. Simulation results are provided that verifies the success of the algorithm.

In this paper the concepts, design aspects and application of Sliding Mode Control (SMC) systems to power electronics and motion control systems are discussed. The salient features of the Variable Structure Systems (VSS) with sliding modes are order reduction, decoupling in the design procedure, insensitivity in plant parameter changes and disturbances rejection. Simple implementation makes concepts of SMC very attractive in power electronics and motion control systems. Application to problems like control of DC and AC converters, the control of electrical machines and the design of the observers for electrical machines are discussed.