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Sum-of-squares (SOS) approach is an effective technique for analysis and design of nonlinear systems. Due to the advantage, this study presents an SOS observer-based control approach for sensorless position servo of permanent magnet linear synchronous motor. To apply the SOS approach, a nonlinear polynomial model is firstly established to describe motor dynamics. Based on this model, a full-order Luenberger observer is developed to estimate the system state. The observer gain that ensures the convergence of state estimation is deduced via the stability analysis and solved by using the third-party toolbox SOSTOOLS. Compared with earlier studies, the advantage of the proposed observer is that the exact motor dynamics is used. Therefore, no further hypothesis or adaptive mechanism on the mover speed is required, which is usually utilized to solve the problem of speed variation with the state observation. According to the principle of backstepping control, the controller is systematically designed to guarantee the asymptotic stability of the closed-loop system. Since both the mechanical and electrical dynamics have been considered, the control signal is directly presented as a voltage. This is different from the conventional current control strategy, which applies the proportional–integral controller to produce a voltage command. The experiment results are provided to validate the proposed control scheme. Moreover, a recent work that is based on the sensorless linear quadratic regulator optimal control is adopted as a comparison study.

This study presents an analytic designing approach for the sensorless direct thrust force control (DTFC) of permanent magnet linear synchronous motor (PMLSM). A modified stator flux estimator developed by use of an adaptive mechanism and correction factors is devoted to measuring the stator flux linkage and the mover's position. The parameters of the flux estimator are adjusted in advance through an off-line identification process to increase the accuracy of estimation. Based on the principle of backstepping control, the reference q -axis flux command, which is equivalent to the thrust force command, is first obtained from the Lyapunov functional analysis. Then, the reference d -axis flux command is calculated according to the characteristics of stator flux linkage. The voltage control strategy is constructed to stabilise the global system and ensure the convergence of DTFC. Compared to the conventional direct torque/thrust control, the proposed control scheme can apply the space vector modulation to achieve a fixed switching frequency. Consequently, the phenomenon of flux and force ripples inherited from DTFC is improved. The experiment results are provided to validate the proposed control approach. Moreover, a recent work that is based on the optimal control is adopted as a comparison study.

A robust control approach for permanent magnet linear synchronous motor servo drive is presented in this work. First, the nonlinear motor dynamics is described in the stationary reference frame instead of the d–q axis synchronous rotating reference frame. Then, a backstepping control technique is employed to the systematic analysis and design of the drive system. To ensure satisfactory performance of the position control, an adaptive fuzzy neural network is introduced to deal with the uncertain dynamics. The online adaptation law is deduced by using the Lyapunov functional method so that the asymptotic stability of the closed-loop system is guaranteed. In contrast to the conventional current vector control strategy, this work develops a voltage control scheme via a complete theoretic derivation, which considers both the mechanical and electrical dynamics of motor’s behavior. Moreover, the proposed control algorithm performs without using the Park and inverse Park coordinate transformations, and the computation efficiency can be improved as a result. Finally, the practical experiment and a comparison study with the conventional current control approach are carried out to illustrate the effectiveness of the proposed control scheme.

This paper presents a position servo control approach for a permanent magnet linear synchronous motor. The non-linear motor dynamics is expressed in the backstepping control scheme on which a recursive designing procedure is carried out. Based on the desired motion trajectory, the magnetic thrust force is first calculated and then treated as the control objective for the next subsystems. The command voltages to stabilise the whole system are established concerning the electric properties of the magnetic windings. To overcome the impacts of system uncertainties, an adaptive neural network is exploited to estimate the uncertainty and provide necessary compensation in the control effort. Based on the Lyapunov functional analysis, the adaptive laws for online tuning the parameters of the neural networks are derived so that the precision of position servo control can be improved. Compared with the conventional current regulated control scheme, this investigation introduces a voltage-controlled pulse-width modulation with a complete theoretic base, including the mechanical and electrical dynamics. The effectiveness of the proposed approach is verified by the experimental results and a comparison study with a recent work developed in the robust fuzzy PI control scheme.

This paper introduces an observer-based control approach for linear stepping motor (LSM) drive systems. The nonlinear motor dynamics is addressed in the backstepping control framework. Based on this model, a systematic analysis and design algorithm is developed to deal with stabilization and trajectory tracking of linear stepping motor systems. Moreover, to broaden the application range, the backstepping control method is extended to the output feedback control scheme. With simply measuring mover position, an observer-based backstepping controller is constructed to ensure satisfactory performance. In contrast to the conventional current regulated control strategy, this investigation considers a voltage-controlled pulse-width modulation (PWM) strategy with a complete theoretic exploration. The numerical simulations and experimental results illustrate the correctness and effectiveness of proposed approach.

This investigation considers stability analysis and control design for nonlinear time-delay systems subject to input saturation. An anti-windup fuzzy control approach, based on fuzzy modeling of nonlinear systems, is developed to deal with the problems of stabilization of the closed-loop system and enlargement of the domain of attraction. To facilitate the designing work, the nonlinearity of saturation is first characterized by sector conditions, which provide a basis for analysis and synthesis of the anti-windup fuzzy control scheme. Then, the Lyapunov–Krasovskii delay-independent and delay-dependent functional approaches are applied to establish sufficient conditions that ensure convergence of all admissible initial states within the domain of attraction. These conditions are formulated as a convex optimization problem with constraints provided by a set of linear matrix inequalities. Finally, numeric examples are given to validate the proposed method.

This paper introduces a fuzzy guaranteed cost control approach for automated steering of a highway vehicle. The Takagi–Sugeno (T–S) fuzzy model is utilized to depict the dynamics of nonlinear time-varying lateral system. Based on the fuzzy model, an observer-based fuzzy controller is developed so that without knowing road’s curvature the vehicle can track center of the present lane on a curved highway section. Integrating H ∞ control and optimal control strategies, the fuzzy controller and observer are formulated by solving a minimization problem, which is to minimize a given quadratic performance function. Sufficient conditions to ensure minimum upper bound of the performance function are derived in terms of linear matrix inequalities (LMIs). Therefore, the designing work can be efficiently completed by applying the convex optimization techniques. Verified by computer simulation, the proposed method can perform well in driving safety and ride comfort.

The control objective of an active suspension system is to produce excellent sprung mass isolation (i.e. ride quality), not too large a rattle space and good road holding ability. Due to the specific dynamics, the active suspension usually adopts a compromise control policy such as linear quadratic regulation (LQR) or linear quadratic Gaussian (LQG) to determine the feedback gains of the controller. In contrast to the full-state feedback control, a sliding mode fuzzy control strategy is employed to design a stable controller for a quarter-car model of the active suspension system. Motivated by the principle of singular perturbation, a systematic design approach is introduced and verified by computer simulation. It is shown that satisfactory performance can be achieved even if the system is under perturbed conditions.

This study addresses an adaptive backstepping fuzzy control approach for automatic steering vehicles. The vehicle lateral dynamics is presented by an eight degree-of-freedom model, which considers the nonlinear behaviors such as tire force, wheel rotations, forward velocity, and roll motion. According to the existing result, the analysis and design on this model is difficult to perform because of its model complexity. To facilitate the designing work, a robust control scheme which is based on T-S fuzzy control strategy is investigated. The stability condition for ensuring that the control system is uniformly ultimately bounded is derived based on Lyapunov's method. Finally, the effectiveness of the proposed approach is verified via numeric examples.

The unmodeled dynamics of a nonlinear control system is studied by using a fuzzy control approach. Based on variable structure system (VSS) theory, this paper presents two fuzzy control schemes. As the upper bound of system uncertainty is known, it can be used as a clue to form fuzzy control logic. The full inference provides an estimation for the unknown upper bound. Examples are given to illustrate the application of the proposed methods. >

This paper introduces a fuzzy model-based control approach for vehicle lateral system with uncertainties. Based on the Takagi-Sugeno (T-S) fuzzy modelling approach, a robust observer-based fuzzy control scheme, which integrates optimal fuzzy control with H∞ control, is investigated. Sufficient conditions that ensure robust control performance of the system are derived and expressed in terms of Linear Matrix Inequalities (LMIs) such that the fuzzy controller can efficiently be synthesised. In particular, the given performance index function is not only minimised but also confined within a prescribed value. The proposed method is validated in the aspect of effectiveness and correctness via computer simulation.

An adaptive control strategy is proposed for the gross motion control of general purpose industrial manipulators. The control structure consists of a self‐tuning variational control component and a feedforward compensation component. Two control schemes are derived and their performance in tracking accuracy and robustness is evaluated by computer simulation.

This paper presents an adaptive fuzzy observer design to synchronize chaotic systems. The chaotic system is expressed in the form of Takagi–Sugeno fuzzy model (T–S fuzzy system), which considers the effect of model mismatches. Based on this model, an adaptive fuzzy observer is developed to deal with the synchronization of nonidentical chaotic systems. In contrast to the framework of parallel distributed compensation for T–S fuzzy system, the proposed method does not rely on the existence of common matrix P which is imposed in stability conditions. The computer simulation examines the performance of two well-known chaotic systems, Lorenz system and Chua circuit. The results show that the proposed approach cannot only attain synchronization but also is robust to parameter perturbations in the drive system.