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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.

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 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 study presents a robust anti-windup fuzzy control approach for uncertain nonlinear time-delay systems with actuator saturations. The discussed system dynamics is presented by the Takagi-Sugeno (T-S) fuzzy model. To facilitate the design process, the nonlinearity of input saturation is characterized by a specific sector condition. Given a stabilizing dynamic output feedback fuzzy controller, an anti-windup control approach is then developed to maximize the size of estimate of the domain of attraction. Significantly, the convergence of all admissible initial states within this region could be ensured. Based on the Lyapunov-Krasovskii delay-independent and delay-dependent analyses, sufficient conditions for the robust stabilization are derived. These conditions are formulated as a convex optimization problem with constraints represented by a set of linear matrix inequalities, allowing the fuzzy controller to be synthesized more efficiently. Finally, numeric simulations are given to validate the proposed approach.

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.

This study presents a fuzzy model-based control criterion for time-delay chaotic systems with uncertainty. The Takagi-Sugeno (T-S) fuzzy model is adopted for representing a chaotic system possessing a time delay. A unified approach for designing a fuzzy observer, which integrates parallel distributed compensation with an adaptive updating method, is first discussed. An observer-based fuzzy control scheme is then developed to deal with stabilization and tracking problems. Based on Lyapunov stability analysis, sufficient conditions that ensure robust control performance of time-delay chaotic systems are derived. These conditions are represented in terms of linear matrix inequalities and adaptive updating laws. Finally, the proposed approach is validated through numerical synchronization, stabilization, and model tracking control simulations.

This work presents stable composite control criteria for multivariable Takagi-Sugeno (T-S) fuzzy systems. On the basis of the linear matrix inequality (LMI) control strategy and parametric optimization, the composite fuzzy control algorithms are derived. Unlike earlier studies of fuzzy control systems on an LMI framework, this investigation develops a supervisory control approach, such that a fuzzy controller can be synthesized more efficiently. Moreover, a robust control scheme is applied to the T-S fuzzy model with parametric uncertainties. The sufficient conditions are deduced in the form of reduced LMIs and adaptive tuning rules. Finally, numeric simulations are given to validate the proposed approach.

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 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.