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In this study, an attempt has been made to design an output feedback controller for local stabilisation of uncertain linear time-invariant plant with bounded control inputs. The design is accomplished via parametric H∞ loop-shaping control by describing the uncertainty as perturbations to normalised coprime factors of the shaped plant. The shaped plant is designed based on the closed-loop design specifications by appropriately choosing the weighting transfer function matrices. For the existence of stabilising controller, a set of sufficient conditions has been derived in linear matrix inequality framework and subsequently, a region of attraction is estimated such that the control inputs never exceed the pre-specified bounds. To illustrate the effectiveness of the proposed method, a numerical example has been elucidated. [ABSTRACT FROM AUTHOR]

This study presents an eigenvalue assignment control architecture for a class of linear time-varying (LTV) systems in the presence of time-varying disturbances. Eigenvalue assignment architecture is presented as a way to achieve feedback stabilisation for LTV disturbed systems. With an input compensator design, the output of slowly LTV systems with disturbances can practically track the reference. Moreover, the implementation of the proposed eigenvalue assignment technique in practice has been demonstrated by using adaptive estimation of time-varying disturbances. [ABSTRACT FROM AUTHOR]

This study aims at developing new linear matrix inequality (LMI) conditions for robust D-stability and D-stabilisation of linear time-invariant uncertain systems in polytopic domains. An existing LMI condition for robust D-stability is lifted into those of larger dimensions that are given as functions of a certain integer κ. Based on the consequent conditions, new sufficient conditions for the existence of a state-feedback control law ensuring the robust D-stability of the closed-loop system is proposed in terms of LMIs. Finally, examples are given to illustrate the benefit of the proposed methods. [ABSTRACT FROM AUTHOR]

This study presents an online motion planning algorithm for generating reference trajectories during flight phases of a planar monopedal robot to transfer the configuration of the mechanical system from a specified initial pose to a specified final one. The algorithm developed in this research is based on the reachability and optimal control formulations of a time-varying linear system with input and state constraints. A two-level control scheme is developed for asymptotic stabilisation of a desired period-one orbit during running of the robot. Within-stride controllers, including stance and flight phase controllers, are employed at the first level. The flight phase controller is a feedback law to track the reference trajectories generated by the proposed algorithm. To reduce the dimension of the full-order model of running, the stance phase controller is chosen to be a parameterised time-invariant feedback law that produces a family of two-dimensional finite-time attractive and invariant submanifolds. At the second level, the parameters of the stance phase controller are updated by an event-based update law to achieve hybrid invariance and stabilisation. To illustrate the analytical results developed for the behaviour of the closed-loop system, a detailed numerical example is presented. [ABSTRACT FROM AUTHOR]

In this study a recent result on the quadratic stability of switched linear time-invariant systems is applied to the problem of vehicle rollover prevention based on selectively switching between two suspension settings. The suggested switching control scheme reduces the transient magnitude of the load transfer ratio (LTR), which is the rollover detection metric, when and if it is above a certain threshold by switching to a stiffer suspension setting from a default soft one. In this way, the controller activates only when the potential for rollover is significant as measured by the LTR. The switching suspension parameters are chosen in a way that results in closed-loop switched roll dynamic response that is guaranteed to be quadratically stable under arbitrary switching. Moreover, a spectral condition is given that allows checking the stability of the switched roll dynamics that is subject to actuator uncertainty when the upper and lower bounds of the uncertain parameters are known. Numerical simulations demonstrate the efficacy of the suggested switched control approach. [ABSTRACT FROM AUTHOR]

Fixed parameter iterative learning control (ILC) for linear-time invariant, single-input single-output systems subject to output noise is analysed with the intent of predicting the expectation of the underlying 'noise-free' mean square error (Euclidean norm) of the time series on each iteration. Explicit formulae are obtained in terms of the 'lifted' matrix models of the plant. Computational experiments are used to confirm the correctness of the proposed properties. Finally, frequency domain formulae are derived to provide insight into links between plant characteristics, noise spectra and other ILC parameters, and illustrated by application to the inverse-model-based ILC algorithm. [ABSTRACT FROM AUTHOR]

The authors study the consensus problem in networked non-holonomic systems (NHSs). Based on Brockett's stabilisability condition, the stabilisation of NHSs is usually considered by using time-varying or discontinuous control (including switching control). Observing that the consensus problem has more flexibility than the stabilisation one, the authors aim to establish time-invariant continuous state feedbacks for the problem at hand. [ABSTRACT FROM AUTHOR]

An integrated batch-to-batch control and within-batch online control strategy for tracking product quality trajectories in batch processes is proposed. On the basis of a batch-wise linear time-varying perturbation model, iterative learning control (ILC) is implemented for batch-to-batch control and the convergence of tracking errors under ILC is guaranteed. Within a batch, a predictive model is constructed by directly partitioning the linear time-varying model according to time, then batch model predictive control with shrinking horizons is applied online to reduce the effects of model-plant mismatch and/or unknown disturbances. By properly combining these two control methods, the integrated control strategy can complement both methods to obtain good performance. The proposed strategy is demonstrated on a simulated batch polymerisation process, and the results show that the performance can be improved quite significantly under the integrated control strategy than only under ILC, especially when disturbances occur. [ABSTRACT FROM AUTHOR]

The authors present an enhanced composite nonlinear feedback (CNF) control technique for discrete-time systems with input saturation and with external disturbances. The conventional CNF control is proven to be capable of yielding better transient performance in tracking control for systems with input saturation. However, when the given system has external disturbances as in most practical situations, the conventional CNF control yields some steady-state errors in the resulting output response. The enhanced CNF technique presented is an extension of the conventional one. It retains the good transient properties of the conventional CNF control and at the same time has the additional capacity for eliminating steady-state bias because of unknown constant disturbances. A numerical example and practical disk drive servo system design are provided to demonstrate the effectiveness of this control technique. Experimental results show that the new design yields a huge improvement over classical approaches. [ABSTRACT FROM AUTHOR]

The two-dimensional (2-D) system theory iterative learning control (ILC) techniques for linear time-invariant discrete systems are extended to the cases of linear time-variant discrete systems. By exploiting the convergent property of 2-D linear time-variant discrete systems with only one independent variable, a kind of 2-D system theory ILC approach is presented for linear time-variant discrete systems. Sufficient conditions are given for convergence of the proposed ILC rules. Two numerical examples are used to validate the ILC procedures. [ABSTRACT FROM AUTHOR]

The optimal updating law of the problem of optimal higher-order iterative learning control is given explicitly and the convergence of the tracking errors is proved. Two examples show that the convergence of the optimal higher-order iterative learning control is faster than that of the optimal one-order iterative learning control. [ABSTRACT FROM AUTHOR]

A method of designing linear parameter varying (LPV) control systems based on the parameterisation of all quadratically stabilising controllers is presented. Conceptions of doubly coprime factorisation and Youla parameterisation of LTI systems are extended to LPV systems with respect to quadratic stability using a state space expression. The parameterisation of closed loop systems, which are affine with any quadratically stable Q-parameter, is then described. This description enables the application of the Q-parameter approach to a variety of LPV control system designs. Above all, a systematic H∞ strategy is focused on and a necessary and sufficient condition and also a design scheme of Q to obtain L2-gain performance, are clarified. [ABSTRACT FROM AUTHOR]

Lower and upper bounds are given to calculate the smallest possible left-half-plane where all poles of a single-input single-output plant with no zeros can be stabilised using constant gain feedback. The results are expected to have immediate application areas in control system theory and filter design. [ABSTRACT FROM AUTHOR]