In this paper, the problem of prescribed-time tracking control with unified prescribed performance is studied for multi-input multi-output (MIMO) nonlinear systems with mismatched nonvanishing disturbances, actuator faults, and time-varying control coefficients whose sign and magnitude are both unknown. On the one hand, a novel prescribed-time stability criterion using Nussbaum functions is proposed to deal with the issues raised by the presence of mismatched nonvanishing disturbances, actuator faults, and time-varying control coefficients. This criterion is of independent interest and can be used beyond the control problem addressed in this paper. On the other hand, based on the proposed stability criterion, a prescribed-time tracking control framework is developed so that the tracking error converges to zero within a prescribed time, in the presence of the aforementioned complicating factors. Compared with existing asymptotic stability results for uncertain MIMO nonlinear systems subject to unknown control coefficients, the proposed framework guarantees that the tracking error remains within the unified prescribed performance boundary, which is uniform with respect to different initial tracking errors, thereby eliminating the need for controller redesign and stability reanalysis. The proposed control method is verified via an electromechanical system and a robot manipulator system in numerical simulation.

In this article, a robust adaptive tracking control scheme is developed for fully actuated systems (FASs) in the presence of nonlinear uncertainties, input disturbances, and multiplicative input matrices perturbation, capable of achieving the adjustable transient and steady-state performance. In comparison with the conventional and finite-time prescribed performance control (PPC) methods subject to the initial value constraint, the proposed prescribed-time PPC scheme blends the FAS approach with the speed transformation, guaranteeing the full-state asymptotic tracking with prescribed-time prescribed performance. First, a basic fully actuated controller is introduced, yielding a closed-loop tracking error system with a linear dominant part. Second, the speed transformation is applied to the closed-loop system, converting the initial PPC problem into the asymptotic convergence problem of the transferred error system and completely eliminating the initial value constraint. Third, the auxiliary control input and adaptive law embedded with positive integrable time-varying functions are devised, ensuring the boundedness of all closed-loop signals and the desired performance. Simulation studies are conducted to demonstrate the effectiveness and superiority of the presented control strategy.

In this article, a prescribed-time output feedback controller is proposed for a class of uncertain nonlinear systems with unknown control coefficients and mismatched nonvanishing disturbances. Both unknown control coefficients and mismatched disturbances are tricky to address by the existing prescribed-time output feedback control frameworks. Differently, a novel prescribed-time control criterion in conjunction with Nussbaum functions is proposed, and prescribed-time stability is achieved. Furthermore, design methods for a state observer and a prescribed-time output feedback controller are developed. With the proposed control design, both the system output and observer errors are rigorously proved to converge to zero within a prescribed time. Moreover, the unified prescribed performance (UPP) of the system output and the satisfaction of output constraints are simultaneously achieved. Numerical simulations and experiments are provided to illustrate the effectiveness of the proposed control design.

Asymptotic state regulation of fully actuated systems (FASs) with time-varying unknown parameters, perturbed input matrices, and nonlinear uncertainties is considered. Compared to the closely related results on FASs with time-varying parameters, the requirement that the time-varying parameters are differentiable and the assumptions imposed on their derivatives in those works are no longer needed in this article, which means that many types of time-varying parameters that were difficult to handle by previous methods, such as those that are continuous and bounded but not differentiable, can now be handled. Furthermore, inspired by the congelation of variables method, a novel robust adaptive method is proposed, which achieves the global asymptotic convergence of the state variables instead of the global boundedness obtained in previous methods, and guarantees the global boundedness of the estimation. In the developed controller, the adaptive part compensates for time-varying parameters, and the robust part overcomes the effects of incomplete compensation and other remaining uncertainties. Moreover, a parallel extension of the developed method to the disturbed case and a discussion on parameter selection are given. Finally, the proposed method is successfully applied to the control of resonant circuit systems and Norrbin ship steering systems.

This study investigates the problem of achieving consensus within a prescribed time for general linear multiagent systems (MASs) operating over directed communication graphs, particularly when agents can only access relative output data via their onboard sensors. Under the assumption of strong observability, we design a periodic delayed output measurements-based distributed observer to recover the relative state information. Leveraging the reconstructed states, a linear time-varying control protocol is developed to ensure consensus is attained within the desired time. In contrast to conventional approaches, our method brings several key benefits. Most importantly, it removes the requirement for direct data exchange over the network, making the system inherently robust against cyber-attacks. Furthermore, the protocol is entirely distributed, which enhances adaptability to dynamic communication structures. At last, since the proposed method utilizes linear state feedback, it avoids the need for real-time solutions of system-related differential equations, thus reducing computational overhead. Numerical simulations demonstrate the efficacy of the proposed strategy.

This paper studies the issue of switched data driven model predictive control (MPC) for a class of hybrid nonlinear systems with modal dwell time (MDT) restriction, where each subsystem is approximated by a T-S fuzzy system with bounded uncertainties. The unknown system matrices are characterized by a quadratic-matrix-inequality representation using the input-state-membership data. On this basis, a numerically tractable semi-definite programming (SDP) problem is formulated to design fuzzy-dependent feedback control law in a receding horizon manner for each switched mode, resulting in the optimization of worst-case infinite-horizon performance cost. Utilizing a set of feasible solutions of the constructed SDP problem, a feasible region and the corresponding approximated reachable set are deduced for each subsystem, based on which an algorithm is proposed to determine an admissible MDT ensuring the persistent feasibility of the switched data-driven MPC and the robust stability of the closed-loop system. The validity and potential of the theoretical results are illustrated through numerical applications to a single-link robot arm and a class of tail-sitter vertical take-off and landing unmanned air vehicles.

Artificial intelligence techniques have been extensively employed in anomaly detection tasks for massive systems and equipment across numerous industries, achieving notable success. Nevertheless, classical machine learning methods typically possess a simplistic design, which occasionally fails to satisfy the detection demands of minor anomalies in certain complex tasks. Conversely, the interpretability of deep learning methods is often insufficient to convince domain experts and operators. Consequently, achieving a balance between detection accuracy and interpretability remains a critical and often conflicting challenge. This article proposes an expert experience-based algorithm unfolding (EAU) network and a conditional canonical correlation analysis (CCCA) theory for anomaly detection of spacecraft under multiple operating conditions, aiming to ensure detection accuracy while enhancing interpretability. First, the EAU network integrates the experiential knowledge with the Lasso regression model and employs sparse coding to iteratively expand it layer by layer (LbL), facilitating deep feature extraction from the original telemetry data. Second, the CCCA method formulates the residual vector on the premise that the correlation between the regularization components of the input and output sets will change markedly before and after the anomaly appears. It subsequently compares the HotellingT2statistic of each sample with the detection threshold, which was constructed based on the kernel density estimation (KDE) method, to discover the evolution of the anomaly. Finally, multigroup comparisons on two simulations and two real satellite-telemetry datasets verify the superior overall performance of the proposed method and provide guidance for selecting anomaly detection approaches.

This article studies the global asymptotic neural network (NN) tracking problem for full-state error constrained spacecraft attitude systems with actuator faults, inertia uncertainties, and external disturbances. In the literature, most existing NN control schemes can only achieve semiglobally bounded stability since the approximation capability of NNs is confined to a compact domain called the approximation domain. Differently, an attitude tracking control strategy in conjunction with a modified smooth switching mechanism is proposed to ensure the global asymptotic stability. Specifically, an adaptive NN controller is developed within the approximation domain to address unknown nonlinearities, and a robust controller is activated outside the approximation domain to drive back the system states. With the proposed design, both attitude and angular velocity errors (collectively defined as the full-state errors) are rigorously proven to globally asymptotically converge to zero. Moreover, the full-state errors are preserved within the unified prescribed performance constraints, which are uniform with respect to any initial conditions, thereby eliminating the requirement for offline computation of the performance boundary. In addition, the undesirable feasibility conditions on virtual control laws are completely eliminated. Theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

The existing adaptive iterative learning control approaches with variable constraints mainly consider the matched uncertainties and only ensure the boundedness of parameter estimation errors. In this paper, an adaptive iterative learning control (AILC) method with prescribed performance constraints and improved parameter estimations is developed for a class of uncertain nonlinear strict-feedback systems. The prescribed performance control is achieved through generating a preset error trajectory in each iteration within the performance envelope and making the error of the actual tracking error versus the preset one small enough all the time. The improvement of the parameter estimation performance is realized by reconstructing the parameter estimation errors and using them to modify the differential-difference adaptive laws. The control algorithm is designed based on the dynamic surface control method and thus free from the “differential explosion” problem. It is guaranteed via the Lyapunov theory that all signals of the closed-loop system are semi-global bounded, the system output could track the given reference trajectory with the prescribed performance in each iteration and the$\mathcal {L}_{2}$norms of the estimation errors are uniformly ultimately bounded along the iteration-axis. Additionally, two simulation examples illustrate the effectiveness and advantages of the proposed adaptive iterative learning control method.

This paper proposes a low-complexity tracking control framework for uncertain nonlinear systems in strict feedback and normal forms, respectively. By leveraging a smooth scaling function, these control schemes ensure unified prescribed performance for the output tracking error of strict feedback nonlinear systems and the full-state tracking errors of normal form nonlinear systems. The notion of unified prescribed performance allows for different performance behaviors via performance functions, which can be either constant or time-varying with arbitrarily large initial values. The main contribution is achieving unified prescribed performance for full-state tracking errors without imposing feasibility conditions, a limitation of existing approaches. To eliminate these strict conditions, we introduce a uniform transformation independent of initial conditions. Additionally, the proposed control schemes are low-complexity since they do not require adaptive mechanisms or function approximation to deal with uncertainties and disturbances. The effectiveness of these frameworks is demonstrated through comparative analysis.

This paper proposes an adaptive tracking control framework for nonlinear systems with unmodeled dynamics, ensuring both practical prescribed-time convergence and prescribed performance for full-state errors. Existing methods often depend on unbounded gains, focus only on output tracking error, or rely on initial conditions, restricting their practical applicability. To overcome these issues, we propose a novel adaptive control framework that constrains full-state errors independent of initial conditions and drives them to a prescribed region within a predefined time. This is achieved by using a bounded, continuously differentiable, prescribed-time gain. An adaptive mechanism with a dissipating term is designed to handle unmodeled dynamics and guarantee zero tracking error even under nonvanishing disturbances. Moreover, a smooth scaling function is introduced to enforce desired transient and steady-state performance while reducing large initial control effort. Numerical simulations demonstrate the superiority of the proposed method compared to existing approaches.

For quaternion-based spacecraft attitude tracking control, most existing prescribed performance control (PPC) schemes require that the scalar part of the error quaternion remain non-zero during the attitude maneuvering, meaning that only local operational range is allowed, which is too restrictive from practical point of view. Differently, by imposing a novel performance constraint, a piecewise virtual control law without singular term is designed, which is singular in most of the existing control schemes if the scalar part of the error quaternion is equal to zero, thus naturally obviating the classical assumption and resting in a global solution. Moreover, the unified prescribed performance constraints are imposed on both the attitude error and virtual angular velocity error. With such design, the performance boundary is uniform with respect to initial error, which implies that off-line computation of performance boundary for initial error can be avoided. Particularly, the initial value of the virtual angular velocity error is difficult to obtain off-line. In addition, by utilizing neural network approximation method, the Nussbaum gain technique and a positive integrable function, the proposed control is able to achieve asymptotic attitude tracking in the presence of inertia uncertainties, external disturbances and actuators fault, as rigorously authenticated by Lyapunov stability theory. A numerical example is provided to verify the effectiveness of proposed control scheme.

In this paper, a robust adaptive control scheme based on the unknown input observer is proposed for fully actuated systems with uncertainties. First, a nonlinear reduced-order unknown input observer with an integral term is introduced to decouple the uncertainties in the system and suppresses the output noises via the action of integral term. Then the linear matrix inequality for solving the observer gains is given by using the linear parameter varying method to treat the nonlinearity. Second, a robust adaptive controller based on the proposed observer, with the adaptive law to estimate the bound of uncertainties, is designed to make the states uniformly ultimately bounded. Due to the design of robust part, the ultimate bounds of states of the closed-loop system can be adjusted via the designed parameters. A simulation of the electromechanical system is given to demonstrate the effectiveness of the proposed method.

In this paper, we investigate the tracking issue for fully actuated systems (FASs) with parameter uncertainties and input quantization. Different from models of the existing adaptive control for FASs, the input matrix herein incorporates uncertain parameters. By utilizing the full-actuation properties, straightforward adaptive state feedback controllers can be implemented in the trajectory tracking of single-order and multi-order FASs, ensuring that the tracking error can converge to an arbitrarily small vicinity of the origin. The simulations of mass-spring-damper systems and permanent magnet synchronous motors (PMSM) validate the effectiveness of the given method.

This paper studies the prescribed-time tracking control problem for nonlinear systems with unknown time-varying parameters, mismatched nonvanishing uncertainties, unknown control coefficients, and potential actuator faults. The proposed control strategy employs a prescribed-time adjustment function to guarantee that state errors converge to zero within a specified time, despite the presence of nonvanishing mismatched uncertainties. The proposed controller avoids the need to use adaptive mechanisms and is therefore simple to implement. Moreover, the proposed control strategy does not require the control coefficient bounds to be known. Based on a prescribed-time scaling function and a barrier function, prescribed performance for state errors is guaranteed, which is uniform with respect to initial conditions, eliminating the need for an offline optimization algorithm to determine the controller gains. The simulation results demonstrate the effectiveness of the proposed control framework.

This paper investigates the practical prescribed-time control problem for uncertain high-order strict-feedback systems (SFSs) using the fully actuated system (FAS) approach, unlike previous research that primarily focuses on asymptotic stability control for such systems. First, the uncertain high-order SFSs are transformed into a high-order FAS. Next, a performance function is introduced to perform a coordinate transformation on the FAS, turning the original control problem into a boundedness issue for the transformed system. Then, neural networks are used to approximate the system’s uncertain terms, and a controller is designed for the transformed system based on the FAS approach. Using Lyapunov theory, it is proved that all signals in the closed-loop system are uniformly ultimately bounded, and the system output can converge to a specified region within a prescribed time. Finally, the effectiveness of the proposed control method is demonstrated through simulations of the RLC circuit system, a numerical example and an electromechanical system.

This paper investigates the event-based adaptive global prescribed performance control (PPC) for a class of uncertain semi-strict feedback nonlinear systems with application to the networked spacecraft simulator (NSS) system. First, to reduce the communication frequency, a novel dynamic periodic event-triggering mechanism (DPETM) is proposed. It can not only avoid continuously monitoring the event-triggering conditions and the Zeno phenomenon in mechanism, but also reduce the trigger frequency on the premise of maintaining the closed-loop system performance. Then, an event-based adaptive global prescribed performance controller is designed by using the backstepping methodology and adopting the time-varying scaling transformation and the nonlinear transformation. The proposed method can ensure that all signals of the resulted closed-loop system remain bounded, meanwhile, the tracking error is always within the predetermined range for any initial values. Finally, the effectiveness, the engineering feasibility and the advantages of the proposed control method are demonstrated by utilizing an NSS system.

The prescribed-time semi-global control for a class of time-varying uncertain systems under a nonlinear growth condition is achieved via linear time-varying feedback. The involved nonlinear uncertainties are categorized as unmatched uncertainties (depending on states and time) and matched uncertainties (depending on time only). Both state feedback and observer-based output feedback are constructed relying on the properties of parametric Lyapunov equations and the time-varying gains acquired by solving scalar differential equations. The proposed output feedback approach features a separation principle, that is, the construction of prescribed-time observer and prescribed-time state feedback is conducted separately. The proposed control scheme is validated by simulations carried out on a standard mechatronics system with complicated loads.

This adaptive formation tracking control is investigated for high-order fully actuated (HOFA) multiagent systems (MASs) with unknown nonlinear dynamics and full-state constraints. To tackle the dynamic uncertainty while maintaining safety constraints on system state, a novel hierarchical formation control framework is presented. First, a nonlinear mapping function (NMF) is introduced, which, by integrating HOFA theory, effectively transforms the original constrained system into an unconstrained HOFA tracking error model, thus removing the feasibility conditions typically required in traditional barrier Lyapunov function methods. Subsequently, distributed observers are designed in the upper layer for followers to estimate the leader's information, while an adaptive formation controller is directly constructed for each follower in the lower layer using the fully actuated theory. Particularly, the neural network approximators are used to learn unknown nonlinear dynamics. By employing Lyapunov stability theory, the designed formation controller guarantees that the entire state stays within the specified constraint set while also ensuring the desired formation performance. Finally, the developed formation control algorithm is proven effective by applying it to a network of multiple robotic arm systems.

Robust adaptive control of fully actuated systems (FASs) with unknown parameters, perturbed input matrices and nonlinear uncertainties is considered. Two novel robust adaptive controllers are developed for the two cases where the unknown parameters are time-varying and constant. For both cases, different from the existing results on FASs with unknown parameters, this article allows the existence of a perturbation matrix that satisfies a certain assumption in the input matrix. Furthermore, for the case of time-varying parameters, under relaxed system assumptions, the global boundedness of the state variables and the estimation error is guaranteed. For the case of constant parameters, under certain assumptions on the nonlinear uncertainty and known nonlinear functions, no pre-estimation of the unknown parameters is required and the state variables globally asymptotically converge to the origin. In addition, a parallel extension of the proposed methods to the generalized multiorder FAS case is also given. The effectiveness of the developed methods is shown by the successful application in the control of electromechanical systems.

It is well-known that the stabilizing control of nonholonomic systems is usually divided into two independent steps. This incurs a discontinuous switching control problem when system states start from certain regions. In light of this, we study the continuous and smooth stabilization control issues for nonlinear systems with two piecewise continuous or even smooth stabilization controllers. First, the sufficient conditions for the existence of these controllers are provided. Then, we use the controller extension method to construct some intermediate auxiliary controllers that can link the piecewise controllers given in advance continuously or even smoothly. In addition, by combining model transformation, including the cascade and fully actuated ones, with the extended state observer, we successfully employ the proposed controller extension method to solve the stabilization control of an underactuated surface ship subject to external disturbance.

The attitude tracking control with unwinding-free performance for rigid spacecraft is studied in this article. A full-state feedback control law based on a hyperbolic sine function is developed such that the resulted closed-loop system can achieve two stable equilibria. By Lyapunov stability theory and Barbalat’s Lemma, it is proven that the obtained closed-loop system is almost globally asymptotically stable, and achieves unwinding-free performance. Further, by constructing a strict Lyapunov function, it is demonstrated that the two stable equilibria are exponentially stable. Moreover, subsets of attraction regions corresponding to each stable equilibrium are characterized. The simulation results illustrate that the proposed attitude control scheme can effectively avoid the unwinding problem during attitude tracking.

In this article, a circulation design for robust eigenvalue assignment in a stabilizable linear system via state feedback is developed based on a complete parametric partial eigenstructure assignment (ESA) approach. It replaces, in each round, a subset of the open-loop (OL) eigenvalues and minimizes simultaneously the condition number of the closed-loop eigenvector matrix (EVM). It is shown that, in those rounds where a real eigenvalue of order 1 is replaced, the minimization of the condition number can be converted into an equivalent convex minimization problem and thus a globally optimal feedback gain matrix can be obtained. When all the OL eigenvalues to be replaced are real ones of order 1, the proposed circulation design generally turns out to be a very simple method for the entire ESA, which possesses good numerical reliability since matrix inverse operations are completely avoided. The proposed circulation design is effectively demonstrated with an illustrative example.

Most existing barrier Lyapunov function (BLF)-based control schemes are only able to handle box-type constraints. However, many physical constraints are ellipsoidal constraints in real-world applications. Therefore, an asymptotic tracking control scheme embedded with an improved command filter is proposed for MIMO nonlinear systems under irregular ellipsoidal output constraints. A novel transformation function, explicitly depending on original constraints, is constructed. With such a design, not only ellipsoidal constraints but also partial ellipsoidal constraints, box-type constraints, and their combination-type constraints can be handled. Moreover, an innovative adaptive nonlinear filter is designed to resolve the complexity explosion problem caused by the repeated differentiations of virtual controllers. Different from the existing filters, the boundary layer errors of the proposed adaptive filter are fully compensated. Furthermore, tracking error is proved to be asymptotically converged to zero with the existence of model uncertainties and external disturbances. In addition, all signals within the closed-loop system are rigorously proved to be bounded. A numerical example is presented to verify the effectiveness of the designed control strategy.

This article presents a predictive control strategy for Euler–Lagrange systems (ELSs) with piece-wise input constraints, includes saturation and dead-zone, by employing fully-actuated system (FAS) approaches. Our comprehensive system design methodology integrates model transformation, controller design, and benchmark applications. Unlike existing methods, the proposed energy-based model transformation (EMT) method first reformulates under-actuated ELSs into (sub-)FASs under general conditions, revealing system controllability and laying a model foundation for controller design. Ulteriorly, the proposed inverse dead-zone predictive cascade control (IDPCC) algorithm employs a cascaded optimization process to redefine new input constraint boundaries, thereby effectively solving the receding horizon optimization problem and circumventing complex nonlinear piece-wise input constraints. By combining IDPCC algorithm with EMT methods not only enhances the solvability of optimization problems in ELSs but also ensures the stability of the corresponding closed-loop system. Finally, the effectiveness of the model transformation methods is validated through four benchmark examples, including simulations of the RTAC system.

This paper studies discrete-time high-order fully actuated (HOFA) model reference tracking (MRT) control for a class of combined spacecraft simulators (CSSs) with disturbances. Firstly, a discrete-time second-order fully actuated system modeling of the CSS system is carried out and generalized to a general discrete-time HOFA system (HOFAS) with disturbances, and the MRT control problem is formulated. Then, by designing a discrete-time HOFA MRT controller consisting of feedback and feedforward components, a linear closed-loop system with an arbitrarily assignable eigenstructure can be obtained. The convergence of the tracking error is proved by introducing the Sylvester equation condition and final value theorem of discrete-time sequence. Further, using the arbitrariness of the eigenstructure configuration, the control performance can be improved via a multi-objective optimization design. Finally, the MRT control of CSS system with disturbances is successfully realized using the proposed technique and comparative simulations are carried out, thus verifying the effectiveness and advantages of the technique.Note to Practitioners—This paper is inspired by MRT tasks for practical CSS systems and also contributes to the advancement of HOFAS theory in the discrete-time domain. Traditional researches on MRT control of discrete-time systems are basically carried out under the framework of the first-order state-space approach which are suitable for solving state vectors rather than control vectors, and thus it is difficult to handle the control problems of complex nonlinear systems. Moreover, converting the original higher-order system to a first-order one and then completing the associated control design would significantly increase the computational effort and complexity. Instead, this paper directly investigates the MRT control of discrete-time HOFASs and designs the HOFA MRT controller, which can easily handle nonlinearities and enable the system output to track the output of the reference model, and produce a closed-loop system with an arbitrarily assignable eigenstructure. Possible application areas include many discrete-time fully actuated systems in a practical or mathematical sense, including spacecraft control, robot or intelligent vehicle control, and UAV control, etc. As long as their discrete-time HOFA models can be established by physical modeling or model transformation, the techniques proposed in this paper are applicable.

In this paper, an observer that combines the design ideas of disturbance observer and state observer is proposed for fully actuated systems with external disturbances. The integral term is also added to the observer to improve steady-state accuracy and design degrees of freedom. With the use of the linear parameter varying method, the Lipschitz nonlinearity is converted to the varying linearity, and the observer gains are then determined via solving a set of linear matrix inequalities. Furthermore, due to the full-actuation property, a control scheme for state stabilization using estimated states and disturbances is given. All the states are ultimately uniformly bounded if the linear part of the closed-loop system is stable. An example of an electromechanical system is presented to demonstrate the practicality of the proposed method.

This paper investigates the problem of tracking control for high-order strict-feedback systems (HOSFSs) with both parametric uncertainties and nonlinear function uncertainties. Based on the high-order fully-actuated (HOFA) system approach, a direct high-order robust adaptive command filtered backstepping (HORACFB) design is proposed. We adopt the concurrent learning (CL) technique to identify the unknown parameters through the examination of linear independence within the recorded data. To do so, we have introduced a novel parametric model for constructing the parameter update law under the presence of both parametric uncertainties and nonlinear function uncertainties. This is achieved by introducing new filtering variables to avoid utilizing high-order derivative information of system states required by the existing CL technique. The proposed framework can guarantee both reference tracking and unknown parameter estimation convergence (to their true values) with arbitrary accuracy that can be tuned by the designer. The closed-loop system proves to be uniformly ultimately bounded. Last but not the least, the proposed control framework avoids converting the high-order systems into first-order ones, thereby reducing unnecessary backstepping steps.

This paper concentrates on the optimal tracking control for a discrete-time nonlinear time-varying fully-actuated system (FAS) with full state constraints and time-varying delays. An explicit analytical predictive controller is constructed by incorporating the predictive control scheme and the constraint elimination technique into FAS approaches. The proposed PEC-FAS scheme dexterously eliminates full state constraints without introducing new constraints and uncertainties, makes full use of full-actuation property to compensate the time-delay actively. Especially, it reduces the coupling degree of the states, and eliminates the nonlinearities arising from the original system and constraints elimination process simultaneously. These efforts largely improve the solvability of the strongly nonlinear and coupled optimization problem with constraints and time-delay. Meanwhile, by analytically expressing the predictive information by off-line calculation, the nonlinear optimization problem is converted into a series of linear convex optimization problems without constraints and time-delay, which fundamentally mitigates the computational burden and the design complexity compared with the previous nonlinear predictive control approaches. Theoretically, it is demonstrated that the proposed controller is recursively feasible, which also owns a recursive explicit analytical predictive controller sequence in each predictive horizon, and the tracking error system is asymptotically stable under the certain condition with predictive parameters. Finally, the simulation of a benchmark under-actuated application of a rotational translational actuator (RTAC) system, demonstrates the effectiveness and the simplicity of the proposed PEC-FAS scheme.

The event-triggered neural network (NN) adaptive control problems based on the fully actuated system (FAS) approach are studied for uncertain strict-feedback nonlinear systems. Firstly, event-based NN are utilized to approximate the unknown system nonlinearities, and the assumption that nonlinearities are known at all times is removed in the existing literature. Different from the backstepping design approach that the virtual control signals are non-differentiable at each triggering instant, this problem is avoided by utilizing the FAS approach. Then, an event-triggered NN adaptive controller that only receives states and using parameter estimations at each triggering instant is developed by using the FAS approach. To stabilize the control system, the adaptive parameters, the NN weights estimations, and Lyapunov solutions are used to design a novel adaptive event-triggering scheme (ETS), which can compensate the effect of triggering and save communication resources. It is proven that the ultimate boundedness of the system is guaranteed and the Zeno behavior can be eliminated. Finally, the effectiveness of the proposed method is illustrated by two simulation examples.

Fully actuated system (FAS) approach was proposed in 2020 and 2021 as a general framework for control system analysis and design based on a newly discovered general type of fully actuated models for dynamical systems. Due to its great advantages and power in dealing with complicated nonlinear time-varying and time-delay systems with possibly nonholonomic features, it has attracted much attention in the control community immediately since its birth. By now, numerous results have been produced for analysis and control of various types of complicated systems, which cover the topics of adaptive control, robust control, predictive control and fault-tolerant control, and involve time-varying and time-delay systems, discrete-time systems, stochastic systems and even impulsive systems. Meanwhile, a large number of applications have also been carried out. These include spacecraft control, aircraft and quadrotor control, robot control and control of power electronic systems and servo systems. In this article, an overview of the FAS approach is presented, ranging from models, basic theories, and control techniques to applications.

In this article, a high-gain observer (HGO)-based differentiator is proposed to approximate the derivatives of the virtual control to ease the "explosion of complexity" problem in backstepping for the second-order strict-feedback systems (SOSFSs) and high-order strict-feedback systems (HOSFSs). Unlike the existing high-order command-filtered backstepping or extended dynamic surface control that needs to tune a large number of parameters, this proposed high-order HGO-based backstepping (HOHGOB) scheme can improve the derivative approximation performance by only tuning a single parameter, i.e., the observer gain. A further advantage of the proposed HOHGOB scheme, in addition to its simplicity and ease of implementation, is that the estimation error of the derivatives shrinks to zero as the observer gain grows to infinity. We have also rigorously established that the states of the closed-loop system achieve uniform ultimate boundedness under the designed high-order backstepping controller. Additionally, the output tracking error can be made arbitrarily small by the designer. The efficacy of the proposed scheme is numerically validated through a benchmark application to a single-link robot arm.

In this article, a trajectory tracking control strategy is proposed for robot manipulators via a fully actuated system (FAS) approach, which has shown its simplicity and flexibility for most of the nonlinear controller design. However, the motion control for robot manipulators is more complicated since unknown dynamical model, external disturbances, friction forces, and various physical constraints are required to be considered. Therefore, the FAS approach cannot be straightforwardly applied. To address these challenges, the dynamic model of robot manipulators is established via model identification methods. Furthermore, based on the identified model, an FAS composite control strategy with simple structure is designed, which is achieved by integrating a high-order disturbance observer (HODO) in the inner loop, with an FAS trajectory tracking controller in the outer loop. Specifically, the HODO is utilized for handling the uncertain dynamics and external disturbances. Moreover, the controller gains are optimized using a gradient-based optimal parameter tuning method (OPTM). By imposing joint angle constraints, joint angular velocity constraints, and input torque limits into the formulation, the OPTM also ensures the satisfaction of these physical constraints. Numerical simulations and experiments are provided to validate the performance of the proposed controller.

In most existing results, event-triggered controllers are designed based on the backstepping design approach for uncertain strict-feedback nonlinear systems (SFNSs). However, the transmitted signals in the event-triggered scheme (ETS) are discontinuous, which makes the repetitive differentiation of virtual control signals undefined. To overcome this deficiency, this article designs an event-triggered adaptive controller for uncertain SFNSs based on the fully actuated system (FAS) approach. Since the system states and the adaptive parameters are only updated at each triggering instant, the original dynamics cannot be completely removed by using the FAS approach, leading to that the asymptotic stability of the control system is difficult to be guaranteed. To handle such a problem, an ETS with the adaptive parameters is constructed based on Lyapunov method to compensate the effect of triggering. As a result, the asymptotic stability of the system can be guaranteed in the presence of nonlinearities without the global Lipschitz condition, and Zeno behavior can be avoided by using the contradiction method. Furthermore, a positive lower bound for interevent intervals can be got by adding a constant into the ETS, which ensures that the system is practically stabilizable under the bounded nonlinearities. Finally, two simulation examples are presented to demonstrate the superiority and effectiveness of the proposed approach.

Although the literature on control of overhead crane systems is extensive and relatively mature, there is still a need to develop strategies that can simultaneously handle factors such as the double pendulum effect, variable cable length, input saturation, input dead zones, and external disturbances. This article is concerned with adaptive tracking control for underactuated overhead cranes in the presence of the above-mentioned challenging effects. The proposed controller is composed of the following two components. First, a tracking signal vector that effectively reduces system swing magnitudes is constructed to improve the transient performance and guarantee smooth operation of the system. Second, an adaptive law is designed to estimate and compensate for the overall effects of the friction, the external disturbances, and certain nonlinearities. The system stability has been proved rigorously via the Lyapunov method and Barbalat's lemma. Extensions to the cases with input saturation and dead zones have also been discussed. Extensive numerical simulations have been conducted to verify the performance and robustness of the proposed controller, in comparison to some existing methods.

Despite the dominance of unsupervised and self-supervised anomaly detection methods in the current satellite fault diagnosis domain, supervised anomaly detection offers a superior alternative for high-sensitivity detection and lightweight deployment requirements specific to subsystems or components, such as attitude control systems (ACSs). This article addresses the issues of over-design and insufficient accuracy in the CNN network design for satellite ACS fault diagnosis by introducing the modified particle swarm optimization-advanced convolution blocks-based CNN (MPSO-ACBCNN) method. First, we present the ACBCNN, a lightweight, flexible-layer CNN architecture. This architecture leverages advanced convolution blocks (ACBs), which incorporate numerous efficient design elements to enhance feature extraction capabilities within power spectral density (PSD) graphs of various fault samples, and employs classical dense connection methods to prevent the issue of gradient vanishing. Second, we devise the MPSO-ACBCNN algorithm to optimize the ACBCNN fault diagnosis architecture for specified ACS using MPSO. In MPSO-ACBCNN, several optimizations to the canonical PSO are implemented, including the fitness design that balances the tradeoff between total parameter quantity and the training effectiveness, and methods to ensure feasible solutions, etc. Finally, numerical experimental results demonstrate the effectiveness and superiority of MPSO-ACBCNN in fault diagnosis for ACS.

In this article, the λ -policy iteration ( λ -PI) method for the optimal control problem of discrete-time linear systems is reconsidered and restated from a novel aspect. First, the traditional λ -PI method is recalled, and some new properties of the traditional λ -PI are proposed. Based on these new properties, a modified λ -PI algorithm is introduced with its convergence proven. Compared with the existing results, the initial condition is further relaxed. The data-driven implementation is then constructed with a new matrix rank condition for verifying the feasibility of the proposed data-driven implementation. A simulation example verifies the effectiveness of the proposed method.

As a crucial energy storage for the spacecraft power system, lithium-ion batteries degradation mechanisms are complex and involved with external environmental perturbations. Hence, effective remaining useful life (RUL) prediction and model reliability assessment confronts considerable obstacles. This article develops a new RUL prediction method for spacecraft lithium-ion batteries, where a hybrid data preprocessing-based deep learning model is proposed. First, to improve the correlation between battery capacity and features, the empirically selected high-dimensional features are linearized by using the Box-Cox transformation and then denoised via the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. Second, the principal component analysis (PCA) algorithm is employed to perform feature dimensionality reduction, and the output of PCA is further processed by the sliding window technique. Third, a multiscale hierarchical attention bi-directional long short-term memory (MHA-BiLSTM) model is constructed to estimate the capacity in future cycles. Specifically, the MHA-BiLSTM model can predict the RUL of lithium-ion batteries by considering the correlation and significance of each cycle's information during the degradation process on different scales. Finally, the proposed method is validated based on multiple types of experiments under two lithium-ion battery datasets, demonstrating its superior performance in terms of feature extraction and multidimensional time series prediction.

Based on a complete parametric partial eigenstructure assignment (ESA) approach, a circulation design for ESA in a stabilizable linear system via state feedback is proposed. It is shown that, in those rounds where a real open-loop eigenvalue of order 1 is replaced, a type of very general indices can be globally minimized, which include those for general eigenvector configuration and gain magnitude minimization as special cases. When all the open-loop eigenvalues to be replaced are real and are of order 1, the proposed circulation design turns out to be extremely simple and efficient, and also possesses good numerical reliability since the globally optimal solution is established in a simple, direct, and explicit form, and the proposed circulation algorithms remove completely matrix inverse operations. An illustrative example demonstrates the simplicity and effect of the proposed approach.

This article first studies the problem of replacing one single real open-loop eigenvalue in a multivariable linear system by state feedback, simultaneously achieving minimization of the sensitivity of this assigned eigenvalue. Two types of sensitivity indices of the assigned closed-loop eigenvalue corresponding, respectively, to the cases of structured and unstructured parameter perturbations are considered. It is shown that for this problem simple and neat analytical globally optimal solutions exist. By using the derived globally optimal solutions repeatedly, this article second proposes a circulation design for eigenstructure assignment in a stabilizable linear system via state feedback with low closed-loop eigenvalue sensitivities. As a consequence, in those rounds of replacing a real open-loop eigenvalue of order 1, the sensitivity index of the assigned closed-loop eigenvalue can be globally minimized. In the case that all the open-loop eigenvalues to be replaced are real ones of order 1, the proposed circulation design not only turns out to be extremely simple and efficient, but also possesses good numerical reliability because it removes completely matrix inverse operations. Two illustrative examples demonstrate the simplicity and effect of the proposed approach.

This article establishes the linear static and dynamic time-varying event-triggered and self-triggered controllers with designable minimal interevent times (MIETs) to stabilize input constrained linear systems. We first design a static event-triggered control (ETC) algorithm, in which the control gain dependent on the solution to a parametric Lyapunov equation is time-varying and is only scheduled at a specified time decided by the static event-triggered mechanism (ETM). This can improve the control performance of the closed-loop system and save communication resources synchronously. Moreover, a dynamic ETC is designed to further increase the interevent times. Furthermore, in order to avoid the continuous monitoring of system status, the static and dynamic self-triggered control (STC) algorithms are also established. The Zeno phenomenon is avoided and the corresponding designable MIET is given in all established algorithms. Specifically, the designed control algorithms are extended to solve the corresponding semi-global stabilization problem. In some cases, the MIETs can be selected as an arbitrarily large bounded constant that has no relationship with system itself. Finally, applications to the spacecraft rendezvous control system verify the effectiveness of the designed algorithms.

This paper is devoted to trajectory tracking control for the second- and high-order strict-feedback systems (SFSs) with accurate parameter estimations. By skillfully fusing the techniques of concurrent learning (CL), adaptive command filtered backstepping (ACFB) and high-order fully-actuated (HOFA) system approach, a novel CL-based high-order ACFB (CL-HOACFB) controller is constructed. The typical feature of the proposed controller is that it directly utilizes the HOFA feature to design controller without turning the original second- and high-order strict-feedback systems into the first-order state-space approach to reduce backstepping steps, and circumvents the complexity arising due to repeatedly differentiating the virtual control. Remarkably, the proposed controller provides the ability to identify unknown parameters by only checking the linear independence of the recorded data, which largely relaxes the requirement of persistent excitation (PE) needed in the previous approaches. Theoretically, it is demonstrated that the tracking error can be adjusted to be as small as desired by tuning predetermined parameters. Finally, a benchmark application in the electromechanical system is given to illustrate the validity and potential of the proposed scheme.

The region of attraction of the Lyapunov asymptotic stability at the origin is defined to be a ball centered at the origin, which is clearly simply connected and also bounded in the local case. In this article, the concept of substability is proposed, which allows "gaps" and "holes" in the region of attraction of the Lyapunov exponential stability, and also allows the origin to be a boundary point of the region of attraction. The concept is meaningful and useful in many practical applications, but is particularly made so with the control of single- and multi-order subfully actuated systems. Specifically, the singular set of a sub-FAS is first defined, and a substabilizing controller is then designed such that the closed-loop system is a constant linear one with an arbitrarily assignable eigen-polynomial, but with its initial values restricted within a so-called region of exponential attraction (ROEA). Consequently, the substabilizing controller drives all the state trajectories starting from the ROEA exponentially to the origin. The introduced concept of substabilization is of great importance because, on the one side, it is often practically useful since the designed ROEA is often large enough for certain applications, while on the other side, Lyapunov asymptotically stabilizing controllers can be further easily established based on substabilization. Several examples are given to demonstrate the proposed theories.

A general time-varying nonlinear uncertain system with time-varying delays is proposed, which is composed of two subsystems: one is an uncertain fully actuated subsystem representing the controllable part in the system, the other is an isolated globally uniformly asymptotically (GUA) stable autonomous subsystem which represents the uncontrollable part in the system. Both the single-order fully actuated system (FAS) and the multiorder FAS representations of the controllable subsystem are introduced. The problem of robust stabilization of such a compound system with full-state feedback can be converted into an input-to-state GUA stabilization problem of the fully actuated subsystem, which is solved for both types of single- and multiorder FASs with very general assumptions on the uncertain perturbed functions. Under certain conditions, the solution reduces to that for robust stabilization with partial-state feedback, and naturally reduce to that for robust stabilization of FASs. Two illustrative examples demonstrate both the effect and the application procedure of the proposed robust stabilization approach.

This article considers the design of prescribed-time unknown input observers (PTUIOs) for linear systems, i.e., observers that estimate the state of a linear system with unknown inputs at a prescribed finite time. To this end, the generalized inverse is used first to transform a form amenable for observer design. Then, PTUIOs are designed by using the periodic delayed output so that their estimation errors converge to zero at a prescribed time. Both full-order and reduced-order PTUIOs are considered. For the reduced-order observer design, an alternative approach is also adopted, based on a transformation to a generalization of the normal form. In addition, the proposed PTUIOs are applied to estimate faults. Finally, two examples are given to illustrate the effectiveness of the proposed approaches.

This paper considers the global prescribed-time asymptotic tracking control problem for uncertain strict feedback nonlinear systems with time-varying parameters and an unknown control direction. A novel prescribed-time scaling function is constructed, and the frequently used basic lemma on the Nussbaum function is modified to adapt to the situation where the initial value of the control coefficient is zero. With the help of the nonlinear mapping technique, the congelation of variables method and the backstepping method, an adaptive tracking control method is presented, which exhibits several attractive features: (1) all the closed-loop signals are bounded; (2) for any initial condition, the tracking error can be confined within a prescribed zone in a prescribed time; and (3) the asymptotic convergence of the tracking error is ensured. The effectiveness of the proposed control method is demonstrated by using two simulation examples.

This paper studies the prescribed-time input-to-state stabilization problem of normal nonlinear systems. With the help of some key properties of a class of parametric Lyapunov equations, the prescribed-time input-to-state stabilization problem of normal nonlinear systems under matched uncertainties is studied. Some bounded time-varying controllers are proposed. It is shown that the closed-loop systems are prescribed-time input-to-state stable. Finally, the effectiveness of the established methods is illustrated by two physical systems.

This article addresses the state and fault estimation problems for a class of quantized Takagi–Sugeno (T-S) fuzzy systems with sensor and actuator faults. Two types of observer design methods are developed for two different network transmission environments. In the first case, the signal quantization effect is only considered on the channel between sensor and controller, and a sliding mode observer is designed, which can reconstruct the actuator faults. In the second case, the quantization effects are considered in both the sensor-controller side and the controller-actuator side, which is more difficult to be dealt with compared to the first case, and the sliding mode observer method cannot be effective now since the discontinuous term is difficult to be designed. A descriptor reduced-order observer is presented to solve this problem, where the jumping behavior that resulted from the signal quantization is considered in the observer, which is novel and effective to compensate the quantization effects. Finally, simulation examples of rigid-body satellite attitude control T-S fuzzy system are presented to verify the effectiveness of the proposed observer design approaches.

This article investigates the global stabilization problem of the circular orbit rendezvous system with actuator saturation and time-delay. By decomposing the linearized relative motion equations into a cascade of neutral stable linear systems, linear state feedback controllers are proposed in the presence of both actuator saturation and/or time-delay. The global stability of the closed-loop system is proved. Optimal feedback gain is also obtained in the delay-free case. Simulation results are given to show the effectiveness of the presented methods.

This article designs the static and dynamic event-triggered control (ETC) and self-triggered control (STC) algorithms to achieve the semiglobal stabilization of discrete-time systems with input constraints. First, a novel static ETC algorithm based on the discrete-time parametric Lyapunov equation (DPLE) is designed. In order to further increase the interevent times (IETs), the corresponding dynamic ETC is designed. Next, both static and dynamic STC, where the next control law updates depend on the previous triggered states, are proposed to avoid monitoring the measurement errors. The proposed algorithms are not only capable of reducing the number of transmissions significantly but also build a very simple and clear relationship between the only design parameter and the nontrivial IET (NIET). This allows us to change regularly IETs by adjusting the design parameter so that the nontriviality of static and dynamic ETC and STC is guaranteed and a tradeoff between the IETs and the control performance can be easily found. Specifically, by exploring the properties of DPLE, the designed algorithms avoid the complex relationship between the nontrivial condition and the system matrices. Finally, the designed static and dynamic ETC and STC algorithms are applied to the design of the spacecraft rendezvous control system and their effectiveness is verified by simulation results.

This article addresses the dynamics and control problem of integrated position and attitude stabilization via manipulator actuation for spacecraft in proximity operations subject to external disturbances. Following recursive modeling philosophy, kinematics, and dynamics are first formulated for multiple-manipulator actuated coupled position and attitude system of spacecraft in the presence of external disturbances, where two vector factorizations are proposed to ensure a compact and explicit dynamic formulation, holding the inherent skew-symmetric property of the coefficient matrices. In view of the unconspicuous cascaded-like system structure, a nonlinear control scheme is designed following backstepping philosophy to drive the joints of manipulators causing reactions to robustly stabilize the spacecraft position and attitude. Toward this end, a reference trajectory prescribing the spacecraft motion is predesigned by means of polynomial functions ensuring well-behaviored performance. Then, as a key component, a second-order dynamic filter is constructed making use of the dynamics structure to generate the command joint motion capable of manipulator actuation. The rigorous closed-loop stability and robust performance analyses are undertaken within the Lyapunov framework. Moreover, three types of self-collisions possibly happening in the motion of multiple manipulators are considered and handled by three collision detection methods, which facilitates a collision-free trajectory shaping during the aforementioned reference trajectory design phase. Finally, numerical simulations are given to demonstrate the effect and robustness of the proposed control scheme.

This article proposes static/dynamic event-triggered and self-triggered discrete gain scheduled control with a designable parametric minimal interevent time (MIET) to achieve semiglobal stabilization of linear systems with input constraints. First, a novel static event-triggered discrete gain scheduled control, which can improve the control performance and simultaneously save the communication resources, is proposed by utilizing the properties of the parametric Lyapunov equation (PLE). Moreover, the static self-triggered mechanism, in which the next control law updates based on the previous triggered states, is also designed to avoid the monitoring of all states. In order to further increase the interevent times (IETs), the corresponding dynamic event-triggered and self-triggered discrete gain scheduled control are designed, respectively. All the proposed algorithms can not only avoid the Zeno phenomenon but also provide a designable parametric MIET. This allows to easily find a tradeoff between the IETs and the control performance by adjusting the only design parameter. In addition, by exploiting the properties of the PLE, the designed algorithms avoid the complicated relationship between the MIET and the system matrices. In some cases, the MIET can totally avoid the relationship with the system itself and be designed as an arbitrarily large bounded constant. Finally, applications to the spacecraft rendezvous system show the effectiveness of the established algorithms.

This paper investigates a novel leader-following attitude control approach for spacecraft formation under the preassigned two-layer performance with consideration of unknown inertial parameters, external disturbance torque, and unmodeled uncertainty. First, two-layer prescribed performance is preselected for both the attitude angular and angular velocity tracking errors. Subsequently, a distributed two-layer performance controller is devised, which can guarantee that all the involved closed-loop signals are uniformly ultimately bounded. In order to tackle the defect of statically two-layer performance controller, learning-based control strategy is introduced to serve as an adaptive supplementary controller based on adaptive dynamic programming technique. This enhances the adaptiveness of the statically two-layer performance controller with respect to unexpected uncertainty dramatically, without any prior knowledge of the inertial information. Furthermore, by employing the robustly positively invariant theory, the input-to-state stability is rigorously proven under the designed learning-based distributed controller. Finally, two groups of simulation examples are organized to validate the feasibility and effectiveness of the proposed distributed control approach.

In this paper, an adaptive fuzzy control scheme is proposed for hybrid motion/force of trilateral teleoperation systems with a dual-master-single-slave configuration under stochastic time-varying delays in communication channels. Different from previous works on bilateral teleoperation systems, this paper addresses dual-master trilateral control of a single holonomic-constrained robotic manipulator, where the communication delays are modeled as multiple Markov chains, and the motion/force controls are investigated under consideration of unsymmetric stochastic time-varying delays and system dynamical uncertainties. Using partial feedback linearization, the whole trilateral teleoperation system, which consists of both master and slave manipulator dynamics, is transformed into three subsystems. By integrating Markov jump systems to handle random delays, adaptive fuzzy control strategies are developed for the nonlinear teleoperators with modeling uncertainties and external disturbances by using the approximation property of the fuzzy logic systems (FLSs). It is proven that the trilateral teleoperation system is stochastically stable in mean square under specific linear matrix inequality (LMI) conditions, and all the signals of the resulting closed-loop system are uniformly bounded. The proposed scheme is validated by extensive simulations.

A type of generalized proportional-integral-derivative observers is proposed for descriptor linear systems. Based on a general parametric solution to a type of generalized Sylvester matrix equations, a parametric design approach for such observers is established. The proposed approach provides parameterizations for all the observer gain matrices, gives the parametric expression for the corresponding left eigenvector matrix of the observer system matrix, realizes the elimination of impulsive behaviors, and guarantees the regularity of the observer system. The design method can offer all the degrees of design freedom, which can be utilized to achieve various desired system specifications and performances. In addition, a numerical example is employed to show the design procedure and illustrate the effect of the presented approach.

A parametric design approach for proportional-integral (PI) observers for continuous-time descriptor linear systems is proposed based on a complete general parametric solution to the generalized Sylvester matrix equation. The proposed approach provides complete parameterizations for all the observer gain matrices, gives the parametric expression for the corresponding finite left eigenvector matrix of the observer system matrix, realizes elimination of impulsive responses, and guarantees the regularity of the observer system. The design method offers all the degrees of design freedom, which can be utilized to achieve various desired system specifications and performances and, thus, has great potentials in applications. A numerical example is employed to show the design procedure and illustrate the effect of the proposed approach. Simulation results show a satisfactory tracking performance for descriptor linear systems.

A new parametric approach for robust fault detection in matrix second-order linear (MSOL) systems with unknown disturbances is directly proposed in matrix second-order framework. The residual is generated through utilizing a Luenberger function observer of MSOL systems. Based on a recently proposed eigenstructure assignment approach for MSOL systems, parameterizations of the coefficient matrices of the Luenberger function observer are presented. By combining the parameterizations of the observer eigenvector matrix and an established condition for disturbance decoupling in MSOL systems, the effect of the disturbance to the residual signal is decoupled. A simple algorithm is presented. An example shows the effect of the proposed approach.

This paper deals with the stochastic stabilizability and passive control for a class of linear time-delay systems with Markov jumping parameters and Brownian motions. The transition of the jumping parameters in systems is governed by a finite-state Markov process. A sufficient condition on stochastic stabilizability is established based on stability theory in stochastic differential equations. In terms of a set of coupled linear matrix inequalities, stochastic passive controllers are designed to stochastically stabilize the given systems with passive performance constraints. A numerical example demonstrates the effect of the proposed design approach.

In virtue of the modified bounded real lemma (BRL), multi-objective control synthesis based on parametric eigenstructure assignment in linear systems is considered. By applying the degrees of design freedom provided by eigenstructure assignment in linear systems, a state feedback controller is designed such that the closed-loop system satisfies H/sub /spl infin//-norm bound constraints on disturbance attenuation and pole constraints. The proposed design method is formulated in terms of linear matrix inequality (LMI) which can be easily solved by the MATLAB LMI toolbox. An example demonstrates the effect of the proposed design approach.

By using generalized Riccati equations, a method of designing observer-based feedback controllers against sensor failures for detectable descriptor linear systems is presented. A parameterization of the observer-based fault-tolerant feedback controllers is provided. The controller designed by the proposed method guarantees that the closed-loop system is regular, impulse-free and stable in the normal condition as well as in the event of sensor failures. A numerical example shows the effect of the proposed method.

The design of robust pole assignment via proportional-plus-derivative (P-D) feedback is investigated for a class of second-order dynamic systems. Based on P-D feedback parametric eigenstructure assignment for second-order dynamic systems, parametric expressions of the closed-loop eigenvalue sensitivities to the perturbed elements in the open-loop system matrices are derived and an effective algorithm for robust pole assignment in second-order dynamic systems via P-D feedback is proposed. This method utilizes directly the system data of the original second-order dynamic system, and thus is convenient to use in applications. A three lumped mass-spring dashpot system example shows the effect of this proposed algorithm.

A class of discrete-time Kalman filtering problem for the descriptor time-varying systems with current and delayed measurements is considered. Using the known maximum likelihood (ML) estimation results and the method of measurements reorganization, the optimal Kalman filter and corresponding Riccati equations for descriptor systems involving current and delayed measurements are derived. Our solution does not require system augmentation or system transformation, and the estimator is given in terms of two Riccati equations of the same order as that of the system state. A simple algorithm is presented for the problem.

Particle filters are for the first time introduced into the passive position location estimation. It is shown that the standard particle filter in this case suffers from "sample impoverishment" seriously. Adaptive resampling and regularization operations are suggested to solve the problem. Numerical simulations show that under large initialization errors, the particle filtering significantly improves the location estimation performance compared with the conventional estimation methods.