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Robust regulation is predictively investigated in time-delay models including uncertainties, non-zero reference, and disturbance. Hence, to handle the complexities induced because of the time-delays, disturbances, and uncertainties, enhancing the transient performance would be a main control goal. To this purpose, the required transient and steady-state specifications are achieved through an integral control structure with adjustable gains. The constraints on the signals and aims of the control algorithm are also described by some linear matrix inequalities. Guaranteeing the stability, a minimisation procedure is found to specify the predictive controller. Several simulations are performed to establish the effectiveness of the introduced plan in comparison with the similar strategy. [ABSTRACT FROM AUTHOR]

This research paper focuses on addressing the challenge of infinite-time linear quadratic tracking control (LQT) for linear systems with parametric uncertainty. Traditional solutions to the LQT problem often involve using a discount factor to prevent the cost function from growing unbounded over time. However, this approach can introduce instability in the closed-loop system. To overcome this issue, this paper proposes an alternative approach using an undiscounted cost function that ensures the asymptotic stability of the uncertain closed-loop system. To design a control scheme without requiring precise knowledge of the system dynamics, reinforcement learning (RL) algorithms are employed. However, for systems with uncertain parameters that may lead to instability, the convergence of RL algorithms to a stabilising solution is not guaranteed. To address this limitation, a robust optimal control structure is developed using on-policy and off-policy reinforcement learning algorithms, resulting in a model-free controller. The effectiveness of the proposed robust optimal controller is validated through comparative simulations on an uncertain model of a DC–DC buck converter connected to a constant power load. These simulations demonstrate the advantages and benefits of the robust optimal controller in handling parametric uncertainty and ensuring stability in the control system. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic positioning control scheme for offshore drilling platforms using the equivalent-input-disturbance (EID) method with separated observers. The scheme incorporates observer-based disturbance compensation, wave-filtering state estimation, input transformation, and state feedback control to achieve robust control performance. The main contributions include the design of an EID scheme with separated observers, enabling enhanced disturbance-rejection performance, incorporating a notch filter in the EID estimator to reduce energy loss, and giving an observer tuning scheme to achieve a balanced performance between disturbance rejection and wave filtering. Simulation results demonstrate the effectiveness of the proposed scheme. This work provides valuable insights into designing stable and efficient dynamic positioning systems for offshore drilling platforms. [ABSTRACT FROM AUTHOR]

The problem of robust tracking and model following is reconsidered for a class of uncertain nonlinear systems with some uncertainties and dead-zone input constraints. In this paper, the system uncertainties are regarded as some nonlinear functions which are not required to must satisfy the matching conditions. That is, being different from traditional robust control theory, the matched structures of uncertainties are sufficient, but not necessary, for being able always to design some types of robust tracking control schemes. In addition, the external disturbances of dynamical systems are assumed to be any bounded functions which is not also required to satisfy the matching condition. By making use of the integral inequality, instead of Lyapunov function, a class of robust tracking control schemes is constructed to guarantee the uniform exponential boundedness of the model tracking errors. Moreover, the upper bounds of nonlinear uncertainties are not required to be known, and it is also unnecessary to know or estimate the characteristic parameters of dead-zone functions. Therefore, the resulting robust tracking control schemes have a simple structure. Actually, the resulting robust tracking control scheme is a linear feedback control one with a self-tuning control gain. Finally, an illustrative numerical example is provided to demonstrate the validity of the presented theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, an output feedback controller using iterative learning algorithm is proposed for stabilising a class of uncertain nonlinear single-input single-output (SISO) systems with unknown bounded disturbances. By dividing the time interval into equal iteration periods, iterative learning algorithm is integrated into output feedback control and is carried out under alignment condition. Using the output signal, control input and estimates of uncertain parameters, adaptive state observers are established to generate state estimates which will be employed to design the control law. Based on the backstepping technique, the output feedback controller is developed consisting of the ILC part and robust control part. Different from the traditional ILC update laws of uncertain parameters with constant gains, a novel type of parameter update law is developed where the ILC gain is variable and determined by the real-time state estimates such that system stability can be guaranteed. By virtue of the composite energy function, it is proved that all the signals of the closed-loop system are bounded and the output will uniformly converge to zero along the iteration axis, which indicates the stabilisation of the output signal over the time domain. Simulation verification is conducted to apply the proposed controller to a one-degree-of-freedom suspension system to validate the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

In this paper, we develop an improved data-based integral policy iteration method to address the robust control issue for nonlinear systems. Combining multi-step neural networks with pre-training, the condition of selecting the initial admissible control policy is relaxed even though the information of system dynamics is unknown. Based on adaptive critic learning, the established algorithm is conducted to attain the optimal controller. Then, the robust control strategy is derived by adding the feedback gain. Furthermore, the computing error is considered during the process of implementing matrix inverse operation. Finally, two examples are presented to verify the effectiveness of the constructed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced and robust tracking control method for the class of uncertain and disturbed affine nonlinear systems. The approach is based on predictive sliding mode control (PSMC) to combine the advantages of sliding mode control (SMC) with those of model predictive control (MPC). The resulting controller offers numerous benefits, including high robustness, quick transient response, finite-time convergence, and robustness against uncertainties and external disturbances. However, a significant limitation of the proposed PSMC is its reliance on prior knowledge of the disturbance and uncertainty bounds. To address this drawback, a sliding mode observer (SMO) is synthesised to identify uncertainties and disturbances. The proposed controller successfully eliminates the chattering effect without compromising robustness and precision. The Lyapunov theory is used to prove the stability of the closed-loop system. The effectiveness of the proposed approach is validated through simulations on a well-known benchmark. [ABSTRACT FROM AUTHOR]

This paper aims to establish a new event-triggered model predictive control (MPC) strategy for perturbed nonlinear systems working in a networked environment, which can effectively save the computation and communication resources while ensuring control performance. The core of the framework is a new event-triggered mechanism, which is built by combining the differential and integral (D-I) information of the error between the optimal state and the actual state in a pre-specified time horizon. Based on such a triggering mechanism, a D-I based event-triggered robust MPC algorithm is proposed to stabilise the system and reduce the computation/communication resource consumption. In addition, sufficient conditions to ensure the feasibility and stability of the proposed MPC algorithm and avoid Zeno behaviour are obtained through rigorous theoretical analysis. Finally, the proposed control framework is implemented in the nonlinear cart-damper-spring and autonomous underwater vehicle systems to verify its effectiveness. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative adaptive nonsingular Second-Order Terminal Sliding Mode (SOTSM) control strategy specifically designed to stabilise disturbed nonlinear Cyber-Physical Systems (CPSs) within a finite timeframe. Unlike the conventional methods, our approach incorporates a novel nonlinear sliding surface that eliminates the need for a reaching step, significantly enhancing the system's robustness. Novel real-time adaptive control laws are developed to cope with external perturbations, time-varying delays, and cyber-attack on actuators without requiring the identification of their upper bounds. The proposed method ensures robust performance for time-varying delayed and disturbed nonlinear CPSs, even in the face of strong cyber-attacks targeting the actuators. Moreover, the proposed method has distinct advantages: it delivers rapid response times, enhanced flexibility, exceptional accuracy, smooth and robust control devoid of transient fluctuations or chattering, and ensures finite-time convergence. Simulation results comprehensively demonstrate the superior efficiency and success of our proposed technique compared to traditional methods such as integral Sliding Mode Control (SMC) and State-Feedback Control (SFC) schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel ensemble learning-based adaptive stochastic model predictive control (SMPC) algorithm for constrained linear systems with unknown nonlinear terms and random disturbances. The ensemble network combining a feedforward neural network and a Bayesian network is used to offline learn the nonlinear dynamics and disturbance distribution parameters. Then, the mixed-tube scheme is designed to cope with input constraints and state chance constraints while decreasing computational demands and conservativeness. The reliability of the stochastic tube is guaranteed using the Hoeffding inequality-based verification mechanism, which results in a chance constraint with double probabilities. The feasibility and exponential stability of the SMPC are rigorously proven. A numerical example verifies the merits of the proposed algorithm in terms of the control performance and the feasible domain. [ABSTRACT FROM AUTHOR]

In this paper, a novel fractional-order adaptive controller is presented for a class of nonlinear systems with unknown dynamics. The dynamics of the system is considered to be fully unknown. The multi-layer perceptron (MLP) neural network using restricted Boltzmann machine (RBMs) is employed for online dynamic identification. A deep learning method on the basis of contrastive divergence (CD) algorithm combined with the extended Kalman filter (EKF) is proposed for online optimisation. The proposed controller has two parts. The first part is a simple error feedback controller and the second one is the suggested DT2-FLS. The parameters of DT2-FLS are tuned such that a cost function of tracking error to be minimised and the closed-loop system to be stable. For the best knowledge of the authors, for the first time the tuning rules for the membership function and rule parameters of DT2-FLS are derived by error feedback learning method. The closed-loop stability is demonstrated with Lyapunov method and the well performance of the schemed controller is shown by applying on the induction motor and brushless DC motors. In addition to unknown dynamics, some disturbances are also considered such as abruptly changes in load torque and time-varying rotor resistance. Furthermore, the performance of the suggested scheme is compared with some popular controllers and FLSs. [ABSTRACT FROM AUTHOR]

In this paper we propose a robust output feedback control scheme for constant reference tracking in uncertain linear systems subject to state and control constraints. First, we establish conditions under which a polyhedral set is Output Feedback Controlled Invariant with respect to a linear system subject to polytopic uncertainties, i.e. a set guaranteeing robust constraint satisfaction via output feedback. Then, we build a dynamic output feedback controller for the uncertain model based on set-membership observers, allowing the reduction of the set of possible states consistent with the measurements. Finally, to reduce the tracking error, we propose a model update procedure that adjusts the nominal model used for tracking to the output measurements. Numerical experiments illustrate the ability of the proposed controller to achieve reference tracking with reduced offset for the uncertain systems under consideration. To the best of our knowledge, it is one of the first works that addresses constant reference tracking in uncertain linear systems under constraints with output feedback. [ABSTRACT FROM AUTHOR]

This study presents a model-free robust input-output decoupling control with Nonlinear-Dynamic-Coupling Inversion/Inverter (NDCI) in a U-control framework. Regarding the decoupling, an input/output (I/O) coupling matrix function is proposed to derive two decouplers (U-decoupler/functional inversion and D-decoupler/static matrix inversion). A general existing theorem is proved for model-free sliding mode control (MFSMC) to lay the foundation for the NDCI, which takes the Lyapunov differential inequality for its derivative rather than the semi-define Lyapunov derivative. Accordingly, a multi-input and multi-output (MIMO) model-free decoupling U-control (MFDUC) platform is established to integrate the functionalities into a double closed-loop system framework. To validate the functionalities and configurations, this study presents transparent and comparative simulated bench tests, which also could be treated as user guidance for further study and ad hoc applications. [ABSTRACT FROM AUTHOR]

The complex v-plane is an emerging design domain for fractional order control system design. Recently, several works demonstrated the advantages of tuning FOPID controllers in v-plane. These approaches essentially perform the minimum angle pole placement to a target angle within the stability region of the v-plane and facilitate fractional order control system design tasks because of inherently guaranteed stabilisation of fractional order transfer functions. Accordingly, the optimal FOPID controller tuning problem can be simplified to placement of minimum angle system pole to a target point within the stability region of the v-plane. After reviewing previous v-domain design works, authors investigate prominent target points that can result in improved FOPID control performance for the v-domain design task. The consideration of target points in polar coordinates can provide two design parameters (angle and magnitude), which can convey essential system knowledge associated with the stability and control performance of FOPID control systems. In this perspective, effects of minimum angle pole positions on control performance indices are investigated in detail, and some prominent target points to manage FOPID design in v-domain have been reported. The v-domain design examples are illustrated to reveal the effects of the sampled pole positions on the robust control performance. [ABSTRACT FROM AUTHOR]

Chemical reactors, heat exchangers, level-loops, and distillation columns are commonly encountered in the chemical process industry, and these are having a significant role in the functioning of any chemical process. For the smooth and efficient functioning of any such process, a robust control law design is an important work to be considered. In this paper, a modified 2DOF robust PID control law has been developed to ensure satisfactory performance in both the step and ramp type references and disturbances. Controller parameters are derived with Maximum sensitivity, Gain, and Phase margin specifications. Case studies with different chemical processes like CSTR, distillation column, heat exchangers, and boiler drum level-loop are included for both the nominal and perturbed cases. Lastly, a performance comparison of ISE (Integral of square error), ITAE (Integral time absolute error), IAE (Integral absolute error), and Total variation (TV) of the control signal is also included. [ABSTRACT FROM AUTHOR]

This paper addresses a non-fragile robust model predictive control design for a class of continuous-time uncertain systems with multiple state-delay and constrained control signals. The parameters of the system and the control gain are assumed to have perturbation in the additive form. The Lyapunov–Krasovskii functional approach is employed to derive sufficient conditions for determining a non-fragile robust state-feedback controller for all admissible uncertainties by minimising the upper bound of the defined quadratic cost function with respect to some linear matrix inequalities (LMIs). An additional inequality condition is imposed to address the constraint associated with the control signals. Numerical simulations are provided to assess the performance of the proposed controller. [ABSTRACT FROM AUTHOR]

Diffusion processes, as fundamental mechanisms for particle movement in systems with different concentrations, are used to describe many real-world physical, chemical, biological, engineering, economic and social phenomena. A diffusion process can be modelled via a fractional-order transfer function with time-delay, where its parameters may be affected by circumstance. Hereupon, this study proposes a pioneer robustness indicator to achieve the phase margin invariance regardless of concurrent uncertainty on different parameters of a diffusion process. Afterwards, an analytical procedure is suggested to tune a Fractional-Order Proportional-Integral-Derivative (FO-PID) controller for a diffusion process, to favourably regulate the values of gain crossover frequency and phase margin, such that the proposed robustness criterion is met. Moreover, the solvability of the problem is analytically investigated. Finally, a numerical simulation on robust temperature control during magnetic local hyperthermia, i.e. a common method to treat cancerous tumours, is presented to validate the efficiency of the paper achievements. [ABSTRACT FROM AUTHOR]

In this article, the issue of finite-time boundedness is investigated for a class of uncertain periodic piecewise systems subject to time-varying input/state delays and external disturbances. The main aim of this article is to design an H ∞ observer-based control protocol for guaranteeing the resulting closed-loop system to be finite-time bounded. The state dynamics of the system are not always directly measurable, so that a system states are reconstructed by constructing a periodic piecewise observer system to get the required result. Subsequently, the Lyapunov–Krasovskii functional by the decomposition of periodic positive-definite matrices is built and in conjunction with convex optimisation technique and extended Wirtinger's integral inequality, the sufficient conditions are derived to assure the finite-time boundedness of the closed-loop system. In particular, the corresponding periodic piecewise time-varying controller and observer gain matrices can be obtained as solutions to a set of derived sufficient linear matrix inequality-based conditions. Finally, the efficacy of the proposed control design is examined by utilizing two numerical examples with simulation results including a vibration system model. [ABSTRACT FROM AUTHOR]

This paper presents the design of a robust Restricted-Model-Based control providing tools for both stability and performance analysis of a class of Single-Input-Single-Output (SISO) Takagi–Sugeno (T–S) systems. The proposed method is evaluated in terms of induced L 2 -gain (or so-called H ∞ norm) to be robust to disturbances, sensor noises and uncertainties on the premise variables. Unlike the common approaches reported in the literature that consider exact premise variables, this work deals with the problem of unmeasured premises. The main results of this paper illustrate that stability and performance conditions can be evaluated by examining the feasibility of parametrised sets of linear matrix inequalities (LMIs). The proof of stability is based on the non-linear sector approach of the closed loop under Lyapunov conditions, L 2 norm and system transformations. The result is a control structure with only one parameter tuned via simple conditions. The performance and applicability of the proposed approach are illustrated through numerical simulations of an academic example. [ABSTRACT FROM AUTHOR]

In this paper, a two-loop Model Predictive Controller (MPC) with an improved tube is proposed for industrial application with bounded uncertainties subject to input and state constraints. This scheme attempts to remove some existing obstacles against exploiting MPC in industrial applications, such as (i) risk and cost of a new controller replacement, (ii) difficulties of attaining a precise open-loop model of an industrial system and (iii) high computational burden of MPC methods. To this end, tube conservatism and calculation burden are reduced using the transient response of the error dynamics. Thus the feasible region of the MPC is enlarged and its computation time is reduced. To reduce modelling difficulties, the investigated approach does not require the open-loop model dynamics of the system and utilises the closed-loop model instead. On the other hand, it allows the existing inner-loop controller to remain unchanged without any manipulations, which results in eliminating a new controller replacement risk and cost. Additionally, for the proposed control method, robust stability and recursive feasibility are guaranteed without terminal gradients. Finally, an illustrative example is carried out in the simulation results to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this study, an optimal and robust fractional order PID (FOPID) controller design approach is suggested for Smith predictor based FOPID (SP-FOPID) control system design. This new design approach considers continued fraction expansion (CFE) based approximate models in the optimal design task of FOPID controllers, and this approach preserves the design optimality in control applications. For this purpose, an inverse controller loop shaping design methodology is performed in order to approximate the frequency response of a Bode's ideal loop reference model, and robust performance CFE based FOPID controller models are obtained by solving a multi-objective optimisation problem via a genetic algorithm. Thus, the suggested algorithm can deal with several controller realisation concerns in the design task of FOPID controllers and overcome real-world controller performance issues related with model approximation errors and signal saturation boundaries of electronic hardware. Illustrative design examples demonstrate that the suggested design scheme can preserve design optimality and improve practical control performance in practice. [ABSTRACT FROM AUTHOR]

In various applications in the field of control engineering, the estimation of the state variables of dynamic systems in the presence of unknown inputs plays an important role. Existing methods require the so-called observer matching condition to be satisfied, rely on the boundedness e variables or exhibit an increased observer order of at least twice the plant order. In this article, a novel observer normal form for strongly observable linear time-invariant multivariable systems is proposed. In contrast to classical normal forms, the proposed approach also takes the unknown inputs into account. The proposed observer normal form allows for the straightforward construction of a higher-order sliding mode observer, which ensures global convergence of the estimation error within finite time even in the presence of unknown bounded inputs. Its application is not restricted to systems which satisfy the aforementioned limitations of already existing unknown input observers. The proposed approach can be exploited for the reconstruction of unknown inputs with bounded derivative and robust state-feedback control, which is shown by means of a tutorial example. Numerical simulations confirm the effectiveness of the presented work. [ABSTRACT FROM AUTHOR]

This note is concerned with conditions on a set of non-singular matrices A i ∈ R n × n , i = 1 , 2 , ... , r , so that any convex combination of these matrices is also non-singular. The first part of the note points out that Theorem 2.3 in a previous paper [Beteto et al. (2021). Less conservative conditions for robust LQR-state derivative controller design: An LMI approach. International Journal of Systems Science] provides only necessary conditions, which are not sufficient in the general case. In the second part, some stability results based on Linear Matrix Inequalities (LMIs) for a class of fractional order systems are used to establish new sufficient conditions. Numerical examples are presented for illustration. The results suggest that the new LMI conditions may be less conservative compared to a test proposed in the literature on P-matrices, and also to a positive-definiteness test based on matrix cross-products. [ABSTRACT FROM AUTHOR]

In this paper, a data-based neural policy learning method is established to solve the robust tracking control problem of a class of continuous-time systems which have two kinds of uncertainties at the same time. First, the robust trajectory tracking is achieved by controlling the tracking error to zero. The specific implementation strategy is to construct an augmented system including the tracking error and then transform the robust tracking control problem into an optimal control problem by selecting a suitable cost function. Then, a neural network identifier is built to reconstruct the unknown dynamics and a policy iteration algorithm is adopted by using a critic neural network. In this way, the Hamilton–Jacobi–Bellman equation can be solved. Through this learning algorithm, the approximate optimal control policy is obtained and the solution of the robust tracking control problem can be derived. Finally, two simulation examples are proposed to verify the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper studies the robust static output feedback control of discrete-time Markov jump systems considering that the mode of operation cannot be directly measured. It is assumed that the only available information concerning the main jump process comes from a detector, so that the jump and detector processes can be modelled as a hidden Markov model. Initially, it is considered that the system's dynamic matrix is subject to parametric uncertainties. By assuming a full row rank condition for the output matrix and employing a clusterisation technique for the state space of the hidden Markov model, it is derived a set of linear matrix inequalities (LMIs) that guarantees robust stability in the mean-square sense of the closed-loop system, as well as a bound on its H 2 norm. It is also shown that this design technique is linked to the bounded real lemma, so that a new set of LMIs conditions can be obtained for the 'pure' H ∞ control problem, which can be extended to cover the robust polytopic case. The paper is concluded with an illustrative example. [ABSTRACT FROM AUTHOR]

This study proposes a double sliding mode control-augmented U-control ( U DSM -control) method for a class of single-input single-output nonlinear systems with internal uncertain parameters, model mismatching and external system noise/disturbance to improve robustness in nonlinear dynamic inversion in the U-control system design. For the configuration, the U DSM -control system takes up (1) a double sliding mode dynamic inverter to cancel nonlinearities and dynamics of the plant, (2) a linear invariant controller, the other dynamic inverter of the specified whole desired system performance, so that the whole system dynamic inversion is split into two designs in double feedback loops. For using the framework, this study analyses the associated properties on (1) global stability, (2) double sliding from a switching control driving the states to a sliding band and an equivalent control driving the states to a sliding line and (3) robustness against uncertainties/disturbances and a potential data-driven prototype. To validate the developed control system, it selects bench test examples for simulation studies using Matlab/Simulink, which demonstrates the U DSM -control in terms of accuracy, tracking, and robustness. The tests also present a step-by-step design procedure for potential applications. [ABSTRACT FROM AUTHOR]

Input saturation is very important for the analysis and design of control system. Ignoring the input saturation will have an adverse effect on the control system, and even cause the control system to be unstable. In practice, almost all the control systems are affected by input saturation, involving switched systems, multi-agent systems, robot systems and spacecraft rendezvous systems, etc. In the past few decades, a large number of research results have been obtained on input saturation. This paper attempts to give an overview through a bibliometric analysis based on the publications related to input saturation which were published from 2010 to 2020 and retrieved from the Science Citation Index Expanded database. We give a detailed discussion based on countries, institutions, research areas, journals, authors and author keywords. The main results show that Automatica takes the leading position among the top 10 academic journals, followed by International Journal of Robust and Nonlinear Control and IEEE Transactions on Automatic Control. China ranks a leading position in this field, followed by the United States and France. Harbin Institute of Technology has the topmost total articles, citations and h-index. The future research works are highlighted and we summarise the conclusion in end. [ABSTRACT FROM AUTHOR]

In this article, a novel decentralised event-triggered control scheme is developed for the robust stabilisation of interconnected systems. The existing literature about decentralised robust control by adaptive critic designs (ACDs) is mostly time-triggered which requires extensive communication and computation. To extend the ACD framework and save the communication and computational resources, the event-triggered decentralised control using ACDs is investigated. First, we utilise a series of auxiliary subsystems to restructure the interconnected systems in a distributed way, so as to transform the robust control problem into the corresponding optimal control problem. Then, adaptive critic designs with event-triggering mechanism are investigated to solve the Hamilton–Jacobi–Bellman equations via local policy iteration algorithm. The single-critic networks are employed to achieve the approximation of optimal value functions. The Lyapunov stability proof ensures the asymptotic stability of the whole system and the uniform ultimate boundedness of the critic neural networks. Finally, an interconnected plant is employed to demonstrate the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust fixed-time control approach for the trajectory tracking control of a fully actuated marine surface vessel (MSV) subject to system uncertainties and external disturbances. First, a nominal fixed-time controller is originally designed based on the bi-limit homogeneous method. The nominal fixed-time controller can guarantee the position and velocity tracking errors converge to zero in fixed time in the absence of lumped disturbance. Then, a new type of fixed-time disturbance observer is introduced to estimate the lumped disturbance in fixed time. Finally, a robust fixed-time controller is developed by integrating the nominal fixed-time controller with the fixed-time disturbance observer. The robust fixed-time controller can guarantee the position and velocity tracking errors converge to zero in fixed time even in the presence of lumped disturbance. Benefiting from the feedforward disturbance compensation, the robust fixed-time controller has the strong robustness and excellent disturbance attenuation capability. Numerical simulations and comparisons demonstrate the effectiveness and advantages of the proposed control approach. [ABSTRACT FROM AUTHOR]

This work develops a method to perform surface treatment tasks using a bimanual robotic system, i.e. two robot arms cooperatively performing the task. In particular, one robot arm holds the workpiece while the other robot arm has the treatment tool attached to its end-effector. Moreover, the human user teleoperates all the six coordinates of the former robot arm and two coordinates of the latter robot arm, i.e. the teleoperator can move the treatment tool on the plane given by the workpiece surface. Furthermore, a force sensor attached to the treatment tool is used to automatically attain the desired pressure between the tool and the workpiece and to automatically keep the tool orientation orthogonal to the workpiece surface. In addition, to assist the human user during the teleoperation, several constraints are defined for both robot arms in order to avoid exceeding the allowed workspace, e.g. to avoid collisions with other objects in the environment. The theory used in this work to develop the bimanual robot control relies on sliding mode control and task prioritisation. Finally, the feasibility and effectiveness of the method are shown through experimental results using two robot arms. [ABSTRACT FROM AUTHOR]

The problem of decentralised robust tracking and model following is investigated for a class of uncertain large-scale interconnected systems with both any time-varying delays and any dead-zone input constraints. By combining an adaptive approach with a multiply integral inequality proposed recently in the control literature, a new design method is developed so that (i) the designed decentralised robust tracking control schemes are direct, i.e. not the complicated ones composed of two parts, which can overwhelm the dead-zone input constraints; (ii) it is unnecessary to know any information on the characteristic parameters of dead-zone functions and the time-varying delays, which makes the decentralised robust tracking control rather simple. It is also proved that the local tracking error between the output of each actual subsystem with delays and input constraints and the dynamical signals of the corresponding local reference model is uniformly exponentially bounded. Finally, a numerical example is also given to describe the design procedure and the simulations of this numerical example are implemented to demonstrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the input-to-state stability (ISS) control problem is studied for cyber-physical systems (CPSs) in the presence of asynchronous denial-of-service (DoS) attacks. To enhance the exponential ISS, an intelligent packet-based control method with buffering is proposed by introducing the acknowledgement signal (ACK), in which the trial transmission attempts of the control packet are intelligently determined by the situation (successful or failed) of its last sampling transmission instant after identifying the unnecessary transmission points and the necessary transmission points. Then, inspired by the packet-based control technology with buffering, the sufficient condition for attempting transmission of the control packet is given, in which the exponential ISS with maximum robustness index can be preserved. Compared with the existing method with a fixed transmission rate for the control packet, it is shown that the considered framework improves the exponential ISS performance with equal or less communication resource costs. [ABSTRACT FROM AUTHOR]

The problem of robust stabilisation for a class of uncertain dynamical systems with multiple delayed state perturbations is considered. In this paper, the upper bound of the nonlinearity and uncertainty, including delayed states, is assumed to be a linear function of some parameters which are still assumed to be unknown and an improved adaptation law with σ − modification is proposed to estimate these unknown gains. Moreover, by making use of the updated values of these unknown bounds, we propose a memoryless state feedback controller for such a class of uncertain time-delay systems. Based on Lyapunov stability theory and Lyapunov-Krasovskii functional, it is shown that the closed-loop dynamical system resulting from the proposed adaptive robust control schemes in the presence of multiple delayed state perturbations is globally uniformly practically exponentially stable. Finally, a numerical example is given to demonstrate the validity of the results. [ABSTRACT FROM AUTHOR]

In this study, a discrete fractional order PID controller for nonlinear systems is proposed. U-model is firstly employed to transform nonlinear systems into a linear-like model and model complexity is then degraded and easier for controller design. Then, a new discrete fractional order PID control law in difference equation is presented which takes advantage of two more adjustable parameters. Finally, robust stability of the control law is analysed. Both deterministic and stochastic examples are provided to illustrate the effectiveness and superiority of the proposed controller. [ABSTRACT FROM AUTHOR]

In this paper, we consider the specified-time platoon control for the networked vehicles modelled by the third-order dynamics with uncertain external bounded disturbance. The vehicles are subjected to disturbance and parameter uncertainties. To ensure the platoon can converge within a predefined time, a novel disturbance observer with a time-varying dynamic gain is proposed. By the time transformation approach, the disturbance observer is proved to be uniformly ultimate bounded (UUB) stable in a specified time. The time-varying dynamic gain is also applied to design the novel specified-time controller for the vehicle platoon and rigorous proof is presented based on Lyapunov stability. Under the proposed disturbance observer and controller, the vehicle platoon can be stabilised within the predefined time without depending on any system parameters and initial conditions. Finally, some comparison simulation results are given to verify the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

This paper addresses the trajectory tracking control problem of a planar vertical take-off and landing (PVTOL) aircraft in the presence of position constraints and external disturbances. A novel robust constrained control approach is proposed by incorporating the barrier Lyapunov function (BLF) and nonlinear damping items within the framework of dynamic surface control design. The dynamic surface control is used to avoid the 'explosion of terms' problem of the traditional backstepping control. The tan-type BLF is utilised to preserve the position tracking errors always within the predefined position constraints. The nonlinear damping items are applied to handle the effect of external disturbances. The uniform ultimate boundedness of the resulting closed-loop system is rigorously proved through the Lyapunov's direct method. A distinct feature of the proposed control approach is that it is a general methodology for the trajectory tracking control of PVTOL aircraft with or without position constraints. Both theoretical analysis and numerical simulations illustrate the effectiveness and benefits of the proposed control approach. [ABSTRACT FROM AUTHOR]

This study proposes less conservative conditions for robust linear quadratic regulator controllers using state-derivative feedback (SDF). The algebraic Ricatti equation was formulated using the SDF, and its solution was obtained by linear matrix inequalities. SDF was chosen owing to the presence of accelerometers as sensors. Since accelerometers are the main sensors in mechanical systems, the proposed technique may be used to control/attenuate their vibrations/oscillations. Moreover, to formulate the less conservative conditions, some methods in the specialised literature were used, such as, for example, slack variables by Finler's Lemma. The paper also offers necessary and sufficient conditions for an arbitrary convex combination of square real matrices A 1 , A 2 , ... , A r to be a nonsingular matrix, and thus an invertible one: A 1 must be nonsingular and all the real eigenvalues of A 1 − 1 A 2 , A 1 − 1 A 3 , ... , A 1 − 1 A r must be positive. This result is important in the formulation of the proposed less conservative conditions since it was assumed that a given convex combination is nonsingular. A feasibility analysis demonstrates that the proposed conditions reduce the conservatism. Thereby, it is possible to stabilise a higher number of systems and to reduce the guaranteed cost. Furthermore, a practical implementation illustrated the application of the proposed conditions. [ABSTRACT FROM AUTHOR]

This brief addresses a distributed adaptive asymptotically synchronous tracking problem based on guaranteed connectivity for networked uncertain nonholonomic mobile robots (NMRs) with actuator failures and unknown control directions. First, a radial basis function (RBF) neural network is used to approximate the unknown nonlinear functions, and a distributed nonlinear error surface is introduced to achieve synchronous tracking between NMRs and maintain the initial connectivity patterns. Then, a conditional inequality that allows multiple piecewise Nussbaum functions to achieve robust control is proposed to solve the problem of unknown actuator failures and unknown control directions. Moreover, the proposed protocol ensures that all signals in the closed-loop system are globally bounded and the tracking errors converge asymptotically to zero. Finally, a simulation example verifies the effectiveness of the proposed adaptive laws. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive robust boundary controller is designed for an unstable heat equation with external disturbance flowing into the control end. The disturbance and uncertainty effect is cancelled out in the closed-loop system by using on-line approximation of disturbance upper-bound provided as an output of adaptive robust update law. The asymptotic stability of the controlled system in the presence of unknown disturbance and/or parametric uncertainties is proved utilising the Lyapunov theorem. Unlike previous researches, the control implementation does not require prior knowledge of unknown disturbance. Moreover, the controller does not require measuring the system states or estimating the system states by an observer. The adaptive robust controller can be regarded as a combination of the best qualities of the adaptive controller and the robust controller with no prior knowledge of system uncertainty, and asymptotic tracking error performance. Numerical simulations and comparisons are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A type of high-order fully actuated (HOFA) systems with both nonlinear uncertainties and time-varying unknown parameters is considered, and a direct approach for the designs of robust adaptive stabilising controllers and robust adaptive tracking controllers is proposed based on the Lyapunov stability theory. The established controller is composed of three parts, the basic part cancels the known nonlinearities in the system and simultaneously assigns the linear dominant term in the closed-loop system, the robustness part overcomes the effects of the nonlinear uncertainties in the system, and the adaptation part adjusts online the controller to suit the effect of the unknown time-varying parameter vector. The proposed controller guarantees that the tracking error of the state to a given signal and the estimation error of the parameter vector finally converge globally into a bounded ellipsoid. Particularly, in the case that the unknown parameter vector is constant, an adaptive scheme that enables global asymptotical tracking is presented. An example demonstrates the effect of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a computationally efficient robust predictive control method is proposed for continuous-time under-actuated SISO systems in the presence of actuator saturation and state-dependent uncertainties. The proposition of this research is to employ the idea of model prediction together with the Adaptive Fuzzy Sliding-Mode Control (AFSMC) for tuning the sliding surface parameters by predicting the anticipated effects of uncertainties. In the proposed scheme, only after the trigger conditions are met, the coefficients of the sliding surface are updated and the AFSMC is applied. Hence, computational complexity can be controlled by adjusting the switching rule. In the AFSMC, a fuzzy system is used to approximate a nonlinear function, and a robust term to compensate for any possible mismatches. An adaptively tuned gain is also applied to the control signal to prevent instability caused by the actuator saturation. Based on the updating sliding surface, fuzzy singletons, the upper bound of the fuzzy approximation error, and the saturation gain are adaptively tuned. Closed-loop stability is shown to be guaranteed using the multiple Lyapunov functions theorem and the Barbalat's lemma. Finally, the method is applied for the depth control of an Autonomous Underwater Vehicle (AUV), depicting the excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, three types of uncertain high-order nonlinear models are firstly proposed, namely, a single high-order fully actuated (HOFA) model with nonlinear uncertainties, an uncertain second-order strict-feedback system (SFS) and an uncertain high-order SFS, and the relations among these types of models are also discussed. Secondly, a direct approach for the design of robust stabilising controllers and robust tracking controllers for an uncertain single HOFA model are proposed based on the Lyapunov stability theory. Using the obtained robust control design result, the second- and high-order backstepping methods for the designs of robust stabilising controllers of the introduced second- and high-order SFSs are also proposed. The proposed approaches do not need to convert the high-order systems into first-order ones, and for a specific system design, the proposed high-order backstepping methods need fewer steps than the usual first-order backstepping method, hence are generally more direct and simpler. An illustrative example demonstrates both the effect and the application procedure of the proposed HOFA robust control approaches. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) is an effective method to design a robust controller for unknown nonlinear systems. Uncertainty in the worst case requires a large state-action space. Hence, it is natural to use continuous-time RL methods rather than the discretisation of the spaces. In this paper, we propose a novel continuous-time RL using neural network approximation. Our method uses worst-case uncertainty to train the continuous-time RL algorithm. The backward Euler approximation is used to approximate the time derivative of the value function. Compared with the actor–critic (AC) algorithm, our method finds the robust control policy in the presence of worst-case uncertainty by taking into account the applied actions. It is shown that the AC algorithm finds the robust controller in less episodes, but its robustness is less than the results presented by our approach. The convergence of the proposed algorithm is analysed using the contraction property and differential equation techniques. The experiments show that our approach is more robust than the model-based LQR method and the well-known AC method. [ABSTRACT FROM AUTHOR]

The design of a state feedback and robust controller for regulating the tridimensional (3D) movement of autonomous underwater mobile crafts (AUMCs). The controller design is based on the application of Averaged Sub-Gradient Integral Sliding Mode Realisation (ASGISMR). The application of the ASGISMR yields the solution of the closed-loop extreme seeking control for a non-strictly convex functional depending on the tracking error between reference and 3D position of the AUMC. The design of reference trajectories was proposed to enforce the AUMC to follow a continued submersion and ellipsoidal detouring. The mechanical dynamical form of AUMC is well posed for applying the extended version of ASGISMR, considering that integral term represents an ASG associated to the euclidean norm of the tracking error. The time evolution of the functional over the controlled trajectories of the AUMC is compared with the corresponding functional enforced by a traditional state feedback controller with gravity effect compensation. The proposed controller exhibits better tracking and similar control magnitude than the considered reference state feedback realisation. These outcomes justify the potential contributions of the suggested ASGISMR to obtain the local minimisation of the evaluated functional, considering such controller as an extreme seeking realisation for the proposed AUMC. [ABSTRACT FROM AUTHOR]

In this paper, the important issue is proposed, that is, the robust control of a class of cyber-physical systems (CPSs) in the complex environment where unknown disturbances, unknown bounded delays and stochastic malicious attacks coexist. A Lyapunov–Krasovskii functional (LKF) containing certain double and triple integral terms is constructed to exploit merits of the presented generalised single and double integral inequalities which are tighter than some existing single and double inequalities, leading to the stability analysis result with less conservatism. Furthermore, by using stochastic analysis technique, the stability analysis and H ∞ output feedback controller design are transformed into the convex optimisation problems solved by the linear matrix inequality (LMI) strategy. Finally, the actual load frequency control (LFC) power system and the classical numerical examples are provided to show the rationality for modelling and the advancement of the developed method. [ABSTRACT FROM AUTHOR]

To achieve better adaptation properties, the proposed controller is a weighted combination of indirect and direct adaptive fuzzy controls such that both fuzzy descriptions and control rules can be incorporated under a united controller design at the same time. By using fuzzy systems and robust control technique, the interconnections between subsystems are extended to general unknown nonlinear functions. No a priori knowledge of lower and upper bounds on lumped uncertainties is required to construct each local controller. The resulting closed-loop large-scale system is proved to be asymptotically stable. The controller design is applicable to an automated highway system, and simulation results confirm its practical usefulness. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of Static Output Feedback (SOF) stabilisation for continuous-time linear systems subject to norm-bounded parameter uncertainties using the Linear Matrix Inequality (LMI) approach. Usually, this issue leads to the feasibility of a Bilinear Matrix Inequality (BMI), which is difficult to linearise to get non-conservative LMI conditions. We present first, in this paper, some background results on the SOF controller design and that are found to be extended to the case of norm-bounded uncertainties. We show that some restrictions on the feasibility of these results should be guaranteed. Moreover, by means of some technical Lemmas, we transform the BMI into a new LMI with a line search over a scalar variable. An enhanced and less conservative LMI condition with a line search over two scalar variables is also developed. Furthermore, a simplified version of each LMI condition without a priori fixed parameters is also presented. An extensive portfolio of numerical examples is presented in order to evaluate the conservativeness and to show the superiority of the proposed design method to the background results. [ABSTRACT FROM AUTHOR]

This paper concerns the observer-based robust control for uncertain Takagi–Sugeno(T–S) fuzzy systems with disturbances and time delays. An observer is firstly employed to estimate the system state, afterwards, a memorise feedback control law is furthermore proposed. Since the observer state can not be directly influenced by the system uncertainties and disturbances, the proposed control law brings merit on improving system robustness. A mode-dependent Lyapunov-Krasovskii functional (LKF) is designed. By aid of a linear decoupling method, the original nonlinear pair of the observer and the controller gains can be feasibly decoupled. By this method, the co-design method of the observer and controller is conveniently presented. In presence of the time-varying delays, process disturbances and the system uncertainties, the asymptotical stability of the closed-loop system can be guaranteed. Numerical example is given the validate effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with reliable robust sampled-data control for T–S fuzzy system with time-varying delay. By adjusting the free weight matrix in the concept of extended dissipative, H ∞ , L 2 − L ∞ , passive and (Q , S , R) -dissipative performance are solved in a unified framework. A novel mode-dependent Lyapunov–Krasovskii functional (LKF) is constructed, which fully utilises the characteristics of the real sampling period. Based on Lyapunov stability theory, Newton–Leibniz condition and new integral inequality techniques, some less conservative sufficient conditions are obtained to guarantee the close-loop system is asymptotically stable and extended dissipative. Based on sampled-data approach, a robust reliable controller can be developed by solving the linear matrix inequalities (LMIs). The advantage and effectiveness of the proposed design method can be illustrated by several numerical examples. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive proportional-integral-derivative fractional-order nonsingular terminal sliding mode (PID-FONTSM) control for the cable-driven manipulators using time-delay estimation (TDE). The proposed control uses TDE technique to obtain the lumped system dynamics and brings in an effective model-free structure. Then, a PID-FONTSM surface is designed to further enhance the control performance. The proposed PID-FONTSM surface uses the FONTSM error dynamic as its inner loop and utilises a PID-type surface with FONTSM variable as its outer loop. Benefitting from this structure, the proposed PID-FONTSM surface enjoys the advantages from both PID and FONTSM simultaneously. Afterwards, an adaptive algorithm is developed, to timely and precisely regulate the control gain for the robust term. The proposed control method is model-free, highly accurate and essentially continuous thanks to the TDE technique and developed PID-FONTSM surface and adaptive algorithm. The stability of the closed-loop control system under TDE and PID-FONTSM dynamics is proved using Lyapunov stability theory. Finally, comparative simulations were conducted to verify the effectiveness and superiorities of our proposed control method over the existing methods. [ABSTRACT FROM AUTHOR]