Search

In this article we investigates the practical fixed-time consensus tracking problem for multiple Euler–Lagrange systems, which is subject to, concurrently, stochastic packet losses in directed communication networks (DCNs) and input/output constraints. In practice, the packet losses in the communication network may lead to communication failure and control accuracy degradation. To address this impact, a novel fully distributed observer is established to estimate the leader’s states within fixed time. Subsequently, the state constraint function is introduced so that the designed adaptive control protocol can be used in a uniform structure with unconstrained or asymmetric/symmetric output constraints without changing the adaptive structure. Combining the backstepping technique with fuzzy-logic systems, an adaptive fixed-time local control protocol is constructed to drive each agent to track the leader’s states estimated by the observer. Besides, a novel auxiliary system is introduced to solve the constraint problem of the control input. Furthermore, through rigorous theoretical analysis, it is verified that all signals converge to the compact set close to zero with guaranteed fixed-time convergence rate. At last, a representative simulation is carried out to demonstrate the practicability of the proposed method.

This article demonstrates the realization of angle tracking and deformation suppression by developing two boundary controllers for a flexible variable-length rotary crane arm with extraneous disturbances and asymmetric input-output constraints. The dynamic model description of this kind of crane arm system is several partial differential equations integrated into few ordinary differential equations. The S-curve acceleration and deceleration scheme is utilized to adjust the elongation rate of the arm. A kind of novel observer is put forward to tackle unknown extraneous disturbances. Auxiliary systems and barrier Lyapunov functions are introduced to meet the asymmetric input-output constraints. With the help of Lyapunov's theory, the global exponential stability and uniform boundedness are analyzed. The numerical simulations are finally provided to illuminate its availability of the designed control schemes.

The issues of input-output finite-time stability and stabilization of Takagi-Sugeno fuzzy systems with time-varying state delay and exogenous input signal are studied in this work. For the stabilization process, a controller that does not share the same membership functions as the system is considered and the state feedback is quantized by using a logarithmic quantizer. The desired stability criterion for the addressed fuzzy system is first derived without the control term. It is then extended to the case in which the proposed controller is present. The stability criteria are given in the form of matrix inequalities, which are supported by the augmented Lyapunov-Krasovskii functional, generalized free-weighting-matrix approach and Finsler's lemma. Moreover, as a special case, an asymptotic stability criterion is obtained and used in a comparative study. As a result, the time-delay interval is much larger than some of the works published very recently, which is due to the augmented Lyapunov-Krasovskii functional construction. In addition, the effectiveness and practicability of the theoretical findings are validated with simulation examples.

In this article, we consider a feedback interconnection, which consists of a networked multi-agent system with its distributed controller and two sets of external input signals. Assuming that the agents are identical, and the dynamics of each agent and its local controller are represented by transfer functions, the well-posedness condition, internal stability and input-output stability of the feedback interconnection are investigated. A controller is designed by solving a Diophantine equation, and a gain for the network is selected using root-locus of a unity feedback system to achieve input-output stability. The developed results are demonstrated with a numerical example.

This paper explores a scheme of tuning a basic input-output (I/O) gain (BIOG) depending on usage conditions for improving the work performance of human-operated machines. I/O gain tuning is effective for improving operability and workability, but continual and/or huge changes makes operators confuse the machine dynamics, and then degrade the operability. Thus, the proposed tuning system tunes the BIOG at long terms according to comprehensive characteristics of the work content and operator, collected from a control lever input histogram. The target value is specified by a Gaussian distribution, meaning that all ranges of the spring-type control lever are equally used. This approach was chosen because leveling the control lever histogram independently of the operator and work content provides a consistent operational experience, leading to comfortable operations. In this study, BIOG curves are specified as a polygonal line involving a break and saturation point. These points are adjusted by reciprocal conversion of differential areas between the Gaussian distribution and obtained histogram curves. Experimental results obtained with a robot arm indicated that the developed BIOG tuning system could improve time efficiency by decreasing the differential area and increase the subjective usability as compared to a conventional fixed BIOG system. Moreover, the relationship between the histogram and BIOG curve could reveal features of both the work content and the operators skill level.

This paper presents an input–output linearization (IOL) control strategy to achieve PFC (power factor correction) and output voltage constant in a boost converter. The method transforms the nonlinear system to a linear one in new coordinates by the appropriate nonlinear coordinate transformation and state feedback, ultimately allowing the controller to be designed according to the classical linear control theory. An example implementation is simulated using PSIM software. The simulation results show that the phases of input voltage and input current are consistent, and the output voltage remains constant under load step changes. A set of experimental devices are set up to verify the control method, and the results are consistent with the simulation. Thus, the method is proved to be effective in achieving PFC and provides robust constant voltage output in the boost converter.

This article investigates an output antisynchronization problem of multiagent systems by using an input–output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor–critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy.

Input-output pairing effectively minimizes the loop interaction in MIMO processes and avoids integral instability. In this article, a modified Relative Normalized Gain Array is proposed for the Inp...

This article addresses the issue of input–output finite-time stability (IO-FTS) for nonlinear systems by employing sliding-mode control (SMC) methodology. Many practical systems subject to complex factors, such as the single-link robot arm model (SLRAM), can be characterized as nonlinear systems. Our attention is focused on designing a fuzzy-model-based finite-time SMC law to attenuate the influences of uncertainty, nonlinear term, and external disturbance during the finite-time region. First, a novel integral sliding-mode surface is proposed based on the Takagi–Sugeno fuzzy rule. Then, by using the key point of Lyapunov function theory, an appropriate fuzzy SMC law is designed to make sure that the signal variables can arrive at a domain within the assigned fixed-time level. Moreover, some new IO-FTS criteria are constructed for the resulting sliding dynamics over the whole finite-time level, including reaching phase and sliding motion phase. Via the SLRAM, we demonstrate the effectiveness of the proposed SMC approach.

Global climate change is an alarming problem nowadays. Weather temperatures have been uncertain because of what many scientists claim is the effect of greenhouse gases, contributed mainly by the energy industry. In Malaysia, the energy industry provides a significant contribution to it economy making up about 20 percent of the GDP. Malaysia is the third largest natural gas exporter in the Asia-Pacific region in 2011 and eleventh in the world (British Petroleum Statistical Review of World Energy, 2014). Malaysia's CO2 emission recorded at 185 million tonnes in 2010 (International Energy Agency, 2012). Mohd Safaai et al. (2011) projected that without any mitigation measures 285.73 million tonnes of CO2 will be released in 2020. In the Copenhagen forum 2009, Malaysia has pledged to reduce 40% of carbon emissions by 2020 compared to the 2005 levels. Motivated by the government efforts, this study will construct environmentally extended input-output framework for Malaysia to obtain the energy-related CO2 emission intensities by sectors. The study is responded to the inadequate of reliable data on Malaysia’s sectoral CO2 emissions and to the growing awareness of the effectiveness of Malaysia climate change policies. This study expanding the current understanding of interactions among economic activities, energy intensities, and CO2 emissions. Results of this study found that Transportation recorded the second highest contributor to CO2 emission, and these findings were different to many other existing studies that found transportation sector as a main contributor to CO2 emission. In addition, results suggest that it is necessary to control environmental problems and encourage the energy use efficiency in the production process, particularly in the Building and construction sector, Transportation, Electricity, gas and water and Agriculture sector in Malaysia The findings will help the policy-makers particularly in Malaysia to develop a strategic plan and tools to manage CO2 emission with respect to climate change in these four sectors.

By incorporating a logic-based switching mechanism into high-gain linear controllers, global output-feedback adaptive stabilization is achieved for a class of nonlinear systems with unknown input–output-dependent lower-triangular growth rate and uncertain control coefficient. When a controller candidate, associated with a Lyapunov candidate, is connected into the closed-loop, the logic unit constantly supervises the change rate of the Lyapunov candidate. If it does not decrease as rapidly as predicted, another controller candidate will be switched in to replace the former one. It is theoretically proved that there is only a finite number of switching times, and asymptotic stability of the closed-loop system is guaranteed. Compared with existing results, the logic-based switching adaptive control approach can tolerate strong input–output-dependent nonlinearities, as well as large uncertainties from the system dynamics including the control coefficient. Finally, a numerical example is provided to illustrate the feasibility and the effectiveness of the proposed approaches.

This paper will focus on optimizing parameters of sliding mode controllers (SMC) for hybrid stepper motor models simulated in Matlab/Simulink. The main objective is to achieve a smooth transient and robust, steady-state to track reference rotor position when the stepper motor is subjected to load disturbances. Two different structures of SMC controllers will be studied, which are based on the flat system concept that is applicable to the stepper motor model. The hassle to determine controller parameters will be optimized using the Simulink Response Optimizer application. The performance of the controllers will be evaluated by considering load torque and variation in the model parameters. Although the results showed that an open-loop controller could move the rotor to the desired position, however, the transient response had undesired oscillations before the output settled at the steady state. The response was improved by optimizing SMC controllers’ parameters to meet the desire step response requirement. Despite both SMC methods have successfully tracked the reference, there are some challenges to deal with each method in regard to the state measurements, the number of optimized controllers’ parameters, and the scattering of control inputs.

The purpose of the study was to determine the problem of control of a dynamic system of higher dimension. Relying on Leontev input-output balance, we formalized the dynamic system and synthesized its control. Within the research, we developed a mathematical model that combines different working objects that consume and release various resources. The value of the penalty for all nodes and objects is introduced into the matrix representation of the problem, taking into account various options for their interaction, i.e., the observation problem. A matrix representation of the planning task at each working object is formed. For the formed system, a control loop is created; the influencing parameters of the external environment are indicated. We calculated the system operational mode, taking into account the interaction of the nodes of objects with each other when the parameters of the external environment influence them. Findings of research show that in achieving a complex result, the system is inefficient without optimal planning and accounting for the matrix of penalties for the interaction of nodes and objects of the dynamic system with each other. In a specific example, for a dynamic system with a dimension of 4.8 million parameters, we estimated the control taking into account the penalty matrix, which made it possible to increase the inflow of additional resources from the outside by 2.4 times from 130 billion conv. units up to 310 conv. units in 5 years. Taking into account the maximum optimization of control in the nodes, an increase of 3.66 times in the inflow of additional resources was ensured --- from 200.46 to 726.62 billion rubles

The main problems of constructing models used to develop of spatial and sectoral long-term economic forecasts are due to the lack of direct statistical data in full, i.e. regional tables of distribution of goods and services. Their calculation has to be carried out on the basis of a limited set of direct and indirect indicators that make it possible to determine the spatial structures of production, consumption, accumulation of fixed capital, etc. with an accuracy sufficient for further use in the information content of interregional intersectoral models. The report examines the problems of spatial allocation of the all-Russian table “input-output” and proposes the author's approach to the partial automation of the procedures necessary to construct a set of regional tables (by federal district). Regionalization was carried out using a static interregional intersectoral model with control of bordering outcomes. The result is a set of consistent regional tables as of 2015 - the sum of eight input-output tables exactly matches the all-Russian table. The constructed static multiregional input-output model (OMIOM) across federal districts is necessary for the next stage of work - the development of a semi-dynamic model used to calculate options for long-term national economic forecasts.

Groundwater quality assessment is characterized by pollution injection rates, pollution injection locations and duration of pollution injection for identifying spatial and temporal variation. In this study, spatial variations are obtained by placing observation wells in the downstream zone. Temporal variations in contaminant concentration has been simulated during the study period. Generally, simulations are carried out using various numerical models, which are subject to the availability of all required input parameters and are necessary for the proper management of contaminated aquifers. In previous publications, artificial neural networks (ANNs) are prescribed in such situations as these modeling methods focus on available input/output datasets, thus resolving the concern of obtaining all inputs that a numerical simulator usually demands. Past studies have predicted groundwater breakthrough contaminants. But the effects of input/output variations need to be discussed. This study aims to quantify the effects of a few input/output datasets in the performance of ANN models to simulate pollutant transport in groundwater systems. The combinations of input/output scenarios have rendered these ANN models sensitive to variations, thus affecting model efficiency. These outcomes can reliably be employed for contaminant estimation and provide a paradigm in data collection that will help hydrogeologists to develop more efficient prediction models. HIGHLIGHTS A brief review on groundwater modeling using artificial neural networks.; Effects of input and output parameters in ANN modeling.; ANN modeling strengths and weakness in varying input/output parameters.; The practical implication of this methodology.

Mathematical modeling is the tool to reveal and fully understand the input output relationship in most of the dynamic systems including fuel cells. The dynamic behavior of fuel cell system is deter...

The main application of input—output models is the analysis and forecasting of impact that changes in the final consumption volume and composition have on the production structure. The paper presents results of type I and II multipliers calculation for national and regional (evidence from Moscow) economies based on the input—output tables for 2016 for aggregated economic activities. The attention is devoted first of all to structural aspects of multiplicative effects and to the analysis of the variability of regional multipliers calculation results depending on the approaches. To derive regional multipliers we apply location quotient-based approaches. We show that national type II (including induced effects) and regional multipliers estimations may be highly sensitive to the estimation technique choice. Results from such calculations should be interpreted with consideration to differences of the used techniques and their tendencies to under- or overestimate effects of the final consumption structural changes.

In this paper, artificial neural network is used to calibrate sensors that are commonly used in industry. Usually, such sensors have nonlinear input output characteristic that makes their calibration process rather inaccurate and unsatisfied. Artificial neural network is utilized in an inverse model learning mode to precisely calibrate such sensors. The scaled conjugate gradient (SCG) algorithm is used in the learning process. Three types of industrial sensors which are gas concentration sensor, force sensors and humidity sensors are considered in this work. It is found that the proposed calibration technique gives fast, robust and satisfactory results.

This paper is concerned with the problem of input-output finite-time stability (IO-FTS) for a class of switched singular continuous-time systems. First, the concept of IO-FTS is extended to switched singular systems. Then, by using the average dwell time switching approach and constructing multiple Lyapunov functions, a sufficient condition of IO-FTS for the considered system is presented in the form of linear matrix inequalities (LMIs). Furthermore, based on the derived conditions, a suitable state feedback controller is designed to stabilize the switched singular systems. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed technique.

This paper introduces a model-based robust adaptive input-output control framework for a family of robotic systems that include under-actuation, nonholonomic, and constrained properties. The propos...

SS 201 had been reported as a good substitute for SS304 without any significant compromise in performance. However, modeling EBW process using an efficient tool like adaptive neuro-fuzzy inference system (ANFIS) and use of multi-objective optimization to optimize its performance are not reported yet. Thus, the present study employed ANFIS models tuned by genetic algorithm, particle swarm optimization, gray wolf optimizer, and bonobo optimizer (BO) to predict weld attributes during EBW of SS201 as a function of input process parameters. Among the developed models, ANFIS tuned by BO was seen to yield the best prediction accuracy. In multi-objective optimization (MOO), the two conflicting goals were to minimize secondary dendritic arm spacing and maximize Vicker’s hardness number simultaneously. In MOO, some interesting facts were observed, such as the fixed input parameter of power (P) as 3200-W and squeezed experimental range for the welding speed (S).

This paper investigates input–output finite-time reliable static output feedback (SOF) control of a time-varying system under the influence of both input time delay and actuator failures. An actuator fault model consisting of linear and nonlinear faults is considered during the time-varying control process. The objective is to design a reliable SOF controller that can ensure input–output finite-time stability (IO-FTS) of the resulting closed-loop system. An augmented time-varying Lyapunov functional is constructed, in which some Lyapunov matrices are variable function of time ${t}$ . By dividing the time interval and delay interval into equal segments, the matrix-valued functions are expressed by a linear interpolation formula. Moreover, combining with the single and double Wirtinger-based integral inequalities, delay-dependent IO-FTS conditions are derived. It is shown that the SOF control issue is solved in forms of linear matrix inequalities. In the end, the effectiveness is demonstrated by simulations.

The overheating caused by the operation of implantable device can cause damage to the surrounding tissue. In applications like neural prosthesis, 1 °C of temperature increase could lead to irreversible damage to the subject. Predicting the overheating effect is therefore critical to maintain safe operation. This work proposes a Bayesian recursive multi-step prediction method for implantable device to predict the overheating effect. The method proposed in this article achieves accurate prediction within a horizon with low complexity by model updating that iteratively minimizes a function of the ${j}$ -step-ahead prediction error. At each time instant, the new available input output data are stored in a First In First Out (FIFO) queue of fixed length, and the model parameters are updated by iteratively minimizing the ${j}$ -step-ahead prediction error of the new data. Moreover, the regularization methods are introduced to improve the prediction performance by taking the Bayesian interpretation of the parameters into consideration. Monte Carlo simulation studies indicate that the developed method is able to estimate the fundamental dynamics of the system when the prediction model is underparametered, and is robust to measurement noise. For time varying systems, the developed method can capture the system dynamics during the system variation. The proposed method is demonstrated via an in-vitro test vehicle, which shows that the temperature increase can be predicted with high accuracy and low complexity.

Publisher Copyright: Copyright © 2021 The Authors. This is an open access article under the CC BY-NC-ND license.

There have been efforts to simplify analysis of a complex system by relating it to a simpler system through certain system relations. In this paper, we consider continuous-time systems related by a...

Incremental input/output-to-state stability (i-IOSS) is a popular characterization of detectability for nonlinear systems. For instance, it is known that any system that admits a robustly stable fu...

This paper deals with a state model identification of a gas turbine used for gas transport, using a subspace approach of the state space model. This method provides a reliable and robust state representation of the model, taking advantage of its benefits in the control, monitoring, and supervision of this machine. The model for each variable is set so that the state matrices associated with the gas turbine model are determined from their real input/output data. The comparison of the obtained identification results with those of the actual turbine operation serves to validate the proposed model in this work. This numerical algorithm of the subspace identification method is full of information and more accurate in terms of residual modeling error, and expresses a very high level of confidence in the identified turbine system dynamics. Hence, the controllability and observability tests of turbine operation for different input/output variables allowed to validate the real-time operating stability of the turbine.

This study is concerned with the problem of finite-time fault-tolerant control for uncertain semi-Markovian jump systems with input constraint and multiple time delays. There exist nonlinear functions, exogenous disturbances and measurement noise in these nonlinear systems. First, a dynamic output feedback controller is constructed for the uncertain semi-Markovian jump systems. A closed-loop system is earned. Next, sufficient stability conditions of the closed-loop system are obtained via stochastic Lyapunov function. Not only finite-time boundedness but also input-output finite-time mean square stabilization is achieved. This study is the first time to study. Finally, an example is provided to prove the novelty of the approach proposed in this paper.

Zeros are of extreme importance in linear systems theory, especially unstable zeros, which degrade achievable performance. Furthermore, the presence of a zero in a state-space model implies the exi...

A key problem in process control is to decide which inputs should control which outputs. There are multiple ways to solve this problem, among them using gramian-based measures, which include the Ha...

Cyber-physical systems (CPSs) are implemented in many industrial and embedded control applications. Where these systems are safety-critical, correct and safe behavior is of paramount importance. Malicious attacks on such CPSs can have far-reaching repercussions. For instance, if elements of a power grid behave erratically, physical damage and loss of life could occur. Currently, there is a trend toward increased complexity and connectivity of CPS. However, as this occurs, the potential attack vectors for these systems grow in number, increasing the risk that a given controller might become compromised. In this article, we examine how the dangers of compromised controllers can be mitigated. We propose a novel application of runtime enforcement that can secure the safety of real-world physical systems. Here, we synthesize enforcers to a new hardware architecture within programmable logic controller I/O modules to act as an effective line of defence between the cyber and the physical domains. Our enforcers prevent the physical damage that a compromised control system might be able to perform. To demonstrate the efficacy of our approach, we present several benchmarks, and show that the overhead for each system is extremely minimal.

Some notes are presented to this paper “Input–output finite-time stability of fractional-order positive switched systems” by Liang et al. (Circuit Syst Signal Process 38:1619–1638, 2019). A counterexample is taken to verify our analysis.

Precise estimation of transmission input&output shaft torque and wheel speed is significant to the development of torque distribution and coordinated control method for the power split hybrid powertrain system. This paper studies an estimation method of transmission input and output torque as well as drive wheel speed using an unknown input observer in different operating modes for a compound power split hybrid powertrain system. Firstly, the configuration and dynamic characteristic of power split transmission is analyzed. Then, state equations in different operating modes are established, and a Takagi-Sugeno (T-S) fuzzy model is utilized to deal with the nonlinearities of load resistance torque. Furthermore, an unknown input reduced-order observer is designed to estimate the transmission input&output shaft torque and wheel speed. The asymptotical stability of the developed observer is verified based on Lyapunov theory. Finally, the performance of the unknown input observer is assessed by comparing with the Luenberger observer based on Matlab/Simulink simulation platform and test bench. Simulation and experiment results show that, the proposed observer can estimate the transmission input&output shaft torque and wheel speed more effectively in different operating modes.

This article presents some novel results on sampled-data H∞ control for a class of linear systems. The proposed system is affected by time-varying delay and external disturbance. The main goal of this paper is to transform the original system into an equivalent two interconnected subsystems through the combination of input-output approach and scaled small gain (SSG) theorem. Then, the three term approximation method is adopted to approximate the time-varying delay. By incorporating Lyapunov-Krasovskii functional and wirtinger integral inequality, a new set of sufficient conditions is obtained in terms of linear matrix inequalities (LMIs), which guarantee the stability of the closed loop system, and a H∞ norm bound performances. Finally, the applicability of the developed control design technique and its less conservativeness over other existing ones are proven by means of simulation examples.

In this letter, a nonlinear control design is presented for systems whose input and output vectors are saturated. The proposed synthesis includes an optimal tracker as the seed controller and explicitly imposes all the relevant constraints on system input, on the rate of change in the input, and on system output by using the tools of barrier functions, input–output feedback linearization, comparison argument, Lyapunov argument, and a real-time constrained optimization. The proposed design is applied to microgrid operation with high penetration of renewable generation, where net load and variable generation must be balanced and the microgrid should be operated at an optimal or near-optimal performance level. Since traditional generation has a limited ramping rate, battery storage devices, and demand responses become necessary to effectively deal with variability of renewable generation and to maintain frequency stability, but their capacities are also limited. It is shown that the proposed design is effective for coordinated control of traditional generation, storage and demand response so that the power system frequency is guaranteed to be within the required operational limits and that renewable curtailment is eliminated or minimized.

The black box model of a dynamic system usually consists of just input and output. There is no correlation or coupling between the input and output. This paper proposes a self-coupling black box model method to realize the coupling between its input and output by introducing “virtual variables” to the black box model of a dynamic system considering the advantages of artificial neural network (ANN) in the system. The ANN is used for black box modeling. The modeling process of the self-coupling black box is illustrated through the simulation models and simulation analysis of the particle settlement process and the Unmanned Undersea Vehicle (UUV) launching process. By comparing its result with the result of the standard black box model, the advantages and disadvantages of the self-coupling and standard black box models in the calculation of accuracy are analyzed.

Recently, soft robots that consist of soft and deformable materials have received much attention for their adaptability to uncertain environments. Although these robots are difficult to control with a conventional control theory owing to their complex body dynamics, research from different perspectives attempts to actively exploit these body dynamics as an asset rather than a drawback. This approach is called morphological computation, in which the soft materials are used for computation that includes a new kind of control strategy. In this article, we propose a novel approach to analyze the computational properties of soft materials based on an algebraic method, called the input–output equation used in systems analysis, particularly in systems biology. We mainly focus on the two scenarios relevant to soft robotics, that is, analysis of the computational capabilities of soft materials and design of the input force to soft devices to generate the target behaviors. The input–output equation directly describes the relationship between inputs and outputs of a system, and hence by using this equation, important properties, such as the echo state property that guarantees reproducible responses against the same input stream, can be investigated for soft structures. Several application scenarios of our proposed method are demonstrated using typical soft robotic settings in detail, including linear/nonlinear models and hydrogels driven by chemical reactions.

A new approach to the design of nonlinear disturbance observers (DOBs) for a class of nonlinear systems described by input–output differential equations is presented in this paper. In contrast with established forms of nonlinear DOBs, the most important feature of this new type of DOB is that only measurement of the output variable is required, rather than the state variables. An inverse simulation model is first constructed based on knowledge of the structure and parameters of a conventional model of the system. The disturbance can then be estimated by comparing the output of the inverse model and the input of the original nonlinear system. Mathematical analysis demonstrates the convergence of this new form of nonlinear DOB. The approach has been applied to disturbance estimation for a linear system and a new form of linear DOB has been developed. The differences between the proposed linear DOB and the conventional form of frequency-domain DOB are discussed through a numerical example. Finally, the nonlinear DOB design method is illustrated through an application involving a simulation of a jacketed continuous stirred tank reactor system.

This paper considers asynchronously input-output finite-time control of positive impulsive switched systems(PISS). Firstly, the definition of input-output finite-time stability(IO-FTS) is introduced. By using the Lyapunov functional and average dwell time(ADT) approach, a state feedback controller is designed and new sufficient conditions are obtained to guarantee the corresponding closed-loop system is IO-FTS under asynchronous switching. Such conditions can be solved by linear programming. Finally, a practical example is provided to show the effectiveness of the proposed method.