This paper is concerned with the problem of prescribed performance tracking control for strict-feedback systems with parametric uncertainties and unmatched disturbances. An adaptive command-filtered control approach with performance guarantees is put forward to address the problem. By means of a new-type self-adjustable performance function and a barrier function, it is capable of achieving the practical prescribed time tracking and enhancing the reliability of control implementation simultaneously, without yet the requirement for the specific initial condition. In the control design, a class of finite-time command filters is adopted to solve the “explosion of complexity” problem, and the error compensation mechanism with practical finite-time stability is introduced to remove the impact of filtered errors. Moreover, considering that the overshoot of the tracking error fails to be quantified by the majority of performance functions with infinite initial values, a pair of asymmetric performance functions is constructed such that the trajectory tracking with the predefined overshoot, settling time, and accuracy is achieved, while preserving the capability of relaxing the initial condition. It turns out that the proposed approach warrants the performance-related constraint satisfaction and the boundedness of all the closed-loop signals. Three simulation studies are carried out to demonstrate the theoretical findings.Note to Practitioners—This paper is motivated by the potential fragility problem exhibited in the traditional constraint-handling methods in the presence of paroxysmal factors such as suddenly strong disturbances or highly fluctuating target trajectories. For practical applications (e.g., satellite docking, automobile production, target interception, and network congestion control) suffering from the above scenario, it might be favorable to widen prescribed performance boundaries in a proactive way from the perspective of the safe and reliable operation of the controlled system. On this basis, a novel command filter-based adaptive tracking control solution with self-adjustable performance guarantees is proposed. It not only achieves trajectory tracking with the preassigned overshoot, settling time, and accuracy in the context of raising the reliability of control implementation but also possesses the attributes of inexpensive computation burden and easy acceptability in practical applications.

The target enclosing control problem for autonomous vehicles with uncertainties necessitates simultaneous consideration of control optimality, robustness, and safety-guided performance constraints. This paper presents a performance-prescribed optimal control algorithm using control barrier function (CBF)-based reinforcement learning (RL) to address the above problem, which contains two key contributions. First, a special CBF-based argument term is developed and embedded into the reward function to characterize environmental feedback regarding the risk of violating constraints, which enables the controller to confine enclosing errors within declared boundaries with minimal intervention. Second, a critic-only neural network is utilized to synthesize the optimal control policy, where a novel fixed-time updating law is presented to accelerate the weight convergence to ideal values within a fixed settling time, thereby enhancing the online learning ability and further improving control performance. Theoretical outcomes related to learning convergence, safety, stability, and robustness are rigorously verified. Simulations reveal that the proposed strategy outperforms the previously designed enclosing controllers based on the non-RL and RL ways in terms of complying with prescribed safety constraints and optimizing long-term performance.

This article proposes a resilient performance-based funnel control approach for transmission control protocol (TCP)/active window management (AWM) networks subject to sudden external disturbances, enabling effective congestion control within a prescribed time. The proposed scheme utilizes an error transformation mechanism with a fixed-time convergence performance function, which not only enhances the flexibility in handling initial conditions but also ensures tight performance guarantees. Additionally, a novel self-adjustable funnel boundary is constructed to mitigate the reliability degradation of control schemes caused by bursty factors such as strong disturbances and highly fluctuating reference signals. These techniques are then integrated into a command-filtered backstepping framework to ensure predefined tracking performance and the boundedness of all closed-loop signals. Finally, simulations demonstrate the feasibility and superiority of the proposed theoretical approach.

This article is concerned with the optimal event-based sensor transmission strategy for wireless networked control systems (WNCSs), which aims to minimize the linear quadratic Gaussian (LQG) cost under the communication rate constraints. A sufficient and necessary condition for the convergence of LQG cost is derived. Then, under this condition, the optimal event-based transmission strategy (OETS) is obtained by proposing a search algorithm. Compared with the existing works, the real-time state information of the channel is not required. Finally, the validity of the results is illustrated through a numerical example.

This paper explores the controlled multi-networks of complex dynamical networks in which nodes are assumed to represent nonlinear dynamical systems. This multi-network systemic structure is assumed to be endowed by adequate communication networks in which, for study sake, the intra-node and the out-node inter-connections are distinguished. In turn, via multi-networks operating at control stabilized steady-state orbits (hidden isolated equilibrium or oscillation) of its node systems, possibly with faulty interconnections, certain confluence parallels between Siljak's coordinator and Chen's random-pinning supervisory controls emerge. Thus these findings are summarized in three novel propositions emphasizing their identical or similar roles. The supervisory control integrated complex multi-networks with locally stabilized nonlinear node dynamical systems emerge of paramount importance in the engineering cybernetics.

In the modern world, any development is impossible without the development of new high-tech automated systems in various subject areas. In this study, three different algorithms were considered that use effective motion control of a wheeled robot. Controlling the movement of a wheeled robot is a complex task that requires an accurate algorithm which allows adaptation to various conditions and tasks. The key is to select an appropriate algorithm that matches the task requirements and the robot's characteristics. Simulators are considered the most effective in the field of modeling robotic systems, complexes and individual objects. When using any simulation, it is important to know the factors that significantly influence the final result. The influence of one of these factors is discussed in this article.

With the recent improvement of deep learning (DL) techniques and computer hardware capabilities, neural networks are widely used to monitor massive sensor data and detect earthquakes in them. This makes designing fast, accurate, and generalized DL models necessary for an active field of research for automatic seismic phase picking. A seismic phase picking network called MFFnet is proposed to fuse power spectral density (PSD), expert knowledge, spectrograms, recurrence plots (RPs), and Gramian angle fields. The network uses fast Fourier convolution (FFC) on 2-D representations to extract more interpretable features. Considering the high proportion of noisy signals in field applications, MFFnet uses focal loss (FL) as the loss function to improve network accuracy. Experimental results show that MFFnet achieves precision, recall, and accuracy with 0.96, 0.98, and 0.98, respectively, in seismic phase detection tasks. Shapley value is used to evaluate the relationship between features and network predictions. Compared with other DL networks, the feature extraction approach used in this letter is more explanatory and provides greater confidence in the results.

This article is concerned with the prescribed performance tracking control problem for the strict-feedback systems with unknown nonlinearities and unmatched disturbances. The challenge lies in the realization of a complete performance specification for trajectory tracking in the sense of quantitatively regulating the peak value, overshoot, settling time, and accuracy while ensuring that the initial condition holds naturally. To this end, an error transformation, equipped with a shifting function, is introduced and incorporated with a new-type barrier function. Then, a class of performance functions is exploited to quantify the settling times and steady-state bounds of the intermediate errors. Moreover, to improve the flexibility of formulating performance specifications for the tracking error, a pair of asymmetric performance boundaries are further designed. With their combination, a novel robust prescribed performance control (PPC) approach is proposed in this article. It not only achieves the quantitative performance guarantees but also preserves the unique simplicity of PPC, evading the needs for function approximation, parameter identification, disturbance estimation, derivative calculation, or command filtering. The above theoretical findings are confirmed via three simulation studies.

Science of artificial neural networks and relevant computing mechanisms since McCullock-Pitts artificial neuron (1943) up via neurons and networks of Anderson (1972), Barto et al (1983), Grosberg (1967, 1976), Hopfield (1982, 1984), Kohonen (1972) to Kasabov's evolving connectionist systems with spiking-neurons (2003) have undergone developments beyond any conceivable predictions. The computational efficiency and functionality of all kinds of neural network implies stable operating steady-state equilibrium is fast established and guaranteed. In parallel, Neurophysiology has yielded many insights Gayton-Hall (2006) converging to paradigm of systems biology. It appeared, on the crossroad of these findings with Hilbert's Thirteen problem and Kolmogorov's Superposition Representations in conjunction with Lyapunov foundations of stability and LaSalle invariance principle certain delicate subtle issues emerged Siljak (2008) and Sprecher (2017). This re-thinking the foundations of neural networks via the quest for parallels between artificial and living neurons is believed to open a new horizon. This belief follows obtained results on cultured-neuron controllers and recurrent neural networks with time-varying delays. A closer look into how animal and/or human brain cells can be cultivated as a controlling brain for a mobile robot (physical body) such that can move around and interact with the world. In turn, a new kind of artificial intelligence may be created, which is emulated by stabilized complex highly non-linear complex neural network system.

Forecasting of mortality function is important for many field of human work like insurance companies, government projections of the human assets, medical research. During past years many models were presented. Most common Lee-Carter model is based on the log function on mortality rate which includes as input variables age, year of mortality function and bias, which also enables predicting the life expectancy. In this paper a perceptron based model with minimum number of nodes in the network having custom transfer function is proposed. Results are compared with Lee-Carter and other neural network based models by using MSE type of error. This model is simpler than other neural networks and is easier to handle adjusting the weights while computing results are rather comparable with those of more complex neural network models.

Science of Neural Networks, and even much more so computing applications, have undergone developments beyond any predictions since McCullock-Pitts artificial neuron (1943) up via Hopfield's neurons (1982, 1984) to Kasabov spiking-neurons neucube (2014) and evolving connectionist systems (2003). Still computational functionality of all kinds of neural network implies guaranteed operating steady-state equilibrium is fast-reached first. On the other side of this spectrum Science of Neurophysiology yielded insights converging to Systems Biology approach Gayton-Hall (2006). It appeared, on the crossroad of these findings with Kolmogorov's representation superposition and Hilbert's Thirteen problem certain rater delicate subtle issues emerged Sprecher (2017). This paper gives one perception of these issues and suggested a revised view on the foundations of past developments, possibly by re-thinking own stability results for recurrent neural networks which possess time-varying delays.

The estimation of the domain of attraction of a class of susceptible-infectious-removed-susceptible immigration is investigated. On assumption the disease-free equilibrium and the endemic equilibrium existences, hence a Lyapunov function too, the domain of attraction of the epidemic model is estimated by means of LF-LMI-moment and SOS optimization approaches. An invariant subset of the domain of attraction, along with certain enlargement, has been achieved. Simulation results, given in a comparison presentation, demonstrate feasibility and validity of the proposed technique as well as reveal that this algorithm outperforms other ones in applications to epidemic models.

A decentralized connective stabilization control problem employing reduced-order observers is solved for complexity large-scale systems with expanding construction. Without changing the original decentralized control laws, a decentralized controller is designed for the resulting expanded system so that the new subsystem added to the original one and the expanding system are robustly connective stable. Furthermore the sufficient condition is derived by using Lyapunov theory and LMI approach. Finally, the proposed method is applied to the AGC design of an expanding power system. The simulation results show both the feasibility and the effectiveness of the proposed decentralized control design.

An active queue management controller based on Hamiltonian energy theory for a class of nonlinear TCP network congestion system in the p resence of uncertain parameters and unknown external disturbances is derived. The restriction of inequality assumption is eliminated by introducing the MiniMax methods into dissipation Hamilton system. Sufficient conditions for the existence of MiniMax controller under circumstances the network system is attacked with maximum impact disturbance has been derived via Lyapunov stability theory. Furthermore, the nonlinear uncertainties presence i.s successfully ameliorated by employing a parameter projection mechanism. Simulation experiments have demonstrated this energy based control strategy ameliorates the pertinent control complexity and is considerably more effective in improving both transient stability and robustness.

For constrained three-link manipulation robots it is shown how the complexity of controlling anthropomorphic arm-like manipulators can be considerable ameliorated by designing a sliding-mode control. First, a representation singular system model of three-link manipulators is established by employing constrained equations and corresponding descriptions of force restriction. Then sliding-mode control with constrained control inputs for three-link manipulator is expatiated as appropriate. Based on quadratic performance index, the optimal sliding mode switching function for three-link manipulator system is derived and a modified reaching law on the grounds of the singular model is constructed using a power function. Then the sliding mode controller for three-link constrained manipulator is designed using the proposed reaching law. Using PUMA-560 the efficiency of the proposed method is demonstrated via the obtained simulation results.

In so far developed theory of switched systems is largely based on assuming certain small but finite time interval termed average dwell time, which represents a constraint even when extremely small. Thus currently most of it appears characterized by some slow switching condition with average dwell time satisfying a certain lower bound. However, in cases of nonlinear systems, when the switching seizes to be slow there may well appear non-expected complexity phenomena of particularly different nature. A fast switching condition with average dwell time satisfying an upper bound is explored and established. Thus the theory is extended by shading new light on the underlying, switching caused, system complexities. A comparison analysis of these innovated characterizations via slightly different overview yielded new results on the transient behaviour of switched nonlinear systems, while preserving the system stability. The multiple-Lyapunov functions approach is the analysis framework.

In this paper we present concepts for synergy of switched fuzzy and fuzzy-neural systems. First, an algorithm/procedure for neural network identification of switched fuzzy models, out of input-output data pairs is given. In order to use the existing stability and stabilization results in the field of switched fuzzy systems to the identified switched fuzzy-neural models, an extension of the switched fuzzy model with levels of structure is presented. The proposed concepts are used for identification of discrete switched fuzzy models. To confirm the proposed algorithm/procedure, and the new extended model, a fuzzy-neural identification of the discrete switched fuzzy model for the nonholonomic WMR vehicle is presented. Based on the identified discrete switched fuzzy model for the WMR vehicle, design of discrete switched fuzzy controller is made. The simulation results show the effectiveness of the proposed concepts.

This paper investigates the problem of delay-dependent stabilization for uncertain T-S fuzzy systems with state and input time time-varying delays. A new method is proposed by defining a novel Lyapunov-Krasovskii functional and making use of novel techniques to achieve delay dependence. Unlike existing works in this topic, this approach developed in this paper employs neither free-weighing matrices nor model transformations, using Jensen's inequality. As a result, simplified yet improved stability conditions for T-S fuzzy systems with norm bounded type uncertainties are obtained. All the conditions are derived in terms of linear matrix inequalities (LMI), which can be solved by using LMI optimization techniques. The benefits and efficiency of the method are demonstrated by the numerical example.

This paper presents a controller design method for dealing with the induced ℒ2-norm problem for switched linear parameter-varying (LPV) systems. Considering the arbitrary switchings caused by the parameters varying, a common parameter-dependent Lyapunov function is employed to derive sufficient linear matrix inequality (LMI) conditions for the switched LPV systems. A family of LPV controllers are designed according to the LMI conditions, and each of them is suitable for the corresponding parameter region. The proposed switching LPV control method is applied to an F-16 aircraft longitudinal model and simulation results demonstrate the effectiveness of the approach.

This paper studies the delay-dependent stability problem for a class of switched neutral systems with mixed time-varying delays. Based on Lyapunov-Krasovskii functional approach, a sufficient condition for exponential stability is developed for arbitrary switching signal with average dwell time. This condition is delay-dependent and given in terms of linear matrix inequalities. A numerical example is given to show the proposed method.

A Metropolis criterion based fuzzy Markov game flow controller (MFMC) is proposed to cope with congestion problems in high-speed networks. Because of uncertainties and highly time-varying time delays, for such networks the complete and accurate information is not easy to obtain in real time The Q-learning, which is independent of mathematic model and prior knowledge and yet enables achieving good performance, is a viable alternative. The fuzzy Markov game offers a promising platform for robust control in the presence of external disturbances and unknown parameter variations that are bounded. The Metropolis criterion can cope with the balance between exploration and exploitation in action selecting. Simulation experiments demonstrate the proposed controller can learn to take the best action in order to regulate source flows. Thus it can guarantee high throughput and low packet loss ratio while efficiently avoiding the congestion.

This paper addresses the issues of passivity and feedback equivalence for switched nonlinear systems via multiple storage functions. The concept of storage-like functions for switched systems is presented. A sufficient condition for passivity of the switched nonlinear systems is given via multiple storage functions under some switching signal. Then, the result is extended to find conditions under which the switched system is feedback equivalent to a passive switched system.

An active queue management (AQM) scheme employing state observer and sliding mode control (SMO-SMC) is designed in this paper in order to overcome congestion problem in TCP communication networks. First, sliding mode observer is designed for input delay network system; a sufficient condition is given for the existence of such an observer in terms of linear matrix inequality. Then, on the grounds of estimated system state vector, a controller is synthesized by using the sliding-mode control theory combined with the reaching law technique. The reachability condition in such a SMO-SMC AQM scheme is also discussed. The stability and robustness of the proposed control scheme are then validated for different network scenarios using Matlab/Simulink. Simulation results for the single-bottleneck benchmark network confirmed the proposed scheme achieves high-quality performance as well as outperforms the previous observer-based sliding-mode and traditional controllers.

This paper explores aspects of computational complexity versus rule reduction and of integrity preservation versus optimality index, which have become an issue of considerable concern in learning techniques for adaptive fuzzy inference models. In control-oriented applications of adaptive fuzzy inference systems, implemented as fuzzy-neural networks, a balanced observation of these conflicting requirements appeared rather important for a good yet feasible application design. The focus is confined to a family of adaptive fuzzy inference systems that can be interpreted as a partially connected multilayer feedforward neural networks employing Gaussian activation function. The knowledge base rules are designed implying the connections are a priori fixed, and then the respective strengths adapted on the grounds of input and output data sets. Information granulation plays a significant role too. These as well as membership-function parameters ought to be adapted in a learning-training process via the minimization of an appropriate error function. © 2008 Wiley Periodicals, Inc.

Stability issues for switched systems whose subsystems are all Sugeno fuzzy discrete-time systems are studied and new stabilization control results derived. Innovated representation models for switched fuzzy systems are proposed. The single Lyapunov function method has been adopted to study the stability of this class of switched fuzzy systems. Sufficient conditions for quadratic asymptotic stability are presented and stabilizing switching laws of the state-dependent form employed in PDC scheme are designed. Illustrative examples demonstrate the effectiveness and the feasible performance of the proposed synthesis through the respective simulation results.

The investigated control problem of nonlinear time-delay discrete-time systems is addressed using Takagi-Sugeno model. Parametric uncertainty and time-delay terms are employed in building the Takagi-Sugeno model for the controlled plant for the purpose of close representation of the original plant system. The integrity and robustness are guaranteed for the closed-loop fuzzy system in sense of Lyapunov stability method via fuzzy state observers. Sufficient conditions for the fuzzy system to possess integrity against actuator failures and sensor failures in the closed-loop are derived in terms of linear matrix inequalities under assumption of known bounds on uncertainties. The results for trailer-truck example are used to illustrate the effectiveness of the method.

A robust tracking fuzzy control technique for nonlinear time-delay systems based on fuzzy T-S model is proposed. The fuzzy T-S system model with parametric uncertainties is employed to represent a nonlinear time-delay plant. The control design synthesis is aimed to reduce to negligible small the tracking error for all bounded reference inputs and to guarantee Hinfinperformance. The advantage of proposed tracking control design is only a simple fuzzy controller designs are employed. By means of the proposed method, the fuzzy tracking control design problem is parameterized in terms of a linear matrix inequality problem, which can be efficiently solved using any convex optimization techniques. The example of trucking control for the benchmark truck-trailer system is given to demonstrate the efficiency as well as the validity of the proposed technique.

The recent emerging trend in financial systems engineering relies on exploiting soft-computing technologies, and on employing neural-nets techniques, in particular. Simultaneously, recent empirical studies on economic stabilization programs implemented worldwide have clearly demonstrated that, after the successful disinflation, the inflationary process can no longer be captured and explained using the traditional variables and models provided by economic theory. This paper proposes a combined stochastic and artificial neural-nets approach in expert support systems to the identification of inflation dynamics by means of Box-Jenkins ARIMA and Elman-ANN models. The approach is illustrated by means of the case-study data set on inflation dynamics in the pre-and post-stabilization period in the Republic of Macedonia.