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The robust control network for nonlinear large-scale systems with parametric uncertainties also considers the uncertain robust stabilization problem for controlled networks. In heterogeneous populations, hybrid regression models are the most important statistical analysis tools. To aim of the study is to conduct a more in-depth analysis of the existing completive robust control networks relying on biased temporal logic. Compared with the symmetric distribution, the skewed distribution can obtain accurate and effective information. Therefore, a time-series logic model under skewed distribution is proposed. The temporal logic under skew state is applied to describe the normative language of fuzzy systems. Firstly, the mixed nonlinear regression model under skewed distribution data is introduced to test whether the temporal logic formula can be realized under the skew state. Secondly, through the method of reduction, the control flow interval logic CFITL is studied, and the time series logic sequence is used to describe the measurement output loss. The sufficient conditions for the control network system to satisfy the exponential stability and H ∞ performance index are given. The linear matrix inequality obtains the completeness control network to be designed, and the effectiveness of the proposed method is verified by stochastic simulation experiments. Finally, the method is verified to be practical and feasible based on actual data. The maximum recognition rates of nearest neighbor classification, nearest subspace classification and biased distribution temporal logic classification reached 0.9019, 0.9622 and 0.9304, respectively.

The quest for an efficient quantitative feedback theory (QFT) Automatic Loop Shaping algorithm is an open research problem. Different approaches have been adopted in the literature to efficiently solve this optimization problem, which is computationally hard due to its nonlinear and nonconvex nature. The first algorithms focused on different forms of simplification of the original problem, leading to faster but not accurate results. Stochastic algorithms have been another popular way of dealing with the complexity of this problem. These algorithms are faster than an exhaustive search, but do not guarantee, in general, the globally optimal solution. A third, more recent, approach, consists of using interval analysis global search algorithms, which are able to accurately solve the original problem, and are very suitable for execution speed optimization. In this work, one of these algorithms is taken as a basis to propose and develop an improved algorithm which is more efficient in terms of the execution time needed to solve a given problem. This improved efficiency is achieved by using two new information sources: Phase information and feasible boxes information. The main contribution of this work is to propose the use of these two new information sources to reduce execution time and to integrate this idea in the original algorithm. Their individual and joint associated speedups are measured by solving two classical QFT example problems: Matlab® QFT Toolbox example 2 and ACC ’90 benchmark problem. The results show that the new algorithm’s performance is significantly better.

In stochastic computing (SC) systems numbers are represented with mean values of random binary sequences. This paper introduces a novel fuzzy inference architecture, in which the computational mechanism is based on stochastic logic (SL). First, the basic concept of SL is described, then the architecture of the SL-based fuzzy logic controller (SFLC) is built up systematically using the derived stochastic elements. The second part of the paper demonstrates the application of the proposed techniques, where the SFLC-based control performance is evaluated on a real mechatronic system. The results show that the SL-based approach provides effective and robust control performance, simple architecture and high noise tolerance. The proposed method is also benchmarked against conventional FLCs indicating that the robustness of the stochastic architecture allowed to outperform the benchmark controllers in noisy environments.

This letter presents a new algorithm for designing robust output-feedback controllers in the presence of structured uncertainty. The proposed approach is an alternative to the popular D-K Iterations, consisting of a pair of LMIs (Linear Matrix Inequalities) and an associated iterative algorithm. The new algorithm is illustrated by examples and its performance is compared with the D-K iterations by solving instances of the COMPleib benchmark library.

This paper presents an algorithm that computes polytopic robust control-invariant (RCI) sets for rationally parameter-dependent systems with additive disturbances. By means of novel linear matrix inequalities (LMI) feasibility conditions for invariance along with a newly developed method for volume maximization, an iterative algorithm is proposed for the computation of RCI sets with maximized volumes. The obtained RCI sets are symmetric around the origin by construction and have a user-defined level of complexity. Unlike many similar approaches, the proposed algorithm directly computes the RCI sets without requiring control inputs to be in a specific feedback form. In fact, a specific control input is obtained from the LMI problem for each extreme point of the RCI set. The outcomes of the proposed algorithm can be used to construct a piecewise-affine controller based on offline computations.

Set invariance in the presence of uncertainty and disturbance is of central importance for the safety of control systems. This article proposes a data-driven method to compute an approximation of a minimal robust control invariant set (mRCI) from experimental data. For a given dynamical model with additive and multiplicative uncertainty, the proposed method is able to compute a polytopic mRCI with fixed complexity via linear programming (LP). Moreover, the method can be combined with model selection to enable mRCI computation directly from experiment data when the system dynamics are unknown. Specifically, given a model structure, our algorithm begins by identifying the set of admissible models with constraints extracted from the experimental data. Each model in the set of admissible models contains information about the nominal model and the characterization of the model uncertainties. Then, two iterative algorithms based on robust optimization are proposed to compute an mRCI while simultaneously searching for a model ``optimal'' with regard to the mRCI computation and the corresponding invariance-inducing controller. Finally, the method is demonstrated in an experiment with an autonomous vehicle lane-keeping control example.

In this paper, a novel event-sampled robust optimal controller is proposed for a class of continuous-time constrained-input nonlinear systems with unknown dynamics. In order to solve the robust optimal control problem, an online data-driven identifier is established to construct the system dynamics, and an event-sampled critic-only adaptive dynamic programming method is developed to replace the conventional time-driven actor–critic structure. The designed online identification method runs during the solving process and is not applied as a priori part for the solutions, which simplifies the architecture and reduces computational load. The proposed robust optimal control algorithm tunes the parameters of critic-only neural network (NN) by event-triggering condition and runs in a plug-n-play framework without system functions, where fewer transmissions and less computation are required as all the measurements received simultaneously. Based on the novel design, the stability of system and the convergence of critic NN are demonstrated by Lyapunov theory, where the state is asymptotically stable and weight error is guaranteed to be uniformly ultimately bounded. Finally, the applications in a basic nonlinear system and the complex rotational–translational actuator problem demonstrate the effectiveness of the proposed method.

International audience; Based on interval computation, a set‐membership state estimator capable to manage a certain type of outliers in measurements is proposed for uncertain discrete‐time linear systems. To achieve this purpose, two set‐valued filtering techniques are implemented in the presented state estimation algorithm. The setting up of these techniques offers two main advantages. On one hand, the convergence of the estimated state enclosures width is guaranteed and, on the other hand, the algorithm robustness against outliers in data is ensured. That is, unlike former methods, the proposed set‐valued state estimator preserves the framing property despite the presence of some false values in available sensors data. To show the efficiency and the performance of the introduced set‐valued state estimator, it is compared, through two numerical examples, with an optimal interval observer selected from the literature for its high performance.

Vision-aided inertial navigation (VAIN) is a prospective technique for determining the pose of the spacecraft during asteroid missions. The VAIN system can fuse the inertial and visual data by employing the high-degree cubature Kalman filter (HCKF) because it can accurately handle non-linear problems. However, the visual measurements can be corrupted by non-Gaussian noise with unknown time-varying covariance, resulting in severe degradation of the HCKF. To improve the navigational accuracy of the spacecraft in these situations, the authors propose a novel adaptive robust HCKF known as variational Bayesian (VB) adaptive high-degree cubature Huber-based filter (VB-AHCHF). In the novel algorithm, the fifth-degree cubature rule and VB theory are combined to estimate the state and track the non-stationary statistical characteristics of the measurement noise. In addition, utilising the M-estimation, which is defined as the Huber technique, it modifies the update step of the formal Bayesian filtering. Therefore, the VB-AHCHF can exhibit adaptability and robustness to the covariance uncertainty and non-Gaussianity of the measurement noise. Their simulation results show that the estimation accuracy of VB-AHCHF, as well as its adaptability and robustness, is superior to all state-of-the-art algorithms, e.g. HCKF, high-degree cubature Huber-based filter, and the VB adaptive HCKF.

The output of physical systems, such as the scrambled pattern formed by shining the spot of a laser pointer through fog, is often easily accessible by direct measurements. However, selection of the input of such a system to obtain a desired output is difficult, because it is an ill-posed problem; that is, there are multiple inputs yielding the same output. Information transmission through scattering media is an example of this problem. Machine learning approaches for imaging have been implemented very successfully in photonics to recover the original input phase and amplitude objects of the scattering system from the distorted intensity diffraction pattern outputs. However, controlling the output of such a system, without having examples of inputs that can produce outputs in the class of the output objects the user wants to produce, is a challenging problem. Here, we propose an online learning approach for the projection of arbitrary shapes through a multimode fibre when a sample of intensity-only measurements is taken at the output. This projection system is nonlinear, because the intensity, not the complex amplitude, is detected. We show an image projection fidelity as high as ~90%, which is on par with the gold-standard methods that characterize the system fully by phase and amplitude measurements. The generality and simplicity of the proposed approach could potentially provide a new way of target-oriented control in real-world applications when only partial measurements are available. Machine learning has become popular in solving complex optical problems such as recovering the input phase and amplitude for a specific pattern or image measured through a scattering medium. In a more challenging application, Rahmani et al. consider the problem of also producing desired outputs for such a nonlinear system when only some intensity-only measurements of example outputs are available. They develop a neural network approach that can ensure the transmission of images through a highly nonlinear system—a multimode fibre—with a 90% fidelity.

We study residual control charts for the detection of sudden changes in time series with an underlying time-varying trend. Our control charts compare the most recent observations to their direct pr...

Traditionally, hydro turbine governor applications mainly rely on classical proportional–integral–derivative controllers. A classical controller can perform optimally only at the operating point chosen during the controller design. Since hydro power plants are highly non-linear systems alternative control approaches based on adaptive parameters are needed. Historically, due to the limited computation capabilities of microprocessors and programmable logic controllers (PLCs) used in hydro turbine governors, adaptive control schemes were not frequently applied. However, the latest generation of microprocessors and PLCs facilitate the application of adaptive control scheme based on predictive control algorithm for plants with faster dynamic behaviour. In that regard, this study introduces an adaptive controller based on model predictive control (MPC) algorithm developed and applied to a non-linear simulation model of a laboratory hydro power plant. The applied MPC algorithm is based on a linear prediction model whose parameters are identified offline for different operating points across the plant's operating range. The adaptive control scheme updates the prediction model parameters depending on the current operating point. Furthermore, the predictive control algorithm applied in this study is set up as a quadratic programming (QP) optimisation problem that is solved online using a QP solver in a form of Hildreth's algorithm.

In this study a new algorithm is proposed for distributed blind sensor macro-calibration in networked control systems robust to noise. The proposed distributed algorithm for estimation of gain and offset correction parameters is of stochastic approximation type, with local non-linear transformations of residuals. Convergence of the algorithm in mean-square and with probability one to consensus is proved for a large class of non-linear transformations, network properties and communication and measurement noise characteristics. The choice of the introduced non-linear transformations in accordance with the theory of robust statistics leads to the proposal of new calibration algorithms robustified w.r.t. noise. It is demonstrated by Monte Carlo simulation that the proposed algorithms are very efficient in the presence of large outliers from the point of view of both achievement of high convergence rate and adequate values of convergence points, outperforming the existing linear algorithms.

The problem of distributed state estimation for time-varying uncertain systems over a sensor network within the robust Kalman filtering framework is studied. It is assumed that the parameters of the underlying model are subjected to stochastic uncertainties. At the first step, the authors present a particular form of the Kalman filter named information form of a robust Kalman filter. Then, using a consensus scheme that guarantees an agreement in estimation among nodes, they propose a new consensus-based robust filtering approach. The consensus methodology is a hybrid method that is a combination of consensus on information (CI) and consensus on measurement (CM). To this end, before proposing the hybrid consensus filtering algorithm, distributed robust filtering based on CI and CM have been presented. Next, they show that the estimation error in the proposed consensus algorithm is exponentially mean square bounded using a Lyapunov function. Finally, they provide two illustrative examples to show the robust performance and effectiveness of the proposed consensus filtering algorithms.

Quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. We demonstrate that the error of QAOA simulation can be significantly reduced by robust control optimization techniques, specifically, by sequential convex programming (SCP), to ensure error suppression in situations where the source of the error is known but not necessarily its magnitude. We show that robust optimization improves both the objective landscape of QAOA as well as overall circuit fidelity in the presence of coherent errors and errors in initial state preparation.

The existing path tracking methods usually neglect the effect of the drivers on the steering control. This paper proposes a robust steering control method of human-machine steering torque superposition based on linear matrix inequality (LMI) algorithm. First, the model for solving steering superposition torque introduces the steering system and steering resistance torque model in addition to the vehicle model, which increases the nonlinearity and uncertainty of system, and the human in torque superposition control also increases the external interferences. Therefore, this paper proposes a LMI robust control algorithm to reduce the external interference and the influence of uncertain factors on the system and improve the tracking performance of system, by use of Lyapunov stability theory and Schur complement property to convert the region pole assignment and robust control constraint conditions into LMI convex optimization problem. The next, the nonlinear vehicle dynamics solving model including Fiala tire model, steering column model is established; the nonlinear tire model is linearized by use of affine function, and the steering superposition control law is solved by use of LMI. Then, the union CarSim and Simulink simulation is conducted under different situations to verify the robustness and control performance of control system. Finally, through establishing the hardware-in-the-loop experiment table based on LabVIEW-RT, the effectiveness of control strategy is verified. The test results show that the method solves the model uncertainty and the robustness decrement problem resulting from human intervention, ensuring a good tracking performance, and a stable system at the same time.

Robust control of underactuated nonlinear systems is a challenging task using conventional methods because of uncertainties which may lead to failure especially for mechanical and robotic applications. Fuzzy logic controller (FLC) is one of the methods specifically used for designing a robust controller that is able to deal with nonlinearity and achieve satisfactory performance for control applications. FLC uses heuristic information or expert operator knowledge to create its mechanism. However, nonlinearities exist in the control systems make its rule-based FLC design a non-trivial task. Manual trial and error tuning method is common way used for tuning the parameters of FLC to achieve the desired performance of the system. Hence, an automatic tuning using optimization algorithm is necessary. Meta-heuristic optimization algorithms are applied to tune the parameters of FLC automatically. In this paper, three popular meta-heuristic algorithms are used to optimize the scaling factors of FLC, i.e. Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Differential Evolution (DE) to improve the effectiveness of FLC design. A gantry crane system, which is a nonlinear system, is used as test system for this project. The simulation of the optimization of FLC parameters by using meta-heuristics algorithms was successfully achieved. The simulation results show that the optimization techniques for FLC are effective to improve the performance of conventional FLC for gantry crane control, i.e. position control and anti-swing control. In addition, PSO performs better than CS and DE in optimization of FLC for gantry crane system.

In this study, the velocity tracking control of electric multiple units (EMUs) with non-linearity and uncertainty during braking is investigated. A consensus braking algorithm with distributed sliding mode observers is proposed. Firstly, a multi-agent model with non-linear coupling and uncertain external disturbances is established. Secondly, distributed observers are designed, in which a real-time estimation equation for coupling force and disturbances is established based on the equivalent control principle of sliding mode variable structure. Finally, a robust consensus algorithm is proposed to achieve a consensus velocity tracking of each carriage on the target braking curve. The distances between the adjacent carriages are stabilised at stationary distances in a safe range by an artificial potential field function. Experiments show that the algorithm has high accuracy and anti-interference ability in the braking process.

The super-twisting algorithm is a second-order sliding mode control law commonly used for robust control and observation. One of its key properties is the finite time it takes to reach the sliding surface. Using Lyapunov theory, upper bounds for this reaching time may be found. This contribution considers the problem of finding the best bound that may be obtained using a family of quadratic Lyapunov functions. An optimization problem for finding this bound is derived, whose solution may be obtained using semidefinite programming. It is shown that the restrictions imposed on the perturbations and the conservativeness of the obtained bound are significantly reduced compared to existing results from literature.

For the fast and robust control of the delay lines for coherent pulse stacking, we combined the stochastic parallel gradient descent with momentum (SPGDM) and the soft actor-critic (SAC) into a powerful algorithm, SAC-SPGDM. The simulation shows that the algorithm can find the optimal delay-line positions to ensure the 128 pulses are coherently stacked for 7-stage pulses stacking within 25 steps.

To solve the problems of mass parameters and difficulty adjustment of traditional active disturbance rejection controller (ADRC), a novel sliding mode active disturbance rejection control (SM-ADRC) is proposed in this paper. The new control strategy can effectively reduce the number of parameters need to tune, and it is applied to the speed and current loops of PMLSM. In addition, the simulated annealing particle swarm optimization (SAPSO) algorithm is introduced to carry out the off-line optimization study of the parameters of SM-ADRC speed controller to further simplifies the parameter setting process. Simulation results show that the proposed method can not only overcome the difficulty of parameters setting, but will also improve the dynamical performance and steady-state speed tracking precision of PMLSM. Moreover, it enhances robustness to internal and external disturbances and reduces the electromagnetic thrust ripple.

Active disturbance rejection control has multiple advantages such as faster system response, higher accuracy, no excessive requirements on the target model, and suitable for complex and non-linear systems. However, it has the problem of many parameters and difficulty in tuning. A particle swarm optimization algorithm is proposed to optimize the parameters β 1 and β 2 in the nonlinear state error feedback part of the ADRC. This paper compares the simulation effects of PID control, ADRC control and PSO-ADRC control. The simulation results show that ADRC control has better response speed and control accuracy than PID control. Comparing the control results of ADRC before and after optimization, it is found that the optimized controller achieves better control effects.

Interval type-2 fuzzy Markov jump systems (IT2FMJSs) have received much attention because they can better describe complex nonlinear systems with uncertainties and stochastic system mode switching. Over the past decade, many excellent results of fuzzy MJSs (FMJSs) have been reported. However, the transition probabilities which govern the dynamic behaviour of MJSs have been assumed to be completely known, limiting real-world applications of existing results. Different from the previous studies, transition probabilities between system modes switching are partly unknown, and packet dropouts of data transmission are uncertain in this study. The main contributions of this work are: (1) To analyze stochastic stability and reduce conservatism, a novel Lyapunov function which both depends on system mode and fuzzy basis function is constructed; (2) The existence of a mode-dependent and fuzzy-basis-dependent state-feedback controller is investigated; (3) The closedloop system is stochastically stable with a desired H∞ performance, thereby addressing the problem of incomplete transition probabilities and uncertain packet dropouts. An illustrative example of a robot arm is used to demonstrate the effectiveness and practicality of the proposed approach. By virtue of the proposed approach, the effects of incomplete transition probabilities and uncertain packet dropouts on IT2FMJSs are alleviated.

A new data-enabled control technique for uncertain linear time-invariant systems, recently conceived by Coulson et\ al., builds upon the direct optimization of controllers over input/output pairs drawn from a large dataset. We adopt an optimal transport-based method for compressing such large dataset to a smaller synthetic dataset of representative behaviours, aiming to alleviate the computational burden of controllers to be implemented online. Specifically, the synthetic data are determined by minimizing the Wasserstein distance between atomic distributions supported on both the original dataset and the compressed one. We show that a distributionally robust control law computed using the compressed data enjoys the same type of performance guarantees as the original dataset, at the price of enlarging the ambiguity set by an easily computable and well-behaved quantity. Numerical simulations confirm that the control performance with the synthetic data is comparable to the one obtained with the original data, but with significantly less computation required.

Here, the authors propose a novel robust adaptive beamforming method, which is robust to both of the covariance matrix mismatch and the steering vector estimation mismatch. Their method was based on a recently proposed adaptive beamforming technique for large-scale arrays. In this beamforming technique, a sparse sub-array having much smaller number of antenna elements is first selected from the original physical array to perform digital sampling, and then the beamforming coefficients for the original full array are calculated by using only the digital samples from the selected sub-array. In their robust method, they calculate the diagonal loading level automatically by using the spatial-matched method. Then, the steering vector of the desired signal is estimated by maximising the beamformer output power under a constraint that the estimated steering vector does not converge to any of the interference steering vectorsor their linear combinations. Numerical examples are provided to demonstrate the effectiveness of the proposed method against covariance matrix mismatch and steering vector error.

This study addresses the voltage control problem of the medium-voltage distribution systems under uncertainty of the network model. A robust voltage control algorithm (RVCA) is developed in order to manage the voltage constraints considering uncertainties associated with the parameters of load, line, and transformer models. The RVCA determines a corrective solution that remains immunised against any realisation of uncertainty associated with the parameters of the network model. To this end, prior to formulating the voltage control problem, Monte Carlo (MC) simulations are used to characterise uncertain parameters of the network component models and load flow (LF) calculations are carried out to evaluate their impacts. The voltage constraints management under the uncertain environment is then formulated as a robust optimisation (RO) problem. The latter is constructed based on the results obtained through the MC simulations and LF calculations. Once the RO is solved, in order to check the robustness of the solution, system voltages are evaluated using the LF calculations considering the new set-points of control variables and uncertainty of network parameters. The simulation results reveal that neglecting model uncertainty in the voltage control problem can lead to infeasible solutions while the proposed RVCA, at an extra cost, determines a corrective solution which remains protected against the studied uncertainties.

Overlay errors depict misalignment between neighboring pattern layers on the wafer and are a critical measure of quality in lithography processes. In addition, stack-up overlay errors, which describe misalignments across non-neighboring layers, have also been shown to be important for product performance. Models relating overlay errors to tool parameters are the foundation of control methods that utilize in-process error measurements to adapt controllable parameters to minimize those errors. Unfortunately, these models are subject to noise and modeling errors whose characteristics cannot be accurately modeled, in that assumptions of Gaussianity or independence of noise terms, or perfect knowledge of model parameters cannot be ascertained. This paper proposes a methodology for robust control of lithography errors that is capable of handling parametric uncertainties in overlay models, as well as shortcomings in describing the noise and modeling errors. A series of simulations was conducted using a model available in the literature, as well as a model and data from a major 300 mm semiconductor fab. The results illustrate advantages of the proposed robust approach, with benefits increasing with higher levels of uncertainties. The results also depict specific improvements of errors in outer regions of the wafer, which are known to suffer from lower yields.

The research deals with the methodology intended to root robust quality indices in the interval control system, the parameters of which are affinely included in the coefficients of a characteristic polynomial. To determine the root quality indices we propose to depict on the root plane not all edges of the interval parametric polytope (as the edge theorem says), but its particular vertex-edge route. In order to define this route we need to know the angle sequence at which the edge branches depart from any integrated pole on the allocation area. It is revealed that the edge branches can integrate into the route both fully or partially due to intersection with other branches. The conditions which determine the intersection of one-face edge images have been proven. It is shown that the root quality indices can be determined by its ends or by any other internal point depending on a type of edge branch. The conditions which allow determining the edge branch type have been identified. On the basis of these studies we developed the algorithm intended to construct a boundary vertex-edge route on the polytope with the interval parameters of the system. As an illustration of how the algorithm can be implemented, we determined and introduced the root indices reflecting the robust quality of the system used to stabilize the position of an underwater charging station for autonomous unmanned vehicles.

In this paper, a robust super-twisting sliding mode control with adaptive tuning law is developed for a nonlinear biotechnological process, which takes place inside a continuous stirred tank bioreactor. A super-twisting algorithm (STA) is firstly designed to obtain high robustness as well as preserve fast convergence with high accuracy. The benefit of this approach is that its design procedure is independent of the prior knowledge of the bound value of the uncertainties and perturbations. However, the STA has a drawback that provides a chattering in the control loop. In order to overcome this drawback, an adaptive tuning algorithm is developed to adjust the STA control law without frequency switching and alleviate the undesired chattering phenomenon. Then, the robustness can be achieved despite the existence of the unknown uncertainties and external perturbations for the nonlinear process. In addition, a formal proof of the global uniform asymptotic stability based on Lyapunov criterion of the closed-loop process is derived. Several simulation results show that the proposed adaptive super-twisting algorithm guarantees the performance of the STA under external disturbance and parametric uncertainty with less chattering and illustrate the overall performance improvements.

Referring to the fact that the $$\varvec{n}$$ th links bipedal walking robot has high nonlinearity and uncertainty parameters; therefore robust controllers for walking robot should be properly designed. This paper proposes a new robust control scheme based on fuzzy system and Ant Colony Optimization algorithm. Adaptive fuzzy controllers (AFCs) based on Ant Colony Optimization (ACO) algorithm are proposed to eliminate the chattering phenomenon that occurs when the walking robot moves on rough surfaces. Six rotational joints are used to connect the seven links of the bipedal walking robot. Those joints are assumed frictionless and actuated by six independent servomotors. Therefore, six adaptive fuzzy controllers are designed in this work (one for each joint). To design robust fuzzy controllers, the Ant Colony Optimization algorithm is utilized to tune and find the best parameters of the output membership function of the fuzzy controller. For comparison purposes, optimal PID controllers (OPIDCs) are designed with optimal parameters that are chosen by utilizing the Ant colony algorithm. The performances of the two proposed controllers (AFCs and OPIDCs) are tested under significant disturbances situations such as carrying different weighted things by the walking biped robot. In addition, the stability of the adaptive fuzzy controller is studied and proved by applying Lyapunov theory.

Global hydropower growth continues to increase with a relative higher rate of total installed capacity in just last 10 years. This expansion means that it is increasingly important to make a further research with respect to various hydropower stations. As the core of hydropower stations, hydraulic turbine regulating system (HTRS) attracts many attentions. In essence, HTRS is a complex nonlinear system that governs the frequency and the electrical power output of hydroelectric unit. The design of the control laws for the HTRS is an important and difficult task. In this study, a hybrid imperialist competitive algorithm (HICA) for the dynamic model of HTRS system is proposed and applied to estimate the parameters of fuzzy sliding mode controller (FSMC). In the proposed approach, sliding mode controller (SMC) is regarded as a robust control technique to the external uncertain load disturbances and fuzzy logic rule provides a better proportional gain and reduces the inherent chattering effect of the SMC controller. The HICA is developed to search optimal values of the control law and the membership functions of fuzzy logic rules. Simulations are carried out to verify the effectiveness of proposed approach, where the results show that compared with parallel PID controller and conventional SMC controller, the designed FSMC controller performs much better in terms of system performance and chattering reduction. Also, the results certify the superiority of the HICA algorithm in estimating the parameters for the proposed controller of HTRS in comparison to other classical evolutionary algorithms, where HICA reduces the value of the objective function by 3.28%, 5.33% and 9.69% compared with ICA, BSA, and PSO under unload condition, and HICA reduces the value of the objective function by 3.69%, 4.01% and 10.70% compared with ICA, BSA, and PSO under load condition.

© 2018 Elsevier Ltd An extension of the well-known delayed resonator concept is proposed with the objective to enhance its robustness in vibration suppression under guaranteeing stability of the overall set up. The novel resonator feedback is based on an acceleration measurements passing through a delay of polynomial distribution. The feedback design is formulated as an optimization problem over the parameter set formed by the polynomial coefficients and the gain of the delay free part. The first objective is to minimize the sensitivity of the resonator performance with respect to variations of the excitation frequency, i.e. to widen its frequency stop-band centered at the nominal excitation frequency. The second, equally significant objective is to guarantee that this does not lead to instability of the overall mechanical system coupled with the absorber and to ensure sufficient stability margin. The arising non-convex and non-smooth optimization problem is widely discussed and a penalty method is developed, where the unconstrained problem in every iteration step is solved using available software tools for optimization and spectral design of time delay systems. The proposed design technique is validated by simulations for two case study examples. ispartof: Journal of Sound and Vibration vol:443 pages:576-590 status: published

An active disturbance rejection control (ADRC) of induction motor based on an adaptive particle swarm optimization (APSO) algorithm is proposed in this paper, in order to realize the precise decoupling of induction motor and the disturbance compensation. The novel control method employs APSO as the automatic tune mechanism for ADRC controller. According to the feedback information of induction motor, an optimal solution can be achieved via the optimization mechanism and self-learning ability of APSO, so the reliance of ADRC controller on parameters can be reduced. In order to obtain the better optimization solution more efficient, the aggregation degree and the evolution speed are introduced into the APSO to dynamically modify the inertia weight based on the practical optimization process. Experimental results indicate that the robustness of the proposed optimal design method for ADRC is better than the conventional ADRC when the disturbances occur, and the method is feasible and effective.

Optimization is at the core of control theory and appears in several areas of this field, such as optimal control, distributed control, system identification, robust control, state estimation, model predictive control and dynamic programming. The recent advances in various topics of modern optimization have also been revamping the area of machine learning. Motivated by the crucial role of optimization theory in the design, analysis, control and operation of real-world systems, this tutorial paper offers a detailed overview of some major advances in this area, namely conic optimization and its emerging applications. First, we discuss the importance of conic optimization in different areas. Then, we explain seminal results on the design of hierarchies of convex relaxations for a wide range of nonconvex problems. Finally, we study different numerical algorithms for large-scale conic optimization problems.

Aim. This work presents the amelioration of direct power control using synergetic-super twisting algorithms for asynchronous generators integrated into dual-rotor wind turbine systems. Method. The main role of the direct power control is to control the active and reactive powers and reduce the harmonic distortion of stator current of asynchronous generator for variable speed dual-rotor wind turbine systems. The traditional strategy is more attractive due to its high efficiency and simple algorithm. Super twisting algorithms are a non-linear command strategy; characterized by robustness against the parameters change or disturbances, it gives a good power quality under different conditions such as changing generator parameters. Novelty. Synergetic-super twisting algorithms are designed. Synergetic-super twisting algorithms construction is based on synergetic command and super twisting algorithms in order to obtain a robust control strategy and a fast system with acceptable precision. We use in our study a 1.5 MW asynchronous generator integrated to dual-rotor wind turbine system in order to regulate the active and reactive powers. Results. As shown in the results figures using synergetic-super twisting algorithms the ameliorate performances especially minimizes the torque, active and reactive power undulations, and reduces harmonic distortion of stator current (THD = 0.19 %) compared to traditional strategy., Мета. Робота представляє вдосконалення безпосереднього регулювання потужності за допомогою синергетичих алгоритмів супер-скручування для асинхронних генераторів, інтегрованих у системи вітряних генераторів з подвійним ротором. Метод. Основна роль безпосереднього регулювання потужності полягає у керуванні активною та реактивною потужностями та зменшенні гармонічних спотворень струму статора асинхронного генератора для вітряних генераторів з подвійним ротором зі змінною швидкістю обертання. Традиційна стратегія є більш привабливою завдяки її високій ефективності та простому алгоритму. Алгоритми супер-скручування – це нелінійна командна стратегія; характеризується стійкістю до зміни параметрів або порушень, це забезпечує хорошу якість енергії в різних умовах, таких як зміна параметрів генератора. Новизна. Розроблені синергетичні алгоритми супер-скручування. Побудова алгоритмів синергетичного супер-скручування базується на алгоритмах синергетичних команд та супер-скручування, для того щоб отримати надійну стратегію керування та швидку систему з прийнятною точністю. У нашому дослідженні ми використовуємо асинхронний генератор потужністю 1,5 МВт, інтегрований в систему вітряних турбін з подвійним ротором для регулювання активної та реактивної потужностей. Результати. Як показано на рисунках з результатами, із використанням алгоритмів синергетичного супер-скручування, покращені характеристики особливо мінімізують крутний момент, коливання активної та реактивної потужності та зменшують гармонічні спотворення струму статора (THD = 0,19%) порівняно з традиційною стратегією.

Sliding mode control (SMC) assures finite time convergence of sliding variables, precise performance of system and robustness with respect to internal and external disturbances during sliding. SMC is associated with chattering due to discontinuous term. Use of higher-order sliding mode (HOSM) leads to spontaneous attenuation of the chattering effect and retain its finite time convergence property. In this manuscript, new continuous control structure is proposed leading to second-order modes. This algorithm gives finite time convergence and continuous control for both relative degree one and relative degree two surfaces. The investigated algorithm is validated in simulation for motion control system.

This paper proposes an adaptive MIMO fuzzy controller optimized with Jaya optimization technique used to robust control of uncertain coupled tank system. Firstly, the parameters of MIMO fuzzy controller are optimally identified using Jaya algorithm. Then a fuzzy sliding surface is implemented to ensure that the coupled tank system is asymptotically stable based on Lyapunov concept. The proposed algorithm is applied to control the fluid level of double tank systems. The comparison results with PID and traditional fuzzy controller are presented to confirm that the new Adaptive MIMO Fuzzy control method proves a robust and simple approach to effectively control highly nonlinear uncertain systems.

International audience; The problem of robust fault estimation (FE) for a class of nonlinear systems in discrete-time domain is studied via proposing a comprehensive partition-based switching scheme. Both enhancement factors and attenuation factors are designed synchronously for accomplishing a comprehensive partition of the entire space spanned by the considered normalized fuzzy weighting functions. Thanks to the proposed comprehensive partition-based switching scheme, six subspaces would be obtained by identifying the real-time permutation order of the underlying parameters. Therefore, a novel fuzzy FE observer with six kinds of switching modes can be on-line implemented. As a result, the robust H∞ performance index can be enhanced since much more information is employed than those reported results. Finally, simulation comparisons over the existing results are provided to show the obtained advantages of the article.

This paper considers quantization and channel coding for control systems using rate limited digital communication channels. We design quantization and channel coding to make the networked control system robust to quantization and bit errors. A numerical example is provided to show that our design is robust to bit errors at low SNR, which is achieved at a small loss of the mean squared error at middle SNR.