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In practice, many physical systems, including physiological ones, can be considered whose input can take only positive quantities. However, most of the conventional control methods do not support the positivity of the main input data to the system. Furthermore, the parameters of these systems, similar to other non-linear systems, are either not accurately identified or may change over time. Therefore, it is reasonable to design a controller that is robust against system uncertainties. A robust positive-input control method is proposed for the automatic treatment of targeted anti-angiogenic therapy implementing a recently published tumour growth model based on experiments conducted on mouse models. The backstepping (BS) approach is applied to design the positive input controller using sensory data of tumour volume as feedback. Unlike previous studies, the proposed controller only requires the measurement of tumour volume and does not require the measurement of inhibitor level. The exponential stability of the controlled system is proved mathematically using the Lyapunov theorem. As a result, the convergence rate of the tumour volume can be controlled, which is an important issue in cancer treatment. Moreover, the robustness of the system against parametric uncertainties is verified mathematically using the Lyapunov theorem. The real-time simulation results-based (OPAL-RT) and comparisons with previous studies confirm the theoretical findings and effectiveness of the proposed method., (© 2023 The Authors. IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

This paper introduces a nonlinearity compensation based robust tracking controller for hypersonic flight vehicles. The controller focuses on robust control design with respect to the nonminimum phase feature and system uncertainties for the control-oriented model. Firstly, following the principle of decomposing the complex control problem into two simpler problems, the original robust tracking problem for nonlinear nonminimum phase hypersonic flight vehicles is decomposed into a simpler robust tracking problem for a linear nonminimum phase system with disturbances and a stabilization problem for a nonlinear system without disturbances. After the problem decomposition, a proportional-integral tracking controller and a feedback linearization controller are designed for the linear system and the nonlinear system, respectively. Then, the addition of the two designed controllers gives the final controller. By compensating for the system nonlinearity using the secondary system and its controller, the proposed control method can satisfy the robust tracking control requirements for nonlinear nonminimum phase hypersonic flight vehicles. The simulation results show, compared with a feedback linearization control method and a composite neural learning control method, the proposed method has superior tracking accuracy and robustness against system uncertainties and external disturbances., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper presents a tracking controller for nonlinear systems with matched uncertainties based on contraction metrics and disturbance estimation that provides exponential convergence guarantees. Within the proposed approach, a disturbance estimator is proposed to estimate the pointwise value of the uncertainties, with a pre-computable estimation error bounds (EEB). The estimated disturbance and the EEB are then incorporated in a robust Riemannian energy condition to compute the control law that guarantees exponential convergence of actual state trajectories to desired ones. Simulation results on aircraft and planar quadrotor systems demonstrate the efficacy of the proposed controller, which yields better tracking performance than existing controllers for both systems.

To address the difficulty of optimizing turbidity process with large inertia and long time delay, a twice-optimal control and construction pruning (TOCCP) strategy is proposed, which can be used to improve the fast stability and strong robustness of industrial process. The new approach owns the advantages of twice optimal control (TOC) and construction pruning (CP) method. The TOC constructs an infinite-dimensional state observer of the system, which can eliminate the influence of time delay on the system. The CP is adopted to improve the mid-band damping of the open-loop frequency characteristics to improve the robustness of the system. Moreover, an exhaustive algorithm is designed to find the optimal time scale, the most important parameter for adjusting the system response speed. In addition, the influences of different parameter changes on the robustness of the system are analyzed, and a practical parameter tuning formula is provided. Simulation-based comparisons of TOCCP and the other two algorithms were made. The results show that the system optimized by TOCCP has strong robustness, especially in disturbances rejection and set-point tracking. Moreover, TOCCP is practical and easily implemented to set parameters, providing guidelines for industrial application of large inertia and time-delay systems., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

Disturbances in the aviation environment can compromise the stability of the aviation optoelectronic stabilization platform. Traditional methods, such as the proportional integral adaptive robust (PI + ARC) control algorithm, face a challenge: once high-frequency disturbances are introduced, their effectiveness is constrained by the control system's bandwidth, preventing further stability enhancement. A state equalizer speed closed-loop control algorithm is proposed, which combines proportional integral adaptive robustness with state equalizer (PI + ARC + State equalizer) control algorithm. This new control structure can suppress high-frequency disturbances caused by mechanical resonance, improve the bandwidth of the control system, and further achieve fast convergence and stability of the PI + ARC algorithm. Experimental results indicate that, in comparison to the control algorithm of PI + ARC, the inclusion of a state equalizer speed closed-loop compensation in the model significantly increases the closed-loop bandwidth by 47.6%, significantly enhances the control system's resistance to disturbances, and exhibits robustness in the face of variations in the model parameters and feedback sensors of the control object. In summary, integrating a state equalizer speed closed-loop with PI + ARC significantly enhances the suppression of high-frequency disturbances and the performance of control systems. [ABSTRACT FROM AUTHOR]

Redundancies in modern systems, including multiple channels, processes, and storages, are often exploited to ensure robust operation. Similarly, a Networked Control System (NCS) may utilize multiple channels to facilitate reliable information transfer in case of channel failure. To enhance the performance of Linear Quadratic Gaussian (LQG) control in environments with multiple channels, delays, and packet errors, we propose channel-switching algorithms. Leveraging the encoder and decoder structure for channel modeling, we derive the decoder estimation error covariance matrix, characterizing LQG control performance with respect to delay. Based on this insight, we develop two threshold-based channel-switching algorithms, proven to ensure finite total decoder estimation error variance under certain conditions. Specific conditions are also identified where the proposed algorithms offer improved probabilistic stability. Numerical simulations confirm the superior performance of the proposed algorithms compared to conventional methods across diverse channel environments. Notably, the proposed algorithms demonstrate near-optimal performance in a practical operational scenario involving multiple channels, specifically 5G cellular link and Starlink. [ABSTRACT FROM AUTHOR]

This paper provides a new method to nonlinear control theory, which is developed from the eigenvalue assignment method. The main purpose of this method is to locate the pointwise eigenvalues of the linear-like structure built by freezing the nonlinear systems at a given time instant in a desired disk region. Since the control requirements for the transient response characteristics are the major constraints on the selection of the disk centre and radius, two different update algorithms are also developed to reshape the disk region by changing the disk centre and radius at each time step. The effectiveness of the proposed methods is tested in both simulations and experiments. A validated three-DOF laboratory helicopter is used for experiments. [ABSTRACT FROM AUTHOR]

The diverse application vertices of internet-of-things (IoT) including internet of vehicles (IoV), industrial IoT (IIoT) and internet of drones things (IoDT) involve intelligent communication between the massive number of objects around us. This digital transformation strives for seamless data flow, uninterrupted communication capabilities, low latency and ultra-high reliability. The limited capabilities of fifth generation (5G) technology have given way to sixth generation (6G) wireless technology. This paper presents a dynamic cell-free framework for a 6G-enabled IoT network. A number of access points (APs) are distributed over a given geographical area to serve a large number of user nodes. A pilot-based AP selection (PBAS) algorithm is proposed, which offers robust resource control through AP selection based on pilots. Selecting a subset of APs against all APs for each user node results in improved performance. In this paper, the performance of the proposed transmission model is evaluated for the achieved data rate and spectral efficiency using the proposed algorithm. It is shown that the proposed PBAS algorithm improves the spectral efficiency by 22 % at the cell-edge and 1.5 % at the cell-center. A comparison of the different combining techniques used at different user locations is also provided, along with the mathematical formulations. Finally, the proposed model is compared with two other transmission models for performance evaluation. It is observed that the spectral efficiency achieved by an edge node with the proposed scheme is 5.3676 bits/s/Hz, compared to 0.756 bits/s/Hz and 1.0501 bits/s/Hz, attained with transmission schemes 1 and 2, respectively. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing demand for efficient and robust control algorithms to optimize the performance of autonomous systems. Traditional control techniques often struggle to handle the complexity and uncertainty associated with such systems. To address these challenges, hybrid neuro-fuzzy-genetic algorithms have emerged as a promising approach. This paper presents a comprehensive review of the application of hybrid neuro-fuzzy-genetic algorithms for optimal control of autonomous systems. The proposed algorithms combine the strengths of neural networks, fuzzy logic, and genetic algorithms to achieve adaptive and optimal control in real-time scenarios. The neurofuzzy component provides the ability to model and handle complex and uncertain systems, while the genetic algorithm component facilitates the optimization of control parameters. The combination of these techniques enables autonomous systems to adapt and optimize their control strategies based on changing environments and objectives. The paper discusses the underlying principles of hybrid neuro-fuzzy-genetic algorithms, their advantages, and challenges. It also provides a review of the state-of-the-art research in this field, highlighting successful applications and potential future directions. Overall, the integration of neuro-fuzzy-genetic algorithms in autonomous systems holds great promise for achieving optimal control in various domains, including robotics, aerospace, and autonomous vehicles. [ABSTRACT FROM AUTHOR]

This work is trying to introduce a fractional order floated pole controller as a fast and robust approach. We designed a robust variable structure control that yields a continuous and constrained control signal, also a fast response in the presence of model uncertainties and external disturbances. In the proposed controller, we employed the pole placement algorithm, then by designing proper polynomials gave it robust property, then due to a simple optimization routine, we make it fast and faster within the stability region. Finally, to evaluate the proposed method, numerical examples in different situations of the presence of noise, disturbance, and model uncertainties, also comparative results are presented. This paper proposed an accurate, fast, and robust controller. This can improve the performance of the perturbed functional systems used in the industrial fields. It is proposed to spread the benefit of fractional calculus in the control of complex systems in practical situations. [ABSTRACT FROM AUTHOR]

Any mismatch of power demand and power generation in an interconnected electrical power system causes deviances in tie-line power and frequencies. To overcome this issue, an automatic generation control system equipped with intelligent controllers is used in the power system. This study presents a maiden write-up on a two-d egree -of-f reedo m fractional order -fuzz y-pro porti onal-integ ral-d eriva tive (2DOF-FO-FuzzyPID) controller employed in an interconnected system with different nonlinearities. The controller proposed along with conventional controllers has its gains optimally enumerated by the application of modified whale optimization algorithm (MWOA). Besides this, the potency of the MWOA algorithm over WOA algorithm is examined through some popular benchmark functions. The superior transient response yielded by 2DOF-FO-FuzzyPID controller over PID and fractional-order propo rtion al-in tegra l-der ivati ve controllers of the proposed test system is evaluated. Further, the effectiveness of the projected controller is evaluated by taking the system's nonlinearities and abrupt load perturbation, which acknowledges the robustness of the controller. In spite of these attributes, the stability and relative stability of the 2DOF-FO-FuzzyPID controller is determined in the frequency domain. [ABSTRACT FROM AUTHOR]

This paper proposes a Hybrid Backstepping Super Twisting Algorithm for robust speed control of a three-phase Induction Motor in the presence of load torque uncertainties. First of all, a three-phase squirrel cage Induction Motor is modeled in MATLAB/Simulink. This is then followed by the design of different non-linear controllers, such as sliding mode control (SMC), super twisting SMC, and backstepping control. Furthermore, a novel controller is designed by the synergy of two methods, such as backstepping and super twisting SMC (Back-STC), to obtain the benefits of both techniques and, thereby, improve robustness. The sigmoid function is used with an exact differentiator to minimize the high-speed discontinuities present in the input channel. The efficacy of this novel design and its performance were evidenced in comparison with other methods, carried out by simulations in MATLAB/Simulink. Regression parameters, such as ISE (Integral Square error), IAE (Integral Absolute error) and ITAE (Integral Time Absolute error), were calculated in three different modes of operation: SSM (Start-Stop Mode), NOM (Normal Operation Mode) and DRM (Disturbance Rejection Mode). In the end, the numerical values of the regression parameters were quantitatively analyzed to draw conclusions regarding the tracking performance and robustness of the implemented non-linear control techniques. [ABSTRACT FROM AUTHOR]

An improved robust predictive control method is proposed for a class of multicellular descriptive LPV systems with bounded state disturbances, and an output feedback controller is designed to ensure the asymptotic stability of the systems. To offset the bounded state disturbance, the controller considered the undisturbed LPV system, and based on the off-line state observer, the LMI is used to solve the minimum-maximum optimization problem in the infinite time domain of predictive control. Then the off-line state observer is used to obtain the difference between the estimated values of the disturbed LPV system and the undisturbed LPV system, and the feedback gain of the guaranteed performance is determined, so as to obtain the optimal offset that makes the disturbed LPV system asymptotically stable, which is combined with the undisturbed system control law as the optimal control law and applied to the actual system. The experimental results show that the improved robust predictive control method can obtain better control performance, and improve the stability of the system and the efficiency of solving optimization problems. Simulation experiments also verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles. [ABSTRACT FROM AUTHOR]

A decentralized PI/PID controller based on the frequency domain analysis for two input two output (TITO) coupled tank systems is exploited in this paper. The fundamentals of the gain margin and phase margin are used to design the proposed PI/PID controller. The basic objective is to keep the tank at the predetermined level. To satisfy the design specifications, the control algorithm is implemented for decoupled subsystems by employing a decoupler. First-order plus dead time (FOPDT) models are obtained for the decoupled subsystems using the model-reduction technique. In addition, the control law is realized by considering the frequency domain analysis. Further, the robustness of the controller is verified by considering multiplicative input and output uncertainties. The proposed method is briefly contrasted with existing techniques. It is envisaged that the proposed control algorithm exhibits better servo and regulatory responses compared to the existing techniques. [ABSTRACT FROM AUTHOR]

One of the challenges facing robot tracking is the difficulty of controlling variety types of robots in known and unknown environments. This paper proposes a new strategy of path tracking for a non-holonomic mobile robot subject to kinematic disturbances using model prediction control and H 2 / H ∞ control by Gain-scheduled control law. In this regard, the problem of tracking a mobile robot is expressed in a way that its kinematic error is in the form of linear parameter variable with bounded disturbance. The purpose of the design is to stabilize the controller by solving the linear matrix inequality (LMI) conditions for error tracking model. The following stability conditions are then formulated in such a way that the upper bounds are considered as LMI auxiliary variables. Also with the help of more free matrices, it can reduce conservatism. The target tracking is first designed with the assumption that there is no obstacle in the environment, and then the obstacle avoidance mode is added to the tracer controller, which is done by modifying the optimal state vector definition. In both cases, the system stability check is also performed. Simulation is performed on several predetermined paths, indicating that this strategy is feasible. [ABSTRACT FROM AUTHOR]

To improve the probability of missile coordinated detection, this paper investigates the control method of multimissile cooperative formation. Based on the missile's nonlinear dynamics modeling, the formation control algorithm based on the position of the leader missile (or virtual leader missile) is studied. Meanwhile, considering the influence of the communication time delay, a robust adaptive cooperative controller satisfying the time delay network is designed. The simulation verification result indicates that cooperative formation can be achieved quickly with small velocity and position tracking errors when the missiles are fully connected. Therefore, the multimissile cooperative formation control algorithm designed in this paper can meet the requirements of multimissile coordinated detection, thus improving the missile's cooperative attack capability. [ABSTRACT FROM AUTHOR]

Quantum error-mitigation techniques can reduce noise on current quantum hardware without the need for fault-tolerant quantum error correction. For instance, the quasiprobability method simulates a noise-free quantum computer using a noisy one, with the caveat of only producing the correct expected values of observables. The cost of this error mitigation technique manifests as a sampling overhead which scales exponentially in the number of corrected gates. In this work, we present an algorithm based on mathematical optimization that aims to choose the quasiprobability decomposition in a noise-aware manner. This directly leads to a significantly lower basis of the sampling overhead compared to existing approaches. A key element of the novel algorithm is a robust quasiprobability method that allows for a tradeoff between an approximation error and the sampling overhead via semidefinite programming. [ABSTRACT FROM AUTHOR]

Current control laws for active control of helicopter structural vibration are designed for steady-state flight conditions, while the vibration response of maneuvering flight has not been taken into consideration yet. In order to obtain full-time vibration suppression capability, the authors propose a filtered least mean square-mixed sensitivity robust control method based on reference signal reconstruction (LMS-MSRC), driving piezoelectric stack actuators to suppress helicopter structural vibration response in maneuvering flight. When feedback controller designed by H ∞ theory is implemented, active damping is added on the secondary path to weaken the adverse effects of its sudden changes in maneuvering flight state. Furthermore, a reference signal reconstruction scheme is given concerning equivalent secondary path. In addition, the reconstruction accuracy, the convergence speed, stability, and global validity of the hybrid controller are analysed. Compared with multichannel Fx-LMS, numerical simulations of LMS-MSRC for vibration suppression are undertaken with a helicopter simplified finite element model under several typical flight conditions. Further experiments of real-time free-free beam vibration control are performed, driven by a stacked piezoelectric actuator. The instantaneous overshoot of measured response is 42% less than the peak value and its attenuation reaches 85% within 2.5 s. Numerical and experimental results reveal that the proposed algorithm is practical for suppressing transient disturbance and multifrequency helicopter vibration response during maneuvering flight with faster convergence speed and better robustness. [ABSTRACT FROM AUTHOR]

A decentralized closed-loop optimization based on the difference of convex programming is proposed to assemble large telescopes autonomously in-orbit. This study focuses on assembly problems of mirror satellites with six degrees of freedom. Firstly, the h p -Radau pseudospectral collocation method is adopted to increase computational efficiency. Subsequently, the nonconvex constraints are convexified by applying undominated decomposition and sequential convex programming. Additionally, the fixed trajectory assumption enables the translational optimization problem to be a decentralized problem with convergence-guaranteed proof. The six degrees of freedom optimization problem is decoupled by utilizing the proposed algorithm in translational and rotational planning, and coupled by thruster allocation method. To improve the robustness of the system, the algorithm is wrapped in the model predictive control framework, and the recursive feasible property is ensured using the outer-bounding tube. Therefore, the closed-loop optimal control is robust under additive uncertainties. In the numerical experiments, mirror satellites are released from a high-accuracy fitting carrier satellite to accomplish the assembly, and these satellites are utilized to verify the high-speed algorithm and the effectiveness of the closed-loop optimization. • A convergence-guaranteed difference of convex algorithm is proposed. • Collision avoidance constraints are modeled as high-order multivariate polynomials. • Algorithm is validated using 6DOF spacecraft assembly problem. • Rapidity and optimal nature of the algorithm are demonstrated by numerical examples. • Recursive feasibility model predictive guidance is proved to be robust. [ABSTRACT FROM AUTHOR]

This article refocuses on the global coordination of networked systems with input saturation and external disturbances. With the help of a novel backstepping approach, a multihop relay control algorithm subject to a group of modified heterogenous saturation functions is constructed, in which the number of the hop is equal to the order of the networked system. It is proved that these modified heterogenous saturation functions can develop a global unsaturated control algorithm for each agent. Then, some sufficient and necessary conditions are provided for the global consensus of the connected networked systems with input saturation in the absence of external disturbances. In the presence of the input saturation and external disturbances, a sufficient condition irrelevant to the interactive network topology information is proposed for the global swarm of connected networked systems. Finally, numerical simulations are provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]

Compared to control bandwidth, low‐frequency uncertainties or disturbances like step signals can be well rejected by many methods having two‐degree‐of‐freedom (2‐DOF). Due to robustness constraint, technically more challenging is the rejection of medium frequencies, especially for bandwidth‐limited systems. Here, the equivalent‐input‐disturbance (EID) approach is extended to deal with the main medium‐frequency oscillation of a pantograph‐catenary system. First, a general EID estimator is developed with a low‐frequency estimator as a special case. Then, a fair comparison is conducted to clarify the essential differences between the conventional 1‐DOF‐based and the developed 2‐DOF‐based control systems. Furthermore, a robust stability condition is derived for the 2‐DOF‐based closed‐loop control system. A design algorithm together with design guidelines is provided, where the frequency characteristics of the uncertainties are utilized in the parameter design. Finally, simulations are carried out to validate the developed 2‐DOF‐based method for the pantograph‐catenary system in realistic environment. [ABSTRACT FROM AUTHOR]

This paper presents a low-cost Brushless DC (BLDC) motor drive system with fewer switches. BLDC motors are widely utilized in variable speed drives and industrial applications due to their high efficiency, high power factor, high torque, low maintenance, and ease of control. The proposed control strategy for robust speed control is dependent on two feedback signals which are speed sensor loop which is regulated by Sliding Mode Controller (SMC) and current sensor loop which is regulated by Proportional-Integral (PI) for boosting the drive system adaptability. In this work, the BLDC motor is driven by a four-switch three-phase inverter emulating a three-phase six switch inverter, to reduce switching losses with a low complex control strategy. In order to reach a robust performance of the proposed control strategy, the Lévy Flight Distribution (LFD) technique is used to tune the gains of PI and SMC parameters. The Integral Time Absolute Error (ITAE) is used as a fitness function. The simulation results show the SMC with LFD technique has superiority over conventional SMC and optimization PI controller in terms of fast-tracking to the desired value, reduction speed error to the zero value, and low overshoot under sudden change conditions. [ABSTRACT FROM AUTHOR]

Safe operation of the power grid requires a complete and robust control network to ensure full observability. However, redundancy measurements can create problems in expense, management, and control. The optimal phasor measurement unit (PMU) positioning problem (OPPP) is proposed to limit the number of PMUs deployed in the power grid and ensure the whole grid's observability in the meantime. A modified branch‐and‐bound algorithm (MBBA) to unravel the OPPP is presented. Original BBA, which uses a single search order to create a binary tree, gives only one solution to the OPPP, although more than one optimal solution exists. The proposed MBBA method consists of two different stages: the vertexes in the search tree are investigated by depth‐first search (DFS) in stage 1, and the search route continues as the breadth‐first search. In stage 1, the LP relaxing problems are solved by dual simplex, and in stage 2, the basic viable solution from stage 1 is used to configure the primary simplex until the optimum solution is found. OPPP is formulated as a binary decision variable MBBA model, minimizing linear objective function subject to linear matrix observability constraints. The MBBA model is unravelled using a linear integer‐based external approximation scheme. IEEE test systems are used to check the feasibility of the proposed approach. Matlab software performs simulation based on a number of graph theory‐based methods such as DFS, graph‐theoretical method, simulated annealing, and recursive N‐algorithms. These algorithms are compared to the algorithmic perspective of the proposed MBBA method. IEEE test network results confirm the validity of the proposed methodology. [ABSTRACT FROM AUTHOR]

Model uncertainties are usually unavoidable in the control systems, which are caused by imperfect system modeling, disturbances, and nonsmooth dynamics. This paper presents a novel method to address the robust control problem for uncertain systems. The original robust control problem of the uncertain system is first transformed into an optimal control of nominal system via selecting the appropriate cost function. Then, we develop an adaptive critic leaning algorithm to learn online the optimal control solution, where only the critic neural network (NN) is used, and the actor NN widely used in the existing methods is removed. Finally, the feasibility analysis of the control algorithm is given in the paper. Simulation results are given to show the availability of the presented control method. [ABSTRACT FROM AUTHOR]

This article proposes a novel adaptive backstepping hierarchical sliding mode control with balance and transfer for a ridable ballbot. The algorithm is applied to the dynamics of coupling two vertical planes. The dynamic model is a multiple-input–multiple-output underactuated system. Based on the backstepping and hierarchical sliding mode schemes, a robust nonlinear control law is obtained to govern the balance and transfer of the ridable ballbot; this scheme considers input constraints as well as matched and mismatched uncertainties. The influence of actuator saturation is examined by proposing an auxiliary system. The stability of the whole system is guaranteed via Lyapunov theory and Barbalat’s lemma, which shows that the switching gains are limited by the upper bound of the matched uncertainty. An adaptive law is proposed to estimate the matched uncertainty using radial bias function networks. As a result, the robustness is enhanced, and the constraints of the switching gains on the matched uncertainty can be relaxed. The stabilizing and tracking problems of the ridable ballbot are investigated through comparative simulations. The results demonstrate the effectiveness and robustness of the proposed control algorithm in the presence of parameter variations, external disturbances, and input saturation. [ABSTRACT FROM AUTHOR]

In the underwater environment, robust control algorithms are required to control autonomous underwater vehicles (AUVs) at high speed while preventing large nonlinearities and disturbances. Sliding mode control (SMC) is a well-known robust control theory and has been widely used not only in AUV control but also in systems such as industrial robots which have high nonlinearity in their system dynamics. However, SMC has the disadvantage of causing chattering on the control input, and it is difficult to apply this method to the control fins of an AUV system that cannot move its fins at high speed underwater. In this work, a design for a sliding mode control with sliding perturbation observer (SMCSPO) algorithm is applied to AUVs, and the simulation results under underwater disturbance conditions are discussed. From simulation using MATLAB, it is confirmed that AUV control using SMCSPO shows better trajectory tracking control performance without chattering than PID control. [ABSTRACT FROM AUTHOR]

Model-based methods lose their performance in confronting with model uncertainties and disturbances. Accordingly, some degrees of adaptation to the involved conditions are required. In this paper, a novel robust adaptive scheme is proposed which guarantees the simultaneous identification and control of a system in the presence of external disturbances. Thereafter, the suggested algorithm is implemented on a 2-DOf spherical parallel robot as a stabilizer device. By identifying unknown parameters of Jacobian matrix, the relative identification error is obtained as 0.0207. Applying external excitations to the base, the ratio of end-effector to base orientation is acquired as 0.091, demonstrating proper stabilization in comparison with other two well-known methods. The proposed structure also reveals a reliable performance in tracking desired paths for the end-effector Euler angles. • Introducing a robust adaptive scheme in the presence of external disturbances. • Guaranteeing the simultaneous identification and control of MIMO systems. • Online identification of Jacobian matrix unknown parameters. • Implementation on a 2-DOf spherical parallel robot as a stabilizer. • Demonstrating a reliable performance in tracking desired end-effector path. [Display omitted] [ABSTRACT FROM AUTHOR]

The number of cancer diagnoses and deaths worldwide is rising every year despite technological advancements in diagnosing and treating multiple forms of cancer. An oncolytic virus is a type of tumour-killing virus that can infect and analyze cancer cells while mostly preserving normal cells. The oncolytic Vesicular-Stomatitis Virus therapeutic's cell cycle-specific action mathematically investigated. An optimal Proportion Integral-Derivative (PI-D) controller is introduced in this paper based on a suggested Improved Crow Search Algorithm (ICSA) to enhance the outcome of oncolytic virotherapy. The control technique was tested in a computer using MATLAB simulation. The suggested ICSA is used to tune the parameters of the PI-D controller. The ICSA used the inertia factor and boundary handle mechanism in the position update equation to balance exploration and exploitation. The simulation results show that decrease in total dose, tumour cells to 30%, the tumour remain in the treatment area from day 30 onwards. Furthermore, the ICSA algorithm outperforms the CSA and PSO algorithms by 34.5497×10-6 and 15.2573 ×10-6 , respectively, indicating the robustness of treatment methods that can accomplish tumour reduction through biological parameters ambiguity. [ABSTRACT FROM AUTHOR]

Summary: Provisional microgrids create by the connection of two beside microgrids to achieve economic benefits. So far, there have been many reviews on AC microgrids. However, due to the many advantages of DC microgrids, they cannot be ignored. To achieve the advantages of both AC and DC microgrids, hybrid structures were considered. The subject of controlling hybrid microgrids is relatively more complex. Due to the variable nature of the distributed generation resources, this complexity in islanding conditions is even greater than normal conditions. This paper proposes a robust frequency control for islanding provisional microgrid including hybrid AC/DC part as a master microgrid and AC conventional part as a slave microgrid. As authors are aware, there are no scientific reports for frequency control of the proposed microgrid. One of the issues affecting the performance of this controller is the choice of weighting functions. To achieve optimal controller, an algorithm is used to determine the best weighting functions according to the way the controller works, and by examining the sensitivity functions. Finally, simulations were performed to investigate the performance of the controller and the results of using this controller for this microgrid were examined. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of designing novel switching control for a class of stochastic nonlinear switched fuzzy systems with time delay. Firstly, a stochastic nonlinear switched fuzzy system can precisely describe continuous and discrete dynamics as well as their interactions in the complex real-world systems. Next, novel control algorithm and switching law design of the state-dependent form are developed such that the stability is guaranteed. Since convex combination techniques are used to derive the delay independent criteria, some subsystems are allowed to be unstable. Finally, various comparisons of the elaborated examples are conducted to demonstrate the effectiveness of the proposed control design approach. All results illustrate good control performances as desired. [ABSTRACT FROM AUTHOR]

The multiaxial electrohydraulic vibration test system can not only simulate the multiexcitation on multidimensional vibration environment but also meet the vibration test requirements of high thrust, large displacement, low frequency, etc. In order to eliminate the effects of nonlinear factor and system noise and achieve a more accurate control result, a control algorithm based on the frequency-domain filtered-x least mean square adaptive algorithm (FXLMS) is proposed to achieve the power spectral density (PSD) replication. The main idea is to use the frequency-domain FXLMS algorithm to adjust the controller adaptively corresponding to the transfer function uncertainty and changes of the plant, which are typically caused by time-varying parameters in electrohydraulic actuators and system noise. The details and implantation steps of the proposed algorithm are analysed for the single-input single-output electrohydraulic vibration test system. The proposed algorithm and control strategy are then extended to the multiaxial electrohydraulic vibration test system. Eventually, some experimental targets for performing the PSD replication test on a two-exciter system are carried out, in which the results show that the proposed algorithm is valid and meets the test standards.. [ABSTRACT FROM AUTHOR]

Obtaining a control algorithm capable of navigating the system both in forward and backward motions is one of the control objectives for tractor-trailer wheeled robots (TTWRs). In this paper, a relatively general structure is presented for both forward and backward control of an n-trailer wheeled mobile robot (NTWMR) in the presence of wheel slip effects. To keep better overall performance and track the reference trajectories in forward and backward motions, the NTWMR is intended to be controlled in the presence of slip effects. A control algorithm accompanied by a slip compensation procedure is proposed for the system simultaneously. First, the mathematical model of the system in the presence of slip effects is obtained. A novel physically motivated algorithm is proposed for the tracking control in the presence of unknown uncertainties (longitudinal and lateral slips) for both forward and backward motions. By estimating the slip effects at any instant, the control inputs are produced to compensate for their destructive effects on tracking control of the NTWMR. Then the stability of the closed-loop system is evaluated using the Lyapunov theory. The potential of the proposed controller was verified through several case studies, including comparative results and experimental validation in various motion control manoeuvers for a vehicle with trailers. The proposed method is the first algorithm that can cover a broad range of TTWR motion tasks (forward and backward trajectory tracking, slip attenuation, and global stability), which are required to be developed in NTWMRs. • Proposing a control algorithm for forward and backward trajectory tracking. • Designing the controller in the configuration space without using transformations. • Investigating a slip estimator to attenuate the effects of unknown uncertainties. • Analyzing the stability of the control algorithm using the Lyapunov method. • Presenting various case studies and comparative results to show the effectiveness. [ABSTRACT FROM AUTHOR]

This paper studies how to apply game theory to realize a well-function device-to-device enabled vehicular (D2D-V) communication system, where the uplink channel allocated to the cellular user (CU) is reused by multiple D2D-V users. Considering a non-cooperative setting where the CU and D2D-V users are selfish and profit-driven, a novel Stackelberg game framework is proposed to model the single-leader-multiple-follower hierarchical competition, where the CU and D2D-V users act as the leader and the followers, respectively. For the current D2D-V networks, the interference in the dense vehicle scene often leads to extremely poor communication quality. Moreover, a vehicle's mobility leads to an uncertain channel state and further affects the stability of communication. To achieve effective communication, robust Stackelberg game-based resource allocation is developed, and a price-penalty mechanism is further proposed. Unlike previous Stackelberg games, the robust game is highlighted by handling the channel uncertainty which is embedded in the interference probability constraints. Besides, a game equilibrium (GE) is considered to be the solution and its existence and uniqueness are investigated. Also, a distributed robust power control and nonuniform price bargaining algorithm is proposed to approach the GE. Numerical simulations are performed to evaluate the algorithm performances, and the results indicate that the proposed algorithm is effective in high mobility vehicular networks under uncertain channel environments. [ABSTRACT FROM AUTHOR]

We consider adaptive optimal tracking problems for a minimum-phase discrete-time plant with uncertainty in the output channel and bounded exogenous disturbance. The coefficients of the equation of the linear time-invariant nominal model, the uncertainty gain, and the upper bound of the exogenous disturbance are assumed to be unknown, and the worst-case asymptotic tracking error serves as the performance criterion. It is shown that the statement of the optimal problem depends on a priori information about the bounded control. The solution of the problems is based on the polyhedral estimation of all unknown parameters and the use of the performance factor of the tracking problem as an identification criterion for calculating the current optimal estimates. The performance and efficiency of the proposed control algorithms is illustrated by the results of numerical simulation. [ABSTRACT FROM AUTHOR]

The simplified modeling of a complex system allied with a low-order controller structure can lead to poor closed-loop performance and robustness. A feasible solution is to avoid the necessity of a model by using data for the controller design. The Virtual Reference Feedback Tuning (VRFT) is a data-driven design method that only requires a single batch of data and solves a reference tracking problem, although with no guarantee of robustness. In this work, the inclusion of an H ∞ robustness constraint to the VRFT cost function is addressed. The estimation of the H ∞ norm of the sensitivity transfer function is extended to maintain the one-shot characteristic of the VRFT. Swarm intelligence algorithms are used to solve the non-convex cost function. The proposed method is applied in two real-world inspired problems with four different swarm intelligence algorithms, which are compared with each other through a Monte Carlo experiment of 50 executions. The obtained results are satisfactory, achieving the desired robustness criteria. [ABSTRACT FROM AUTHOR]

Two kinds of uncertainties, one due to the dynamic earthquake loads with a wide frequency band and the other due to structural parameters exist in large and complex real-life structures. Most existing control algorithms consider only one of them, resulting in difficulty to guarantee necessary control performance for large complex structures, such as better vibration suppression on structural peak response and robustness performance. Considering the two uncertainties simultaneously, in this paper, a new adaptive robust H ∞ control methodology is presented for vibration control of structures through adroit integration of synchrosqueezed wavelet transform (SWT) and recursive least-squares (RLS) algorithm. The robust H ∞ control is more effective than the traditional LQR/LQG control in terms of the stability and robustness of the control system. The external excitation signal from ground sensors is filtered by a low-pass filter based on SWT and then inputted into the filtered-x RLS adaptive controller. The effectiveness, accuracy, and computational efficiency of the new adaptive control method is demonstrated using a 76-story wind-excited benchmark super high-rise building structure and a 24-story shear-wall building with an active tuned mass damper (ATMD) system on the top floor. Compared with the existing linear quadratic Gaussian control algorithm, the wavelet-hybrid feedback-least mean square algorithm and the robust H ∞ control algorithm, the simulation results show that the control effect and robust performance indexes of the structure are increased by 5%–35% and 5%–25%, respectively, using the new control methodology. [ABSTRACT FROM AUTHOR]

Uncertainties at end-user and aggregator levels can be highly detrimental to the practical implementation of residential load control schemes for electricity market applications. Uncertainty factors such as end-user non-compliance, comfort violations and load set-point changes associated with the demand response aggregator are unavoidable in practice. This paper proposes a novel two-stage control algorithm for robust centralised management of aggregate residential loads which guarantee precise load set-point tracking in the presence of uncertainties occurring in real-time while ensuring that end-user thermal comfort is not compromised. The approach is underpinned by optimal selection of appliances based on an emulated supply curve followed by solving a one-step-ahead optimisation problem. Using air conditioners and water heaters as the controllable loads, the paper illustrates the effectiveness of the proposed approach in load management whilst mitigating the effects of unknown uncertainties. Further, the developed control scheme is compared with an existing industry approach. The results yield that the proposed control scheme is robust to uncertainties, preserves thermal comfort and is applicable for practical implementation under existing demand response standards. • A two-stage control scheme is proposed for robust management of residential loads. • The controller performance is studied under three different uncertainty scenarios. • The end-user thermal comfort is not compromised for the entire control period. • The proposed control scheme is consistent with existing demand response standards. [ABSTRACT FROM AUTHOR]

Robust finite-time control algorithms for satellite attitude maneuvers are proposed in this paper. The standard sliding mode is modified, hence the inherent robustness could be maintained, and this fixed sliding mode is modified to dynamic, therefore the finite-time stability could be achieved. First, the finite -time sliding mode based on attitude quaternion is proposed and the loose finite-time stability is achieved by enlarging the sliding mode parameter. In order to get the strict finite-time stability, a sliding mode based on the Euler axis is then given. The fixed norm property of the Euler axis is used, and a sliding mode parameter without singularity issue is achieved. System performance near the equilibrium point is largely improved by the proposed sliding modes. The singularity issue of finite-time control is solved by the property of rotation around a fixed axis. System finite-time stability and robustness are analyzed by the Lyapunov method. The superiority of proposed controllers and system robustness to some typical perturbations such as disturbance torque, model uncertainty and actuator error are demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

• The proposed SCUC model exploits the congestion management capabilities of the HVDC system. • Uncertainty budget sets are more adaptive to the plausible contingencies owing to the formulation of congestion-aware factors. • The proposed SCUC model eliminates the local optimal solution due to the optimality cut planes. • The novel three-level Benders Decomposition algorithm solves the mix-integer linear problem. • The proposed model captures improved economic benefits when ensuring the model's robustness. Due to the increasing penetration of wind energy, their natural volatility may cause power flow congestions by blocking some key transmission interfaces. To further reduce energy violations induced by uncertainty issues and ensure the system reliability, the controllability of High Voltage Direct Current (HVDC) system should be exploited in AC-DC grids. By leveraging the congestion management capabilities of HVDC, we propose a congestion-aware robust security constrained unit commitment (SCUC) model, to preemptively consider the plausible congestion circumstances in a more adaptive way in the day-ahead (DA) market. In our model, the conventional uncertainty budget sets are modified by the proposed congestion-aware factors, where the typical operational characteristics of traditional corrective actions and HVDC regulations in real-time (RT) market are formulated adaptively. Moreover, the feasibility region of proposed problem is further pruned by the optimality cut planes where more pertinent real-time information are included, thus the local optimal solution is eliminated and the superior economic benefits are obtained in our model. To solve this mix-integer linear problem (MILP), the model is divided into three parts including the DA decision model, the traditional corrective dispatch model, and the HVDC regulation dispatch model. Correspondingly, an improved three-level Benders Decomposition algorithm is developed to solve the model. The enhanced performance of the proposed model was validated in a modified IEEE 118-bus test case. The results show that, in our model, the flexible regulation of HVDC system optimally allocate the operational burdens between DA and RT markets, thus the congestion-aware robust SCUC model further captures the economic benefits when ensuring the model's robustness. [ABSTRACT FROM AUTHOR]

• Fed-batch fermentations are commonplace for reaching high cellular densities. • Metabolic overflow hinders effective control and limits process efficiency. • Advanced control strategies are simulated and evaluated for fed-batch operation. • Model-based strategies are more robust and efficient for fed-batch culture control. High-cell density cultures (HCDC), prone to metabolic overflow, are typically operated in fed-batch mode to maximize productivity. We developed a simulation-based procedure for assessing advanced control strategies for HCDC, aiming to avoid metabolic overflow. We tested a heuristic controller, a nonlinear model predictive controller (NMPC), and two adaptive controllers under realistic conditions. As a case study, we considered the growth of a carotenoid-producing Saccharomyces cerevisiae strain developed in our laboratory. Our control simulations were consistent with experimental results reported in the literature with equivalent control strategies. All tested controllers could cope well with measurement noise and model parameter variations, reaching low ethanol concentrations (≤ 1 g/L), albeit with substantially different biomass productivities. The NMPC algorithm yielded the best performance even under parametric uncertainty, achieving high biomass concentrations (200 gDCW/L) and cell productivities (5 gDCW/L/h). The presented systematic procedure helps in the evaluation of novel control strategies before their implementation in practice. [ABSTRACT FROM AUTHOR]

The task of designing a robust control system using structured H ∞ algorithm involves several issues: choosing the system topology, the controller structure and tuning parameters to achieve robust stability and performance. Its application can lead to a trial-and-error approach at some stage. In order to minimize that effort, a methodology is proposed. A step-by-step guide is provided and includes modelling an uncertain system and designing roll-off, notch and setpoint filters aided by singular value analysis. The step of choosing a suitable topology for the control system and the controller structure is accomplished by RGA matrix analysis. Using a mixed-sensitivity approach, the design of weighting functions to achieve desired frequency-based performance is carried out considering plant restrictions. Application of the structured H ∞ optimization algorithm is commented and results are validated by means of sensitivity function analysis and nonlinear simulation. A successful study case of control system for tight formation flight is presented which includes nonlinear disturbances, uncertainties, measurement noise, input saturation and restrictions due to RHP poles. Results are compared with those obtained with other robust control techniques. [ABSTRACT FROM AUTHOR]

This paper is devoted to the problem of prescribed performance trajectory tracking control for symmetrical underactuated unmanned surface vessels (USVs) in the presence of model uncertainties and input quantization. By combining backstepping filter mechanisms and adaptive algorithms, two robust control architectures are investigated for surge motion and yaw motion. To guarantee the prespecified performance requirements for position tracking control, the constrained error dynamics are transformed to unconstrained ones by virtue of a tangent-type nonlinear mapping function. On the other hand, the inaccurate model can be identified through radial basis neural networks (RBFNNs), where the minimum learning parameter (MLP) algorithm is employed with a low computational complexity. Furthermore, quantization errors can be effectively reduced even when the parameters of the quantizer remain unavailable to designers. Finally, the effectiveness of the proposed controllers is verified via theoretical analyses and numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, a novel neural network-based robust control method is presented for a vehicle-oriented problem, in which the main goal is to ensure stable motion of the vehicle under critical circumstances. The proposed method can be divided into two main steps. In the first step, the model matching algorithm is proposed, which can adjust the nonlinear dynamics of the controlled system to a nominal, linear model. The aim of model matching is to eliminate the effects of the nonlinearities and uncertainties of the system to increase the performances of the closed-loop system. The model matching process results in an additional control input, which is computed by a neural network during the operation of the control system. Furthermore, in the second step, a robust H ∞ is designed, which has double purposes: to handle the fitting error of the neural network and ensure the accurate tracking of the reference signal. The operation and efficiency of the proposed control algorithm are investigated through a complex test scenario, which is performed in the high-fidelity vehicle dynamics simulation software, CarMaker. [ABSTRACT FROM AUTHOR]

This paper presents a predefined-time convergent robust control algorithm that allows the control designer to set the convergence time in advance, independently of initial conditions, deterministic disturbances, and stochastic noises. The control law is consequently designed and verified by simulations for a full-scale 4-degrees-of-freedom (4D) permanent-magnet synchronous motor (PMSM) system in cases of a disturbance-free system with completely measurable states, a disturbance-free system with incompletely measurable states, a system with incompletely measurable states in the presence of deterministic disturbances, and a system with incompletely measurable states in the presence of both deterministic disturbances and stochastic noises. Numerical simulations are provided for the full-scale 4D PMSM system in order to validate the obtained theoretical results in each of the considered cases. To the best of our knowledge, this is the first attempt to design a predefined-time convergent control law for multi-dimensional systems with incompletely measurable states in the presence of both deterministic disturbances and stochastic noises. [ABSTRACT FROM AUTHOR]

This study is mainly about the designation of a new type of haptic device and an asymmetric teleoperation robot system. Aiming at the problems of tracking and transparency of an asymmetric teleoperation system, a robust control algorithm based on a state observer was proposed. The Haptic Device was designed and was chosen as the master-robot of the system. The Baxter dual-arm robot was chosen as the slave-robot of the system. The simulation experiment of robust control based on a state observer of the asymmetric teleoperation robot was carried out. The experiment results showed that the maximum values of displacement tracking errors in three directions x, y, and z are 0.02 m, 0.01 m, and 0.015 m, respectively. Compared with single- joint PID control, the performance of the new control algorithm is improved. The force feedback experiment on the real asymmetric teleoperation robot system was carried out. The results showed that the force feedback wave is consistent with the actual situation and showed that the robust control algorithm proposed is superior to PID. Therefore, the algorithm perfectly satisfied the system. The experiment parameters also demonstrate that the haptic device satisfies the design requirements of the asymmetric teleoperation robots system and the industry standards. [ABSTRACT FROM AUTHOR]

As the strap-down seeker with its detector is fixed onto the body of the flight vehicle, its measurement is coupled with the information of the attitude motion of the vehicle's body, which results that the inertial line-of-sight (LOS) angular rate used in the traditional guidance laws cannot be measured. Besides, the process that the vehicle adjusts its attitude to realize the instantaneous overload command may cause the target to be outside the field-of-view (FOV) of the seeker. To deal with the FOV constraint and the lack of the inertial LOS angular rate, the novel body-LOS coordinate system based relative motion equations are derived, in which the derivative of attitude angular rates appears. Combined with the dynamics of attitude angular rates, the original low-order integrated guidance and control (IGC) design model with FOV constraint and the roll channel IGC design model are established. Furthermore, an integral Barrier Lyapunov function (IBLF) based three-dimensional low-order IGC scheme with the FOV constraint is proposed in this paper. Meanwhile, the adaptive algorithms are employed to estimate the square of the upper bound of the disturbance, which would be compensated in the IGC scheme to improve the robustness of the control system. Both system order and tuning parameters of the proposed low-order IGC algorithm dramatically decrease, and the design procedure is significantly simplified. With the aid of Lyapunov stability analysis, states of the IGC closed-loop system are proved to be uniformly ultimate bounded. Finally, extensive numerical simulations are carried out to validate the effectiveness and robustness of the newly proposed IGC scheme. [ABSTRACT FROM AUTHOR]