Robust Control Algorithm Research Articles

Intervehicle Security-Based Robust Neural Formation Control for Multiple USVs via APS Guidance

This paper focuses on the intervehicle security-based robust formation control of unmanned surface vehicles (USVs) to implement the formation switch mission. In the scheme, a novel adaptive potential ship (APS)-based guidance principle is developed to prevent intervehicle collisions, which is common and threatening when maneuvering a formation switch. By employing the artificial potential field (APF), the APS can program the real-time attitude reference for USVs by using the security intervehicle distance while achieving the path-following task. As for the control part, a robust adaptive formation control algorithm is proposed to effectively stabilize the USVs to the APS via the fusion of the disturbance observer (DOB) and by using the robust neural damping technique. Regarding the merits of the improved design of the DOB, the weight compression of the neural networks can effectively simplify the structure of the DOB and enhance the observation accuracy of the external disturbance. This can facilitate the avoidance of intervehicle collisions and guarantee the application of the theoretical algorithm in engineering practice. Considerable effort has been made to obtain the semiglobally uniform ultimate bounded (SGUUB) stability via theoretical analysis. Finally, with the sailing scene in a narrow channel, the simulated experiment is illustrated to verify the security performance of the proposed strategy.

Robust Cooperative Control of UAV Swarms for Dual-Camp Divergent Tracking of a Heterogeneous Target

Agents are used to exhibit swarm intelligence in the sense of convergence, while divergence is equivalently common in nature and useful in complex applications for multi-UAV systems. This paper proposes a robust target-tracking control algorithm, where UAV swarms are partitioned by a signed graph to perform opposite movements along or against the trajectory of the target. Uncertainties take place in both the fractional-order model of the target and the double-integrator dynamics of the UAVs. To tackle the challenge induced by the bipartite behavior and unknown components in the multi-UAV systems, the article comes up with a backstepping cascade controller and a new method for uncertainty estimation-compensation via a combined approach based on a neural network (NN) and an Uncertainty and Disturbance Estimator (UDE). Steered by the controller, UAVs in a structurally balanced network will display symmetry of their paths, pursuing or away from the target with respect to the origin. Theoretical derivation and numerical simulations have evidenced that the tracking errors converge to zero. Compared with the traditional NN method to solve such problems, this method is proposed for the first time, which can effectively improve the precision of cooperative target tracking and reduce the chattering phenomena of the controller.

Optimized reduced order ARX modelling and robust control for multivariable uncertain systems

This paper deals with modelling and robust control of linear multivariable uncertain systems based on the new linear multivariable ARX (Auto-Regressive with eXogenous input)-Laguerre model and the loop-shaping design procedure with the relative gain array theory. In fact, to guarantee significant parameter number reduction of the multivariable ARX-Laguerre model compared with the classic multivariable ARX model, we propose the optimization of the Laguerre poles of the model using the genetic algorithm where the Fourier coefficients are identified using the recursive least square method. Later, the optimized multivariable ARX-Laguerre model is exploited to propose a robust control algorithm for linear multivariable uncertain systems. Indeed, we propose to combine the loop-shaping design procedure approach and the relative gain array theory to develop a simplified final robust controller guaranteeing reference tracking and robust stability against parametric uncertainties. This proposition aims for the simplification of the control’s scheme by adjusting the weighting matrix and the final robust controller calculated from the loop-shaping design procedure for a simpler and more experimentally applicable control scheme. The identification of Fourier coefficients and Laguerre poles as well as the robust multivariable control algorithm are experimentally validated on a laboratory coupled two-tank system showing good results in terms of modelling and attending desired control performances.

Design of a process development workflow and control strategy for single-pass tangential flow filtration and implementation for integrated and continuous biomanufacturing

Integrated and continuous biomanufacturing (ICB) offers several advantages compared to traditional batch methods, prompting the development of novel technologies and infrastructure for the implementation of ICB in the biopharmaceutical sector. However, most of the advancements have been in the early stages of the purification process, with relatively fewer advancements in the final filtration steps. This work focuses on some of the last major unit operations of a traditional bioprocess: ultrafiltration and diafiltration. Our proposed technological solution involves robust control algorithms for (1) precisely maintaining consistent protein concentration in single-pass ultrafiltration (SP-UF) and (2) achieving the targeted buffer exchange in single-pass diafiltration (SP-DF). Specifically, we adopted an integrated experimental, modeling, and simulation approach to develop a robust process understanding of SP-UF/SP-DF operations via semi-empirical maximum permeate flux models. These models were employed in the optimization of key design variables such as the filter area and the development of control strategies. Finally, we demonstrate the applicability of our proposed solution by operating a single-pass tangential flow filtration (SP-TFF) system in an integrated and continuous manner for six days. We believe that this work is instrumental for the successful realization of SP-UF/SP-DF operations in both batch and ICB processes.

Modeling and control of robotic manipulators equipped with the flexible cable-pulley based gravity compensation mechanism

Robotic manipulators that utilize the Cable-Pulley based Gravity Compensation Mechanism (CPGCM) have drawn increasing attention due to their exceptional features. In the current study, for the first time, the modeling and control of such systems will be addressed considering the flexibility of the cables. To this end, initially, the dominant dynamics of the cables are identified. Then, the Lagrangian of the whole robotic system including the flexibility of the cables is obtained and the system equations of motion are formed. To design the motion controller for the system, and suppress the vibrations stemmed from the inherent flexibility of the cables, the obtained dynamics is transformed into the standard form of the Singular Perturbation Theory (SPT). To achieve this goal, a combination of the inverse dynamics and Nonsingular Terminal Sliding Mode controller is designed, assuming the rigid model of the cable. A modifying term based on SPT is then added to the previous control signal to tackle the flexibility effects of the cables. The finite-time stability of the overall closed-loop controller is proven using Lyapunov’s direct method. The proposed controller is robust to deal with both structured and unstructured uncertainties of the system. The obtained results confirm that the presented robust control algorithm guarantees the stability of the closed-loop system in the finite time. Moreover, it is able to suppress the arisen vibration because of the inherent axial flexibility of the cables in various operational conditions.

Event-triggered distributed robust optimal control of nonholonomic mobile agents with obstacle avoidance formation, input constraints and external disturbances

This paper proposes an event-triggered distributed robust optimal control algorithm for nonholonomic mobile agents (NMA) systems with collision avoidance, asymmetrically saturated inputs and external disturbances. By employing the limit cycle principle and developing the adaptive dynamic programming, zero-sum game theory, and an event-triggering mechanism, the algorithm can optimize an H∞ cost function and relax both the perfect velocity tracking assumption and the persistent excitation condition. First, the graph theory is used to establish the communication configuration, followed by the introduction of the distributed kinematic control for collision avoidance. Second, as a perfect velocity tracking assumption cannot be guaranteed under dynamics changes, input constraints and external disturbances, the distributed optimal robust control algorithm is designed. Third, by developing a new event-triggering mechanism, the burden of computational complexity and communications is reduced. The closed-loop stability and the zeno-behavior exclusion are proven by the Lyapunov theory. Finally, a numerical simulation is conducted with comparison to the sampling period–based control algorithm to demonstrate the effectiveness of the proposed algorithm.

A New Distributed Robust Power Control for Two-Layer Cooperative Communication Networks in Smart Grids with Reduced Utility Costs

The packet loss during transmission of load control commands can lead to regulation errors in the smart grid and increase the cost of utility agencies due to the purchase of additional automatic generation control (AGC) services. In this paper, a two-layer cooperative communication network between the utility company and relays is presented. The utility company rents the relay to assist with the downlink transmission to improve the reliability of communication and reduce the data transmission cost due to packet loss. Furthermore, the uncertainty of channel gain is considered, and a two-tier game model is established. A distributed robust power control algorithm based on the continuous convex approximation method is proposed to obtain the optimal relay power allocation and price. Through the simulation analysis of the proposed scheme and the two comparison schemes, the cost of the utility company was reduced by 6% and 21%, and the standard deviation of income value between the relays was reduced by 40% and 48%, respectively.

Robust Quantum Control for the Manipulation of Solid-State Spins

Robust and high-fidelity control of electron spins in solids is the cornerstone for facilitating applications of solid-state spins in quantum information processing and quantum sensing. However, precise control of spin systems is always challenging due to the presence of various noises originating from the thermal environment and control fields. Here, noise-resilient quantum gates, designed with robust optimal control (ROC) algorithms, are demonstrated experimentally with nitrogen-vacancy centers in diamond to realize tailored robustness against detunings and Rabi errors simultaneously. In the presence of both 10% off-resonance detuning and 10% deviation of a Rabi frequency, we achieve an average single-qubit gate fidelity of up to 99.89%. Our experiments also show that, ROC-based multipulse quantum sensing sequences can suppress spurious responses resulting from finite widths and imperfections of microwave pulses, which provides an efficient strategy for enhancing the performance of existing multipulse quantum sensing sequences.

Robust predictive control of perturbed switched linear systems with stable and unstable subsystems in the presence of uncertainties

In this article, robust model predictive control is proposed for discrete-time switched linear systems in the presence of external disturbances, model uncertainties and constraints on the states and input. To solve the problem, a mode-dependent average dwell time switching strategy is constructed in controller design procedure, with less limitations than the existing average dwell time switching. Using some mathematical tools, the model predictive control problem is turned into a feasible linear matrix inequality. The control algorithm is presented via two theorems, and the stability analysis is analytically presented, based on multiple Lyapunov function method. Compared with the previous studies, the underlying switched system may consist of stable and unstable subsystems, perturbed by model uncertainties and external disturbances. The simulation results are also given to study the effectiveness of the proposed robust model predictive control algorithm, from a comparison viewpoint.

Motion Control Method of Bionic Robot Dog Based on Vision and Navigation Information

With the progress and development of AI technology and industrial automation technology, AI robot dogs are widely used in engineering practice to replace human beings in high-precision and tedious industrial operations. Bionic robots easily produce control errors due to the influence of spatial disturbance factors in the process of pose determination. It is necessary to calibrate robots accurately to improve the positioning control accuracy of bionic robots. Therefore, a robust control algorithm for bionic robots based on binocular vision navigation is proposed. An optical CCD binocular vision dynamic tracking system is used to measure the end position and pose parameters of a bionic robot, and the kinematics model of the controlled object is established. Taking the degree of freedom parameter of the robot’s rotating joint as the control constraint parameter, a hierarchical subdimensional space motion planning model of the robot is established. The binocular vision tracking method is used to realize the adaptive correction of the position and posture of the bionic robot and achieve robust control. The simulation results show that the fitting error of the robot’s end position and pose parameters is low, and the dynamic tracking performance is good when the method is used for the position positioning of control of the bionic robot.

Robust servo linear quadratic regulator controller based on state compensation and velocity feedforward of the spherical robot: Theory and experimental verification

There are few studies on the lateral motion of spherical robots. In this article, a new algorithm is proposed to solve the problem of low control accuracy of the lateral motion. A single lateral motion model is established, and the optimal solution of the linear quadratic regulator in infinite time domain is obtained. Aiming at the problems of longitudinal velocity and lateral angle coupling and low control precision, state compensation and velocity feedforward are carried out, and an improved robust servo linear quadratic regulator control algorithm is proposed. Experiments show that the proposed lateral control algorithm has strong adaptability and robustness to changing speeds and lateral angles, and the control effect is stable and reliable.

Adaptive Robust Control for Nonlinear Mechanical Systems With Inequality Constraints and Uncertainties

The inequality constraints, system nonlinearities, parameter uncertainties, and external disturbances are always unavoidable in practical mechanical systems. This article proposes an adaptive robust control (ARC) algorithm from the view of servo constraint following to tackle the control problem of mechanical systems subject to the above factors. For the inequality constraints, a creative diffeomorphism which could convert the two-sided bounded state variables to the unbounded ones is explored, which could render the transformed nonlinear system free from inequality constraints. For the system uncertainties, a leakage-type ARC algorithm is developed, which could render the system the practical stability. The permanent magnet linear motor (PMLM) system is utilized as a typical application to verify the proposed state transformation and ARC approach. Numerical simulations show that the displacement of the PMLM system could well track the desired trajectory without violating the given bound line.

Review of Control Algorithms of Vehicle Anti-lock Braking System

Background: Automobile anti-lock braking system (ABS) is an important part of the vehicle active safety control system, which is widely used in all kinds of vehicles. At present, the research of ABS mainly focuses on the study of the control algorithm, which is intended to improve the stability, robustness, and adaptability of the control algorithm. Objective: In the future, it is necessary to explore adaptive robust control algorithms that adapt to extreme conditions such as high nonlinearity and sudden road changes, such as active disturbance rejection control technology, deep learning neural network control technology, etc. Method: According to the research status of domestic and foreign researchers in the field of ABS control algorithms, ABS control algorithms are mainly divided into two categories: control methods based on logic thresholds and control methods based on slip ratio. Results: The comparative study of ABS control methods shows that the logic threshold control method has strong maneuverability and simple implementation, but its adaptability is poor. Sliding mode control has strong robustness and good transient response, but chattering needs to be suppressed. Although the PID control algorithm is simple and easy to implement, it needs to improve the transient response of the system.

Detection of Cyber Attacks and Network Attacks Using Machine Learning Algorithms

Now a days cyber crime growing and has a big effect everywhere globally. ethical hackers are normally involved in identifying flaws and recommending mitigation measures. the cyber safety international, there's a pressing need for the improvement of powerful techniques. Because of the effectiveness of machine learning in cyber security issues, machine learning for cyber security has recently become a hot topic. In cyber security, machine learning approaches have been utilized to handle important concerns such as intrusion detection, malware classification and detection, spam detection, and phishing detection. Although ML cannot fully automate a cyber-security system, it can identify cyber-security threats more efficiently than other software-oriented approaches, relieving security analysts of their burden. As a result, effective adaptive methods, such as machine learning techniques, can yield higher detection rates, lower false alarm rates, and cheaper computing and transmission costs. Our key goal is that the challenge of detecting attacks is fundamentally different from those of these other applications, making it substantially more difficult for the intrusion detection community to apply machine learning effectively. In this study, the CPS is modeled as a network of agents that move in unison with one another, with one agent acting as a leader and commanding the other agents. The proposed strategy in this study is to employ the structure of deep neural networks for the detection phase, which should tell the system of the attack's existence in the early stages of the attack. The use of robust control algorithms in the network to isolate the misbehaving agent in the leader-follower mechanism has been researched. Following the attack detection phase with a deep neural network, the control system uses the reputation algorithm to isolate the misbehaving agent in the presented control method. Experiment results show that deep learning algorithms can detect attacks more effectively than traditional methods, making cyber security simpler, more proactive, and less expensive and more expensive.

Wind energy-harvesting technologies and recent research progresses in wind farm control models

In order to sustain the overall competitiveness of the wind power industry, unrelenting focus is required on working toward the advancement of enabling technologies and research studies that are associated with wind farm systems. First, wind farm technologies that include various turbine generator systems coupled with different power transmission configurations have enormous impact in determining the quality of wind power production. In addition, modern wind farms are expected to implement robust power control algorithms to meet more advanced requirements of electricity generation. Accordingly, this study explores the statuses of wind energy harvesting technologies and wind farm control strategies by discussing their recent and future impact on transforming the wind power industry. Doubly fed induction generator (DFIG)-based wind energy harvesting technology is well-matured and has exhibited an excellent track-record in past and recent experiences, but its capability of being further scalable for large-scale power production is limited as it is largely incompatible with high-voltage power transmission networks. On the other hand, permanent magnet synchronous generator (PMSG)-based technology is making significant advancements to attain the maximum possible efficiency level in greatly facilitating larger scale power generation, although the construction of bulky and costly power transmission systems is required. In this regard, future technological advances in the wind farm industry are expected to reasonably optimize the design and cost of high-voltage power transmission systems. Similarly, an increasing number of research studies are introducing a number of power optimization-based control models to create an ideal integration of the aforementioned wind farm technologies so as to ultimately enhance the reliability of electricity production by maintaining the systems’ safety. Yet, additional work is still expected to be undertaken in the future for a more extended evaluation of the performances of many different control models under a similar environment.

Event-triggered robust adaptive control for unmanned surface vehicle in presence of deception attacks

This article focuses on a double layer virtual ship guidance–based adaptive event–triggered path-following control for unmanned surface vehicle under the unknown actuator gain and deception attacks. In the guidance unit, the adaptive virtual ship is developed to derive the smooth reference paths for the controlled objective. That can effectively remove the abrupt phenomenon of actuators. Associated with the double layer virtual ship guidance strategy, a robust adaptive control algorithm is proposed for the unmanned surface vehicle by fusing the event-triggered rule and the adaptive compensated technique. In the algorithm, the deception attacks caused by the communication channel were compensated by constructing the attack adaptive parameters. That is critical to improve the accuracy and stabilize of the closed-loop control system. Besides, the transmission burden from the controller to the actuator was reduced for merits of the input-based event-triggered mechanism. Finally, the theoretical stability was proofed by the Lyapunov theorem, and the well performance was obtained through numerical simulation in presence of the time-varying disturbances.

Low-Computation Tracking Control of Nonlinear Systems With Asymmetric Full-State Constraints and Unknown Control Directions

This article considers the tracking control problem for an uncertain feedback nonlinear system with asymmetric time-varying full-state constraints and unknown control directions. We propose a new low-computation full-state constrained robust control algorithm, that removes the feasibility conditions of virtual controllers and solves the unknown control direction problem without using the Nussbaum gain technique. By introducing nonlinear transformation functions, the original constrained systems are converted into new unconstrained tracking error systems, and the new systems eliminate the limitation of the initial conditions. Then, to seek the correct control directions, an orientation function with error conversion is constructed, which avoids introducing Nussbaum-type functions and logic switching rules. The proposed method possesses inherent robustness against model uncertainties and disturbances, and guarantees that the full-state constraints and the tracking error of systems enter into a prescribed set in a fixed time. Finally, simulation examples are presented to demonstrate the superiority and effectiveness of the developed control algorithm.

Hybrid Backstepping-Super Twisting Algorithm for Robust Speed Control of a Three-Phase Induction Motor

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.

Motion control framework for unmanned wheel-legged hybrid vehicle considering uncertain disturbances based robust model predictive control

The paper proposes a motion control framework for the unmanned wheel-legged hybrid vehicle to track the motion trajectory considering uncertain disturbances. The whole-body dynamic model is built with the contact force of each rolling wheel, which serves as the foundation to obtain trajectory tracking. The angular momentum and linear momentum are optimized by the robust model predictive control algorithm considering the soft constraint of the relaxation variable. The contact force between wheel and ground is solved by the quadratic programming algorithm to efficiently obtain the flexion/extension joint and wheel motion planning. Then, the explicit algorithm to calculate the torque commands of the flexion/extension joint considering the feed-forward torque and feedback torque to improve the control accuracy. Simulation results validate that the control framework based on the robust model predictive control algorithm can solve the uncertain disturbances in process of the vehicle running on the rough road.

Robust iterative learning control for random switched singular systems in time domain

A robust iterative learning control algorithm is proposed for continuous-time switched singular systems disturbed by random measurement noise. Firstly, the switched singular systems is transformed into a differential-algebraic equation composed of slow subsystem and fast subsystem by nonsingular transformation. Then, the control input is continuously modified by the state measurement errors and the learning gain with attenuation characteristics. The convergence of each subsystem is strictly proved in theory, and the sufficient conditions for the convergence of this algorithm are given. Theoretical analysis shows that the algorithm can effectively suppress the measurement noise and make the system state completely track the desired trajectory in a finite time interval. Finally, compared with the fixed gain iterative learning control method, the simulation results show that the tracking accuracy of the proposed algorithm is significantly improved.