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In this paper, a robust nonlinear approach for control of liquid levels in a quadruple tank system (QTS) is developed based on the design of an integrator backstepping super-twisting controller, which implements a multivariable sliding surface, where the error trajectories converge to the origin at any operating point of the system. Since the backstepping algorithm is dependent on the derivatives of the state variables, and it is sensitive to measurement noise, integral transformations of the backstepping virtual controls are performed via the modulating functions technique, rendering the algorithm derivative-free and immune to noise. The simulations based on the dynamics of the QTS located at the Advanced Control Systems Laboratory of the Pontificia Universidad Católica del Perú (PUCP) showed a good performance of the designed controller and therefore the robustness of the proposed approach.

Uncertainty and nonlinearity in the depth control of remotely operated vehicles (ROVs) have been widely studied, especially in complex underwater environments. To improve the motion performance of ROVs and enhance their robustness, the model of ROV depth control in complex water environments was developed. The developed control scheme of interval type-2 fuzzy proportional-integral-derivative control (IT2FPID) is based on proportional-integral-derivative control (PID) and interval type-2 fuzzy logic control (IT2FLC). The performance indicators were used to evaluate the immunity of the controller type to external disturbances. The overshoot of 0.3% and settling time of 7.5 s of IT2FPID seem to be more robust compared to those of type-1 fuzzy proportional-integral-derivative (T1FPID) and PID.

Proportional pressure-reducing valve (PPRV) is widely used in brake circuits and pilot supply lines for directional control valves. The performance of PPRV can be degraded because of the effect of magnetic hysteresis, frictions, and other disturbances, which worsens the accuracy of the entire hydraulic system. This paper presents a high-performance pressure controller of a PPRV considering the model nonlinearities. A nonlinear phenomenological model based on the Hammerstein modeling method is first developed to describe the dynamics of a PPRV. A pressure-dependent damping ratio is carried out to handle the asymmetric nonlinearity at the starting process of pressure adjustments, and a series of experiments are conducted to verify the effectiveness of the model. A second-order adaptive robust controller is developed based on the Lyapunov theorem to guarantee the tracking performance in the presence of parameter uncertainties and uncertain nonlinearities. Comparative experiments show that the proposed second-order adaptive robust control algorithm together with the phenomenological model achieves a significant reduction in pressure-tracking error, compared with the one without the proposed plant model, or other existing controllers such as the feedforward proportional–integral–derivative (PID) controller and the first-order adaptive robust controller. [ABSTRACT FROM AUTHOR]

A real-time hybrid simulation (RTHS) is a promising technique to investigate a complicated or large-scale structure by dividing it into numerical and physical substructures and conducting cyber-physical tests on it. The control system design of an RTHS is a challenging topic due to the additional feedback between the physical and numerical substructures, and the complexity of the physical control plant. This paper proposes a novel RTHS control strategy by combining the theories of adaptive control and robust control, where a reformed plant which is highly simplified compared to the physical plant can be used to design the control system without compromising the control performance. The adaptation and robustness features of the control system are realized by the bounded-gain forgetting least-squares estimator and the sliding mode controller, respectively. The control strategy is validated by investigating an RTHS benchmark problem of a nonlinear three-story steel frame The proposed control strategy could simplify the control system design and does not require a precise physical plant; thus, it is an efficient and practical option for an RTHS.

In this paper, the research emphasis focuses on the tracking precision of position loading and torque loading systems with velocity-sensitive and adaptive robust control. Compared with conventional PID control, the improved PID control based on velocity-sensitive fully manifests its superiority to position loading. By contrast, we analyzed the possible influence for the control difference of conventional PID and velocity-sensitive with PID. Furthermore, for the purpose of accurate torque loading, a mathematical model was established through dynamics analysis and the adaptive robust controller, where the adaptive robust control algorithm is designed to generate the reference position trajectory for the servo system in the upper controller while closed-loop position tracking is performed in the underlying controller, was built based on a state space equation. In the end, an experimental platform was built to verify the feasibility and advantages of the position and torque loading system with the velocity-sensitive and adaptive robust control.

In Diabetes Mellitus, the pancreas remains incapable of insulin administration that leads to hyperglycaemia, an escalated glycaemic concentration, which may stimulate many complications. To circumvent this situation, a closed-loop control strategy is much needed for the exogenous insulin infusion in diabetic patients. This closed-loop structure is often termed as an artificial pancreas that is generally established by the employment of different feedback control strategies. In this work, the authors have proposed an arbitrary-order sliding mode control approach for development of the said mechanism. The term, arbitrary, is exercised in the sense of its applicability to any n -order controllable canonical system. The proposed control algorithm affirms the finite-time effective stabilisation of the glucose-insulin regulatory system, at the desired level, with the alleviation of sharp fluctuations. The novelty of this work lies in the sliding manifold that incorporates indirect non-linear terms. In addition, the necessary discontinuous terms are filtered-out once before its employment to the plant, i.e. diabetic patient. The robustness, in the presence of external disturbances, i.e. meal intake is confirmed via rigorous mathematical stability analysis. In addition, the effectiveness of the proposed control strategy is ascertained by comparing the results with the standard literature.

This study designs a robust closed-loop control algorithm for elevated blood glucose level stabilisation in type 1 diabetic patients. The control algorithm is based on a novel control action resulting from integrating algebraic meal disturbance estimator with back-stepping integral sliding mode control (BISMC) technique. The estimator shows finite time convergence leading to accurate and fast estimation of meal disturbance. Moreover, compensation of the estimated disturbance in controller provides significant reduction in chattering phenomenon, which is inherent drawback of sliding mode control (SMC). The controller is applied to one of the most reliable models of type 1 diabetic patients, named Bergman's minimal model. The effectiveness and superiority of the designed controller is shown by comparing it to classical SMC and super-twisting sliding mode control. The designed controller is subject to three different cases for detailed analysis of the controller's robustness against meal disturbance. The three cases considered are hyperglycaemia, hyperglycaemia combined with meal disturbance and three meal disturbance. The simulation results confirm superior performance of algebraic disturbance estimator based BISMC controller for all the cases mentioned above.

A profound concern dwelling on developing practical control algorithms for autonomous cars is to guarantee robustness and resilience in harsh driving situations. Extraneous environmental factors coupled with structured and unstructured uncertainties have always posed concerns about the proposed algorithms' effectiveness. This paper presents an improved control algorithm based on a robust adaptive neural network trained with integral sliding mode (NN-ISMC) with the capacity for state estimation and auxiliary control inputs. Additionally, the paper considers the contribution of active front steering (AFS) integrated with direct yaw moment control (DYC) to employ during critical driving scenarios. Moreover, a super-twisting ESO disturbance observer (STESO-DO) was developed to estimate disturbances imposed on the car during an emergency maneuver, such as double-lane change, in terms of successive strong gusts of crosswind. Additional uncertainties were introduced to the system to evaluate the algorithm's robustness, such as the tire cornering stiffness and traveling speed. The performance of the designed framework was further assessed against two documented comparable methods in the literature using high-fidelity MATLAB/Simulink–CarSim co-simulations. The findings from various driving conditions and speeds indicate that the proposed controller successfully stabilizes the handling dynamics and thus enhances the path-tracking performance compared to the previously reported methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust control method for the control of a 7-DOF exoskeleton robot. The external disturbances and unknown dynamics in the form of friction forces, different upper-limb's mass, backlash, and input saturation make robot unstable, which prevents the robot from correctly following the defined path. A new fractional sliding mode controller (NFSMC) is designed, which is robust against unknown dynamic and external disturbances. Fractional PID controller (FPID) has high trajectory tracking, but it is not robust against external disturbances. Therefore, by combining NFSMC and FPID controllers, a new compound fractional PID sliding mode controller (NCFPIDSMC) is proposed, which benefits high trajectory tracking of FPID and robustness of NFSMC. The stability of the proposed control method is verified by Lyapunov theory. A random noise is applied in order to confirm the robustness of the proposed control method., Competing Interests: The authors have declared that no competing interests exist.

The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostly, such a process is to be decoupled into several input/output pairings (loops), so that a single controller can be assigned for each loop. In the frame of this research, the Brain Emotional Learning Based Intelligent Controller (BELBIC) forms the control structure for each decoupled loop. The paper's main objective is to develop a parameterization technique for decoupling and control schemes, which ensures robust control behavior. In this regard, the novel optimization technique Bacterial Swarm Optimization (BSO) is utilized for the minimization of summation of the integral time-weighted squared errors (ITSEs) for all control loops. This optimization technique constitutes a hybrid between two techniques, which are the Particle Swarm and Bacterial Foraging algorithms. According to the simulation results, this hybridized technique ensures low mathematical burdens and high decoupling and control accuracy. Moreover, the behavior analysis of the proposed BELBIC shows a remarkable improvement in the time domain behavior and robustness over the conventional PID controller.

In recent years, balance control of two-wheeled bicycle has received more attention of scientists. One difficulty of this problem is the control object is unstable and constantly impacted by noise. To solve this problem, the authors often use robust control algorithms. However, robust controller of selfbalancing two-wheeled bicycle are often complex and higher order so affect to quality during real controlling. The article introduces the stochastic balanced truncation algorithm based on Schur analysis and applies this algorithm to reduce order higher order robust controller in control balancing two-wheeled bicycle problem. The simulation results show that the reduced 4th and 5th order controller arcoording to the stochastic balanced truncation algorithm based on Schur analysis can control the two-wheeled bicycle model. The reduced 3rd order controller cannot control the balance of the two-wheeled bicycle model. The reduced 4th and 5th order controller can replace the original controller while the performance of the control system is ensured. Using reduced 5th, 4th order controller will make the program code simpler, reducing the calculation time of the self-balancing two-wheel control system. The simulation results show the correctness of the model reduction algorithm and the robust control algorithm of two-wheeled self-balancing two-wheeled bicycle. [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 paper concerns the modelling of an accumulator used in industrial elastic web processing plant to allow changing material roll while the rest of the line remains at a constant web velocity. A nonlinear model of a motor actuated accumulator is first summarized. This model is derived from the physical relationships describing web tension and velocity dynamics in each web span of this accumulator. A linear model is deduced from the nonlinear one around a working point for frequency domain analysis. Thus the effect of some mechanical accumulator parameter variations are analyzed. In a second part, multi-model industrial PI controllers, adjusted with evolutionary algorithm on our realistic nonlinear model are compared with multi-model H∞ controllers. Both controllers allow good robustness against mechanical parameter variations., (Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper describes a predictive extended state observer-based robust control for uncertain process control applications. The technique discussed in the article uses the extended state observer (ESO) that can estimate the dynamics of the system as well as total disturbance encountered in the system. The disturbances, parametric uncertainties associated with the processes are treated as an extended state variable to be estimated in real-time using ESO. With the implementation of a predictive algorithm with an ESO, the proposed control structure extends its applicability to time-delayed higher-order processes. The proposed control technique utilizes the simple first-order modified predictive ESO even in the case of higher-order processes. The novel predictive ESO is able to obtain a delay less estimation of total disturbance as compared with existing normal ESO. Also, novel predictive ESO maintains its stability margin in presence of time delay as well provides better response as compared with normal ESO. Numerical simulations show that the proposed scheme provides a significant improvement in transient response as compared with internal model control-based proportional-integral-derivative (IMC-PID) control. The proposed scheme requires less knowledge of the process as compared with the IMC-PID structure. The implementation of the proposed control is tested on a real-life single tank level control system. Because of its merit, the suggested technique can be used as automatic for online tuning, as it is less reliant on the process model. [ABSTRACT FROM AUTHOR]

In this paper, a robust controller for attitude stabilization of a small quadrotor helicopter is developed. The TS (Takagi-Sugeno) fuzzy model approach and the H ∞ robust control are combined to produce the proposed algorithm. Besides, disturbances and parametric uncertainties are considered. First, the nonlinear model of the vehicle is linearized around several operating points to obtain the representation of a TS fuzzy model, which represents the nonlinearity of the system dynamics. Then, a robust fuzzy controller is synthesized which guarantees desired control performances. The given controller is designed using numerical tools such as linear matrix inequalities (LMI). Finally, simulation results and real-time experiments are presented to validate the performance of the proposed scheme to robustly stabilize the quadrotor dynamics at the desired reference. [ABSTRACT FROM AUTHOR]

Robust control of a class of uncertain systems that have disturbances and uncertainties not satisfying "matching" condition is investigated in this paper via a disturbance observer based control (DOBC) approach. In the context of this paper, "matched" disturbances/uncertainties stand for the disturbances/uncertainties entering the system through the same channels as control inputs. By properly designing a disturbance compensation gain, a novel composite controller is proposed to counteract the "mismatched" lumped disturbances from the output channels. The proposed method significantly extends the applicability of the DOBC methods. Rigorous stability analysis of the closed-loop system with the proposed method is established under mild assumptions. The proposed method is applied to a nonlinear MAGnetic LEViation (MAGLEV) suspension system. Simulation shows that compared to the widely used integral control method, the proposed method provides significantly improved disturbance rejection and robustness against load variation., (Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.)

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]

The heavy equipment industry is building more and more equipment with electro-hydraulic control systems. The existing industry practices for the design of control systems in construction machines primarily rely on classical designs coupled with ad-hoc synthesis procedures. Such practices produce desirable results, but lack a systematic procedure to account for invariably present plant uncertainties in the design process as well as coupled dynamics of the multi-input multi-output (MIMO) configuration. In this paper, two H(infinity) based robust control designs are presented for an automatic bucket leveling mechanism of a wheel loader. In one case, the controller is designed for the base plant model. In another case, the controller is designed for the plant with a feedback linearization control law applied yielding improved stability robustness. A MIMO nonlinear model for an electro-hydraulically actuated wheel loader linkage is considered. The robustness of the controller designs are validated by using analysis and by simulation using a complete nonlinear model of the wheel loader linkage and hydraulic system.

A robust control method for synchronizing a biaxial servo system motion is proposed in this paper. A new network based cross-coupled control and adaptive tuning techniques are used together to cancel out the skew error. The conventional fixed gain PID cross-coupled controller (CCC) is replaced with the adaptive cross-coupled controller (ACCC) in the proposed control scheme to maintain biaxial servo system synchronization motion. Adaptive-tuning PID (APID) position and velocity controllers provide the necessary control actions to maintain synchronization while following a variable command trajectory. A delay-time compensator (DTC) with an adaptive controller was augmented to set the time delay element, effectively moving it outside the closed loop, enhancing the stability of the robust controlled system. This scheme provides strong robustness with respect to uncertain dynamics and disturbances. The simulation and experimental results reveal that the proposed control structure adapts to a wide range of operating conditions and provides promising results under parameter variations and load changes.

This paper addresses the problem of designing adaptive fuzzy-based (or neural network-based) robust controls for a large class of uncertain nonlinear time-varying systems. This class of systems can be perturbed by plant uncertainties, unmodeled perturbations, and external disturbances. Nonlinear H(infinity) control technique incorporated with adaptive control technique and VSC technique is employed to construct the intelligent robust stabilization controller such that an H(infinity) control is achieved. The problem of the robust tracking control design for uncertain robotic systems is employed to demonstrate the effectiveness of the developed robust stabilization control scheme. Therefore, an intelligent robust tracking controller for uncertain robotic systems in the presence of high-degree uncertainties can easily be implemented. Its solution requires only to solve a linear algebraic matrix inequality and a satisfactorily transient and asymptotical tracking performance is guaranteed. A simulation example is made to confirm the performance of the developed control algorithms.

Summary: This article presents new approaches for analyzing and designing the robust control system by using both the gain and phase information. These approaches aim to relax the stability condition and further improve the system performance than the H∞‐robust control theory. The contributions of this article are: (i) to propose a new gain‐phase (GP‐) robust stability theorem which can further relax the stability condition than the small‐gain theorem by using both the gain and phase information of uncertainty, (ii) to propose a new GP‐mixed sensitivity problem which can further improve the system performance than the mixed sensitivity method by using the GP‐robust stability theorem instead of the small‐gain one, (iii) to develop the WΩ‐K iteration algorithm to solve the GP‐mixed sensitivity problem and to verify the effectiveness of the proposed method through the design example of automotive fuel control system, and (iv) to formulate a new Ψ‐control problem with phase optimality criterion and an H∞/Ψ‐mixed control problem with both the gain and phase optimality criteria, to derive their solution for SISO output feedback systems and to show their effectiveness through two design examples. [ABSTRACT FROM AUTHOR]

Up to now, the supervision and control of deadlock-free resource allocation has received considerable attention, particularly regarding their deadlock problems. To date, most solutions have supposed that allocated resources never fail. However, this is quite the opposite in reality since some resources may fail unexpectedly. A robust system should be resilient to such failures. In this paper, resources are divided into reliable ones and unreliable ones. On the basis of the deadlock avoidance algorithm which is proposed for the problem of deadlocks, we propose a robust control algorithm in the paradigm of systems of sequential systems with shared resources, which can acquire and release resources in a multitype and multiquantity way. It is validated to be a polynomially complex robust control algorithm by the distributivity analysis. Finally, experimental results show that the proposed approaches are effective as well as efficient in response to resource failures. [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]

Abstract In this work, a novel optimized robust control algorithm, based on the mirror-mapping method, is proposed for a class of industrial unstable process with time delay. The optimizing criterion is to minimize the sensitivity function to enhance its robustness. The controllers are designed based on the Padé approximated mirror-mapping process with a stable form, other than the original unstable system. The developed algorithm could release the internal stability constraints to the unstable plant. By using the graphical stability criterion, a systematic methodology is derived to obtain the exact stabilizing region, where the sole design parameter is related to the stability degree of the closed-loop system. The proposed algorithm is with characteristics of concise and efficient design. Three experiments has been employed to illustrate that the control effects can achieve the satisfied performance in aspects of disturbance rejection and robustness. Highlights • A novel optimized robust algorithm is proposed with more efficient performance. • The algorithm could release the internal stability constraints to unstable plant. • The exact stabilizing region is identified by the graphical stability criterion. [ABSTRACT FROM AUTHOR]

The subject of deadlock control for automated manufacturing systems has been extensively studied in the past several years. For most of them, researchers have supposed that shared and dedicated resources never fail. As any manufacturing practitioner knows, resource failures, e.g., defective parts, faulty sensors, blurred signals, and broken actuators, are a common problem. Hence, the robust supervision and control for a system with unreliable resources is necessary. In our previous work, we proposed a robustness intensification algorithm based on a deadlock avoidance algorithm to ensure that processes not requiring unreliable resources can operate smoothly. In this article, a new robust method, which combines the advantages of absorbing policies and distributing policies with the aid of critical regions, is developed. It includes two algorithms, i.e., a robustness algorithm for processes not requiring unreliable resources and an intensification algorithm for processes requiring unreliable resources. Compared with existing absorbing and distributing policies, the new robust method can increase the production rate while decrease the algorithm complexity for a system allowing flexible routes apart from each process stage acquiring more than one type of resources. [ABSTRACT FROM AUTHOR]

In this paper, the robust stabilization issues on uncertain system with ${H_\infty }$ performance are investigated by applying impulsive time related control algorithm. A hybrid control scheme is novelly proposed using proportional impulsive plus integral mechanism. This impulse-time-dependent (ITD) feedback controller employs the proportional control action ${K_{p}}$ at impulse instants for stabilizing. Then, intermittently the integral control strategy ${K_{I}(t)}$ is implemented between impulsive time intervals for improving the state convergence. Sufficient conditions are derived to ensure the closed-loop performance. In addition, a memory controller ${K_{D}}$ is introduced to tackle the stabilization problem of system with signal transmission delay. Furthermore, a distributed time-varying delay ${\delta _{k}(t)}$ is defined in the impulsive control-based system. Considering discrete and distributed time-varying delays concurrently, the ITD stability criteria are deduced to guarantee the ${H_\infty }$ performance on uncertain system with exogenous disturbance. Numerical examples are conducted to show the expected stabilization and state convergence response with disturbance attenuation of this development. [ABSTRACT FROM AUTHOR]

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]

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]

Background: For the fracture reduction robot, the position tracking accuracy and compliance are affected by dynamic loads from muscle stretching, uncertainties in robot dynamics models, and various internal and external disturbances., Methods: A control method that integrates a Radial Basis Function Neural Network (RBFNN) with Nonlinear Disturbance Observer is proposed to enhance position tracking accuracy. Additionally, an admittance control is employed for force tracking to enhance the robot's compliance, thereby improving the safety., Results: Experiments are conducted on a long bone fracture model with simulated muscle forces and the results demonstrate that the position tracking error is less than ±0.2 mm, the angular displacement error is less than ±0.3°, and the maximum force tracking error is 26.28 N. This result can meet surgery requirements., Conclusions: The control method shows promising outcomes in enhancing the safety and accuracy of long bone fracture reduction with robotic assistance., (© 2024 John Wiley & Sons Ltd.)

This paper presents the mathematical modeling of a multivariable process used in level control, under the assumptions that all pipes are filled with fluid and the inertial effects caused by flow variations, in case of small accelerations, are neglected. Because of the way the two tanks are interconnected and interact with each other, the resulting mathematical model is nonlinear. In the paper it is proposed a robust control algorithm for this plant developed based on H-infinity loop shaping procedure. The required computations and the study of the behavior of the system are done in the Matlab simulation environment. [ABSTRACT FROM AUTHOR]

This paper compares various angle-tracking algorithms to balance the performance and noise for a steering-by-wire (SBW) system. Direct and quiet steering experiences can improve drivers' acceptance of the SBW system. Linear quadratic regulator (LQR) control, robust control, and conventional cascade proportional–integral (PI) control have been developed and compared both theoretically and experimentally. To avoid the risky and time-consuming parameter-tuning process, a high-fidelity steering resistance model, which comprises a linear two-degree-of-freedom vehicle model and a dynamic LuGre friction model is established. Step and sine wave tests are simulated in a Matlab/Simulink environment to determine the reasonable parameter region for various methods. Then, the three types of algorithms are implemented on a prototype SBW vehicle and compared under the same scenarios. Finally, the simulated and experimental results are illustrated in detail. According to the indicators of control bandwidths, steady-state errors, cockpit sounds, and current waveforms, it is clear that LQR and robust control can achieve faster response and more acceptable noise, with uncertain and relatively larger tracking errors. Cascade PI control, in comparison, can realize smaller steady-state errors and gentler current waveforms, with slight noise and slower response. [ABSTRACT FROM AUTHOR]

Summary: A hybrid integrator‐gain system is discussed that aims for improved low‐frequency disturbance rejection, while, at the same time, does not deteriorate overshoot and settling times when compared with a linear integrator. The hybrid integrator has similar phase advantages as the well‐known Clegg integrator but without inducing the discontinuous behavior resulting from resetting system state values. Optimal tuning of the controller parameters of the hybrid integrator is strongly influenced by machine‐specific properties and therefore favors a data‐driven optimization approach. However, as a time‐domain optimization algorithm can easily lead to nonrobust solutions in the sense of large peaking of the closed‐loop frequency response functions, frequency‐domain robustness constraints will be imposed. By means of an adaptive weighting filter design, the parameter updates are penalized upon violation of said robustness constraints. Posed in an unconstrained problem formulation, this is subsequently solved by applying a Gauss‐Newton–based parameter update scheme. Closed‐loop stability of the linear time‐invariant plant and controller in feedback connection with a hybrid integrator‐gain system element follows from a circle‐criterion‐like analysis, which is based on evaluating (measured) frequency response data. Measurement results obtained from an industrial wafer scanner demonstrate the effectiveness of the approach. [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]

Iterative learning control (ILC) is an effective technique that improves the tracking performance of systems by adjusting the feedforward control signal based on the memory data. The key in ILC is to design learning filters with guaranteed convergence and robustness, which usually involves lots of tuning effort especially in high-order ILC. To facilitate this procedure, this paper proposes a systematics approach to design learning filters for arbitrary-order ILC with guaranteed convergence, robustness and ease of tuning. The filter design problem is transformed into an H ∞ optimal control problem for a constructed feedback system. This approach is based on an infinite impulse response (IIR) system and conducted directly in iteration-frequency domain. The proposed algorithm is further advanced to the one that explicitly considers system variations based on μ synthesis. Important characteristics of the proposed approach such as convergence and robustness are explored and demonstrated through both simulations and experiments on a wafer scanning system. [ABSTRACT FROM AUTHOR]

Abstract Motion planning of multi-robot systems has been extensively investigated. Many proposed approaches assume that all robots are reliable. However, robots with priori known levels of reliability may be used in applications to account for: (1) the cost in terms of unit price per robot type, and (2) the cost in terms of robot wear in long term deployment. In the former case, higher reliability comes at a higher price, while in the latter replacement may cost more than periodic repairs, e.g., buses, trams, and subways. In this study, we investigate robust control of multi-robot systems, such that the number of robots affected by the failed ones is minimized. It should mandate that the failure of a robot can only affect the motion of robots that collide directly with the failed one. We assume that the robots in a system are divided into reliable and unreliable ones, and each robot has a predetermined and closed path to execute persistent tasks. By modeling each robot's motion as a labeled transition system, we propose two distributed robust control algorithms: one for reliable robots and the other for unreliable ones. The algorithms guarantee that wherever an unreliable robot fails, only the robots whose state spaces contain the failed state are blocked. Theoretical analysis shows that the proposed algorithms are practically operative. Simulations with seven robots are carried out and the results show the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic time-energy efficient robust control (ATERC) deployment approach for selecting either a near-optimal closed-loop control law or a robust control law based on the requirement of the system. The near-optimal closed-loop control law is designed by applying the population-based sine-cosine algorithm (SCA) to the considered interceptor problem. While the robust control law is formulated by using an artificial time delayed control (TDC) approach. In presence of external disturbances, the ATERC methodology deploys the TDC-based robust guidance law to the interceptor, while in the absence of such uncertainties the SCA-based near-optimal guidance law is applied in order to improve the time-energy minimization. This guidance approach also incorporates input saturation which expands its applicability. Using Lyapunov stability analysis, this work establishes an uniformly ultimately bounded (UUB) stability for the discussed system on application of the proposed control approach. Extensive simulation studies involving nonmaneuvering targets and targets performing bank-to-bank maneuver, affirms the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

This work presents the application of linear matrix inequalities (LMIs) to the robust adjustment of controllers with pre-defined structure to improve the damping of dynamical systems. The uncertainty is described using norm-bounded models. Results of some tests show that gain and zeros adjustments are sufficient to guarantee robust stability and performance with respect to various operating points. Making use of the flexible structure of LMIs, we propose an algorithm that guarantees the damping factor specified for the closed-loop system, always using a controller with flexible structure. The technique used here is the pole placement, whose objective is to place the poles of the closed-loop system in a specific region of the complex plane, for a set of operating conditions. Results of tests with an electrical power system are presented and compared to another robust control technique that makes use of polytopic models. [ABSTRACT FROM AUTHOR]

A Non-isothermal Jacketed Continuous Stirred Tank Reactor (CSTR) is extensively used in chemical as well as in other process industries to manufacture different products. The dynamics of non-isothermal CSTR are highly nonlinear and open-loop unstable in nature. Moreover, it may have parametric uncertainties, disturbances and un-modeled side reactions which may cause the reactor temperature to deviate from the reference value. This deviation may degrade quality of the product because the chemical reaction inside the CSTR depends on reactor temperature. For such a nonlinear, unstable and uncertain process, designing a control scheme with the ability to reject the effects of disturbances along with a good reference tracking capability is a challenging control engineering problem. In this work, a novel robust sliding mode control technique named as Improved Integral Sliding Mode Control (IISMC) has been presented for uncertain non-isothermal jacketed CSTR process. Moreover, a variety of recently developed sliding mode control techniques such as Classical Integral Sliding Mode Control (CISMC) and Super Twisted Algorithm based Sliding Mode Control (STA-SMC) have also been devised and compared with the proposed approach in order to investigate the effectiveness of the proposed scheme. A Lyapunov based analysis has also been provided to assure the robust stability of the closed loop process. Furthermore, in order to extend the state feedback approach to the output feedback scheme, two robust observers; High Gain Observer (HGO) and Extended High Gain Observer (EHGO), are also designed for the very process. They have also been compared with each other and have been investigated for robust stability using Lyapunov based approach. Finally, an output feedback control scheme using IISMC and EHGO has been presented and its performance has been examined and compared with the IISMC based state feedback approach. The simulation results show that the proposed control scheme effectively rejects the uncertainties and disturbances without leading the process to instability and offers good reference tracking capabilities. [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 this paper, we design a linear time-invariant two-input-two-output controller for the permanent magnet synchronous motor (PMSM). First, the nonlinear system of a PMSM is approximated using a linear system with structured uncertainties according to the geometric structures of PMSM. We then design a linear controller for the approximated linear system using a standard $\mu$-synthesis robust control method. The main contribution of this paper is that we recognize that the nonlinear PMSM can be reduced to a linear system to apply the mature modern control theory, without referring to classical PID control, thus largely reducing the design effort. The newly designed robust controller is not only easy to calculate, it is also easy to implement and requires less sensors. By virtue of modern robust control theory, it responds fast to exogenous inputs, and robust against parameter uncertainties as well. The applicability of the designed robust controller is both numerically simulated and experimentally verified on a TMS320 F28335 based control board, with performance further compared with the widely used field-oriented controller. [ABSTRACT FROM PUBLISHER]