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In multi-mass systems, torsional vibration is a common and annoying phenomenon. Effective vibration suppression and robustness to wide-range parameter variations are essential for a sound motion system. However, most control methods focus on the primary resonance mode, and the high-order resonance modes are not actively treated in the control design, resulting in the control bandwidth not being high enough and limiting the control performance. This paper proposes a novel two-stage design scheme to realize a wideband control to improve control performance. First, a hybrid uncertainty model is tailored for multi-mass systems, which uses an equivalent and uncertain spring constant to describe the variation of the primary mode and a dynamic uncertainty to cover the other resonance modes. This hybrid model strikes a better balance between the model conservatism and the feasibility of a less conservative design. Then, the passivity of the parameter uncertainty is utilized to conduct a phase compensation on the nominal system. After the phase compensation, all uncertainties are converted into norm-bounded ones, and the robust performance design is carried out. This method is applied to vehicle drivetrain benches, and its superiority is validated through simulation comparisons and experiments on two typical types of drivetrain benches. • Hybrid model for multi-mass systems characterizes primary resonance and other modes. • Two-stage design method with phase-shaping achieves high performance robust control. • The method is applied to drivetrain benches, outperforming the conventional methods. [ABSTRACT FROM AUTHOR]

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Summary: In this paper, we investigate the global adaptive stabilization problem via partial‐state feedback for a class of uncertain chained‐form nonholonomic systems with the dynamic uncertainty and nonlinear parameterization. The notions of Sontag's input‐to‐state stability (ISS) and ISS‐Lyapunov function, together with the changing supply rates technique are used to overcome the dynamic uncertainty. The nonlinear parameterization is well treated with the aid of the parameter separation technique. The discontinuous input‐to‐state scaling technique is employed in this procedure to derive the global stabilization controllers. Additionally, we develop a switching adaptive control strategy in order to get around the smooth stabilization burden associated with nonholonomic systems. The simulation results illustrate the efficacy of the presented algorithm. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Carbon capture and utilization (CCU), which emerged as a means to reduce anthropogenic carbon emissions, has been highlighted to close the carbon cycle and combat climate change. CCU involves utilizing or converting captured CO2 to create value-added products that can replace or supplement fossil fuel-derived products. In order to meet climate goals, commercial-scale CCU facilities need to be built and their capacities increased, but barriers to large-scale CCU deployment still exist, primarily caused by large uncertainties in product/energy prices, markets, technologies, and policies. Conventional techno-economic analysis (TEA) methods cannot appropriately assess viability of CCU deployment projects which include dynamic uncertainties and deployment strategies. More flexible methods that can account for both time-varying uncertainties and dynamic capacity building are needed. We propose a systematic framework for the evaluation of commercial CCU deployment using the theory of real options and reinforcement learning (RL). RL is needed as considering options of adding capacities or delaying/abandoning the project at multiple time points under dynamic uncertainties lead to a large-scale stochastic optimal control problem which cannot be solved using conventional optimization methods like stochastic programming. The framework consists of three steps: surrogate modeling, uncertainty modeling, and assessment modeling. The framework is dependent on the type of CCU technology, uncertainties, real options and RL algorithm. We demonstrate the application of the framework through a case study of CO2 hydrogenation-to-methanol process in Europe, which is a late-stage, i.e., high technology readiness level (TRL), technology.

Due to the limitation of machining accuracy, the transmission performance uncertainty of mass gears must be evaluated quantitatively to provide the basis for its application in the whole machine. Based on the polynomial chaotic expansion (PCE) method, a dynamic uncertainty analysis method for gear systems with a specified precision was proposed in this paper. Combined with tooth surface contact analysis and load-bearing contact analysis, a dynamic model of the gear system was established to fully reflect the influence of typical manufacturing errors. Based on this, a PCE model was established to approximate the system dynamics model. The dynamic uncertainty of the gear system was quantified based on the PCE approximation model and the Monte Carlo method, respectively, and the computational accuracy and efficiency of the PCE model with different orders and numbers of sample points were compared and analyzed. Finally, Sobol′ sensitivity indices from the PCE model of the gear system to random errors were computed, and the primary and secondary relationships of influence on the dynamic performance of the gear system were determined. The results showed that the PCE method had good applicability to the quantification of dynamic uncertainty and error sensitivity analysis of gear systems, and it had both accuracy and high efficiency. [ABSTRACT FROM AUTHOR]

This study explores the issue of adaptive stabilization for a class of high-order uncertain nonlinear systems with asymmetric output constraint and zero dynamics. By combining skillful Barrier Lyapunov Function (BLF) with the technique of continuous feedback domination equipped with a group of integral functions including nested sign functions, a continuous state feedback stabilizer is established, which guarantees that the closed-loop system’s states converge to zero asymptotically while keeping the asymmetric output restriction inviolate. Superior to the existing methods, the developed strategy can not only simultaneously cope with the output asymmetric constraints and dynamic uncertainties, but also unifies the construction and theory analysis for the limited and unlimited output. At last, a numerical simulation is offered to verify the efficiency of the developed method.

Abstract The length of flexible manipulators with a telescopic arm alters during movement. The dynamic parameters of telescopic flexible manipulators exhibit significant time-varying characteristics owing to variations in length. With an increase in the manipulators’ length, the nonlinear terms caused by flexibility in the manipulators’ dynamic equations cannot be ignored. The time-varying characteristics and nonlinear terms of telescopic flexible manipulators cause fluctuations in rotation angles, which affect the operation accuracy of end-effectors. In this study, a control strategy based on a combination of fuzzy adjustment and an RBF neural network is utilized to improve the control accuracy of flexible telescopic manipulators. First, the dynamic equation of the manipulators is established using the assumed mode method and Lagrange’s principle, and the influence of nonlinear terms is analyzed. Subsequently, a combined control strategy is proposed to suppress the fluctuation of the rotation angle in telescopic flexible manipulators. The variation ranges of the feedforward PD controller parameters are determined by the pole placement strategy and length of the manipulators. Fuzzy rules are utilized to adjust the controller parameters in real-time. The RBF neural network is utilized to identify and compensate the uncertain part of the dynamic model of the flexible manipulators. The uncertain part comprises time-varying parameters and nonlinear terms. Finally, numerical simulations and prototype experiments prove the effectiveness of the combined control strategy. The results prove that the proposed control strategy has a smaller standard deviation of errors. Therefore, the combined control strategy is more suitable for telescopic flexible manipulators, which can effectively improve the control accuracy of rotation angles.

In view of the shortcomings of existing task allocation strategies, this paper proposed a task allocation strategy in mobile crowd sensing with a limited number of workers and used social networks to allocate tasks to obtain high profits. Firstly, it established the dynamic uncertain environment of social network to complete and propagate task. Then, considering that different social networks have different preferences for tasks, this paper set the uncertain value of task preference, and described the reliability of task allocation with the help of the theory of economic value at risk. Finally, it used Monte Carlo Bayesian inference method to study the Gaussian process of complex parameters of task dynamic propagation model, and designed a sampling algorithm based on knowledge gradient to select monte carlo sampling points, so as to realize high profit task allocation strategy. In order to verify the performance of the proposed strategy, it is compared with four benchmark sampling algorithms. The experimental results show that the proposed task allocation strategy is effective in improving profit. [ABSTRACT FROM AUTHOR]

In this article, we investigate the global robust stabilisation problem for a class of uncertain chained-form non-holonomic systems. Three types of uncertainties are taken into account, including dynamic uncertainty, unknown control coefficients, and nonlinear parameterisation. Different from the existing works, the input-to-state stability (ISS) with unknown supply rates is used to characterise the system of dynamic uncertainty, and the magnitudes of the control coefficients are not required to be known a priori. The nonlinear parameterisation is well treated with the aid of a parameter separation technique. By constructing a switching control strategy, we propose a discontinuous adaptive stabilising control scheme via output feedback to overcome the serious uncertainties in the chained-form non-holonomic systems. The simulation results illustrate the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

Due to the limitation of machining accuracy, the transmission performance uncertainty of mass gears must be evaluated quantitatively to provide the basis for its application in the whole machine. Based on the polynomial chaotic expansion (PCE) method, a dynamic uncertainty analysis method for gear systems with a specified precision was proposed in this paper. Combined with tooth surface contact analysis and load-bearing contact analysis, a dynamic model of the gear system was established to fully reflect the influence of typical manufacturing errors. Based on this, a PCE model was established to approximate the system dynamics model. The dynamic uncertainty of the gear system was quantified based on the PCE approximation model and the Monte Carlo method, respectively, and the computational accuracy and efficiency of the PCE model with different orders and numbers of sample points were compared and analyzed. Finally, Sobol′ sensitivity indices from the PCE model of the gear system to random errors were computed, and the primary and secondary relationships of influence on the dynamic performance of the gear system were determined. The results showed that the PCE method had good applicability to the quantification of dynamic uncertainty and error sensitivity analysis of gear systems, and it had both accuracy and high efficiency.

• The Bayesian neural network propagation method based on the improved Monte Carlo sampling method can be applied not only to the uncertainty evaluation of different distributions, but also to the distribution of the same variable combination, which is more adaptable than the traditional MCM method, and the evaluation results are more stable. • The Bayesian neural network propagation method incorporates historical a priori information,which not only operates stably but also reduces the interference of other information and makes the evaluation results vary in a small range with good robustness. • By solving the multi-batch probability density function based on Gaussian mixture model by EM algorithm, the probability distribution of different batches can be responded dynamically from the macroscopic point of view. • From theoretical analysis to experimental validation, it is proved that the method can be used for cylindricity error uncertainty evaluation even with fewer samples, which provides a new idea for cylindricity error uncertainty evaluation. Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable. [ABSTRACT FROM AUTHOR]

Redundantly actuated distributed parallel manipulators (RADPMs) are widely used for posture alignment and assembly of large-scale components. The structural characteristics of multiple redundant actuation chains not only possess potential advantages, but also bring about challenges for multi-joint coordinated motion. To address the synchronization control issue of the system with dynamic uncertainties, a novel adaptive synchronous tracking control (ASTC) scheme is proposed to realize high-precision trajectory tracking and coordination performance simultaneously. In the proposed ASTC scheme, a synchronization error is first introduced to depict the coordination relationship between adjacent joints and coupled with the tracking error to form a composite error in the joint space. Based on the defined errors, a dual-space adaptation law is proposed through the linear parameterized expression of the system dynamic model to obtain feedforward compensation for dynamics. Additionally, in order to restrain the influence of inevitable external disturbances, a robust control compensation term is introduced to improve the disturbance rejection ability. Moreover, the stability of the entire closed-loop system is proved by utilizing the Lyapunov theory. Finally, simulation and experiments are conducted on an actual 4-PPPS RADPM, and the comparative results demonstrate that the proposed scheme can effectively improve the tracking accuracy and synchronization performance of the system. • An adaptive synchronous tracking control scheme is proposed for parallel manipulators. • The control scheme enhances the coordination of redundant actuation joints. • Uncertain dynamic parameters and unmodeled disturbances are taken into account. • The effectiveness of the scheme is verified through simulation and experiments. [ABSTRACT FROM AUTHOR]

Nonlinear control of turbofan engines in the flight envelope has attracted much attention in consideration of the inherent nonlinearity of the engine dynamics. Most nonlinear control design techniques rely on the correction theory of reference model parameter to extend the typical flight operations from ground operation. However, dynamic uncertainties in flight envelope lead to the deviation of operating state, and it is negative to control performance. This article is to develop online correction neural network–based speed control approaches for the turbofan engine with dynamic uncertainty in the flight envelope. Two improved online correction nonlinear ways combined with nonlinear autoregressive moving average (NARMA) are proposed, such as gradient search nonlinear autoregressive moving average with feedback linearization (NARMA-L2) control and iterative learning NARMA-L2 control. The contribution of this article is to provide better control quality of fast regulation and less steady errors of engine speed by the proposed methodology in comparison to the conventional NARMA-L2 control. Some important results are reached on both turbofan engine controller design and dynamic uncertainty tolerance at the typical flight operations, and the numerical examples demonstrate the superiority of the proposed control in the flight envelope. [ABSTRACT FROM AUTHOR]

The length of flexible manipulators with a telescopic arm alters during movement. The dynamic parameters of telescopic flexible manipulators exhibit significant time-varying characteristics owing to variations in length. With an increase in the manipulators' length, the nonlinear terms caused by flexibility in the manipulators' dynamic equations cannot be ignored. The time-varying characteristics and nonlinear terms of telescopic flexible manipulators cause fluctuations in rotation angles, which affect the operation accuracy of end-effectors. In this study, a control strategy based on a combination of fuzzy adjustment and an RBF neural network is utilized to improve the control accuracy of flexible telescopic manipulators. First, the dynamic equation of the manipulators is established using the assumed mode method and Lagrange's principle, and the influence of nonlinear terms is analyzed. Subsequently, a combined control strategy is proposed to suppress the fluctuation of the rotation angle in telescopic flexible manipulators. The variation ranges of the feedforward PD controller parameters are determined by the pole placement strategy and length of the manipulators. Fuzzy rules are utilized to adjust the controller parameters in real-time. The RBF neural network is utilized to identify and compensate the uncertain part of the dynamic model of the flexible manipulators. The uncertain part comprises time-varying parameters and nonlinear terms. Finally, numerical simulations and prototype experiments prove the effectiveness of the combined control strategy. The results prove that the proposed control strategy has a smaller standard deviation of errors. Therefore, the combined control strategy is more suitable for telescopic flexible manipulators, which can effectively improve the control accuracy of rotation angles. [ABSTRACT FROM AUTHOR]

This study aimed to eliminate dynamic uncertainty, one of the main problems of haptic teleoperation robotic systems. The optimal adaptive computed torque control method was used to overcome this problem. As is known, excellent stability and transparency are required in teleoperation systems. However, dynamic uncertainty that causes stability problems in the control of these systems also causes poor performance. In conventional adaptive computed torque control methods, updating the parameters of the system is generally discussed, but updating the control coefficients of vital importance in the control of the system is not considered. In the proposed method, an adaptation rule has been created to update uncertain parameters. In addition, the gray wolf optimization algorithm, one of the current optimization algorithms, has been proposed and applied to obtain the control coefficients of the system. The position tracking stability of the system was examined by using Lyapunov stability analysis method. As a result, both simulation and real-time optimal adaptive computational torque control method were used and bilateral position and force control was performed and the performance results of the system are obtained graphically and examined. Optimal adaptive computed torque control method obtained using the gray wolf optimization algorithm was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of both simulation and real time with the optimal adaptive computed torque control method. [ABSTRACT FROM AUTHOR]

This paper investigates the control issue of Euler-Lagrange systems (ELSs) subject to dynamic uncertainties and external disturbances under input and output constraints, and develops two neuroadaptive control schemes, i.e., direct neuroadaptive approximation method and indirect neuroadaptive approximation method. In the control design, a smooth saturation function called Gaussian error function is used to replace the saturation model, which is applied to solve the input saturation issue. Moreover, a new function is used to guarantee that the output does not violate the restricted boundary. The uncertain dynamic of the ELSs is reconstructed by the direct or indirect neuroadaptive method, and then a virtual-parameter learning method is proposed to reduce the computational load of control schemes. With the aid of Lyapunov stability theory, it is proven that all signals in the closed-loop control system are bounded and the tracking error of ELSs converges to zero under the proposed neuroadaptive control schemes. The simulations on a robotic manipulator illuminate the effectiveness and preponderance of the developed neuroadaptive control schemes.

In this paper, an observer-based adaptive fuzzy robust controller is proposed for trajectory tracking control of a bionic mechanical leg (BML) with unmeasured system states, dynamic uncertainties and external disturbances. A high gain observer (HGO) is constructed to estimate the unavailable joint velocities using the joint position feedback signals, while an adaptive fuzzy logic system (AFLS) is employed to address the lumped uncertainties. The nonlinear robust controller is then synthesized via backstepping method to improve the position tracking performance. The stability of the closed loop system is mathematically demonstrated via the Lyapunov’s stability theory. It is proven that under the proposed controller all the closed-loop signals are bounded and the trajectory tracking errors converge to a small neighborhood of the origin with appropriate design parameters. The effectiveness of the proposed control scheme is illustrated by simulation studies.

In this paper, a finite-time control method is presented for bilateral teleoperators to ensure coordination of the master and slave manipulators in the presence of time-varying delays, external disturbances, and dynamic uncertainties. Without the requirement of acceleration measurement, the master and slave manipulators are assured to coordinate with each other within finite time in free motion. Additionally, the coordination errors are ensured to converge to a neighborhood of the origin in finite time. In the constrained motion, where both the master and slave manipulators are affected by unknown external force from the human operator and the removed environment, the bilateral teleoperation system is proved to be stably provided a certain degree of transparency performance during operation. Stability and finite-time tracking are investigated via Lyapunov-Krasovskii theory for the bilateral teleoperation system under time-varying delays and external forces. Subsequently, numerical examples and experiments are presented to demonstrate the efficiency and efficacy of the proposed control approach for teleoperators.

This study presents a command filtered control scheme for multi-input multi-output (MIMO) strict feedback nonlinear unmodeled dynamical systems with its applications to power systems. To deal with dynamic uncertainties, a dynamic signal is introduced, together with radial basis function neural networks (RBFNNs) to overcome the influences of the dynamic uncertainties. Command filters (CFs) are used to prevent the explosion of complexity, where the compensating signals can eliminate the effect of filter errors. Compared with single-input single-output strict feedback nonlinear systems, the method proposed in this study has more suitability. In the end, the simulation experiments are carried out by applying the developed algorithm to power systems, where the simulation results verify the efficacy of the approach proposed.

In recent years, with the development of hyper-velocity weapon, the research focus of penetration effect has switched gradually from high-velocity to hyper-velocity, with insufficient studies on penetration effect and its internal mechanism. Besides, the study on penetration effect of hyper-velocity weapon depends on the measurement of mechanical signal during penetration of projectile. In this paper, dynamic test of hyper-velocity impact penetration acceleration signal is studied in depth. Firstly, the penetration velocity and acceleration of projectile under the impact load are analyzed, the influence of the movement of the testing unit on the measured acceleration signal is studied, and the factors to be considered when measuring the hyper-velocity impact load are given. Further, this paper designs a measurement circuit and dynamic models of each measurement module, details the influence of dynamic characteristics of power delivery network on test accuracy, and provides a method to obtain dynamic characteristics of power delivery network by virtue of S parameter. Finally, to extract the real penetration overload signal, a superior noise reduction algorithm adopting Modified Ensemble Empirical Mode Decomposition (MEEMD) is proposed, taking dynamic uncertainty and degree of approximation as evaluation indicators, and the measured sensor data is analyzed and processed with the proposed superior noise reduction algorithm. The experiment results show that, this method has a better noise reduction effect, and can be adopted to effectively extract real penetration overload signal. The results have theoretical research and application value to evaluate the reasonableness and effectiveness of measurement method of hyper-velocity penetration overload signal, and seek for a superior processing method for the noisy penetration overload signal.

In modern warfare, in order to destroy the target to largest extent, missile warhead is required to explode after penetrating inside the target, therefore, the measurement of mechanical signal during projectile penetration is critical. This paper studies the dynamic design principle of projectile penetration overload test system based on charge sensor, according to characteristics of steep front and a great number of contained installing structure impulse response, designs the measuring circuit based on charge sensor, conducts detailed theoretical deduction on dynamic model of sensor, charge-amplifier, filter and single conversion differential circuit, and provides theoretical basis for design. In order to more objectively evaluate the dynamic characteristics of this kind of test system, the dynamic uncertainty estimation of charge signal measured by this test system is considered as the standard for evaluating its dynamic characteristics, instead of unflatness of amplitude-frequency characteristic and its width in frequency domain, and the comprehensive evaluation on uncertainty of this test system is realized. In order to improve the measurement accuracy of dynamic test system designed, in this paper, an improved de-noising algorithm based on Ensemble Empirical Mode Decomposition (EEMD) is proposed, different low-pass filters are formed on the basis of Intrinsic Mode Function (IMF), de-noising effect of signal with noise is analyzed, and the evaluation indicators of dynamic uncertainty and approximation are established. Finally, the effectiveness of the designed method is verified through experiment.

For the position and velocity tracking of multi-input and multi-output nonlinear dual-robot system with time delay and dynamic uncertainty, an adaptive fuzzy wavelet network sliding mode control scheme is proposed. The integral sliding mode is designed as the inputs of the controller to reduce the number of the inputs of the system and the fuzzy rules. Then, for the problems of time delay and unknown nonlinear dynamics of the system, a fuzzy wavelet network controller is designed combing the approximation ability of the fuzzy system with the learning ability of the wavelet network. Moreover, switching control is designed to reduce the approximation error. Finally, the adaptive laws are designed based on the Lyapunov function to adjust the parameters of the controller in real time. The performance of the proposed control is compared to that of the conventional adaptive fuzzy control in the experimental setup with the two Phantom Omni robots. The experimental results show that better joint position and velocity tracking can be achieved under time delay and dynamic uncertainty with the proposed adaptive fuzzy wavelet network sliding mode control.

Featured Application: The control strategy proposed to this paper can be applied to the joint position and velocity tracking down industrial robots (series or parallel manipulators). Theoretically, it is suitable for general second-order nonlinear systems, such as inverted pendulum control, motor coupling control, dual manipulator cooperative control, etc. Background: As a control strategy of industrial robots, sliding mode control has the advantages of fast response and simple physical implementation, but it still has the problems of chattering and low tracking accuracy caused by chattering. This paper proposes a new sliding mode control strategy for the application of industrial robot control, which effectively solves these problems. Methods: In this paper, a deep deterministic policy gradient–nonlinear nonsingular fast terminal sliding mode control (DDPG–NNFTSMC) strategy is proposed for industrial robot control. In order to improve the tracking control accuracy and anti-interference ability, DDPG is used to approach the uncertainties of the system in real time, which ensures the robustness of the system in various uncertain environments. Lyapunov function is used to prove the stability and finite time convergence of the system. Compared with the nonsingular terminal sliding mode control (NTSMC), the time to reach the equilibrium point is shorter. With the help of MATLAB/Simulink, the tracking accuracy and control effects are compared with traditional terminal sliding mode control (TSMC), NTSMC and radial basis function–sliding mode control (RBF–SMC), the results showed that it had the advantages of nonsingularity, finite time convergence, small tracking error. The motion accuracy and anti-interference ability of the uncertain manipulator system was further improved, and the chattering problem of the system in the motion process is effectively eliminated. [ABSTRACT FROM AUTHOR]

The varying real-world road conditions (eg. slopes, speed limits) have great effects on the vehicle dynamics and fuel characteristics. The lack of consideration for these effects may lead to deteriorative fuel economy and even collisions. This paper presents a hierarchical control framework for a connected and automated vehicular platoon subject to time-vary uncertain dynamics and uniform communication delays. This control method minimizes the fuel consumptions and ensures the tracking performance on the premise of collision-free property. Firstly, a centralized ecological speed planning (CESP) algorithm based on dynamic programing (DP) is designed as the upper layer. This layer produces the fuel-optimal and feasible speed profiles, which can be followed by all vehicles, for the entire platoon based on the preview information of road conditions (eg. slopes, speed limits). Secondly, the distributed collision-free speed tracking control (DCST) method is proposed in the lower real-time control layer. The collision-free property is guaranteed by the uniform boundedness performance of the well-designed potential function of the spacing error. The convergences of all speed tracking errors are proved through barbalat's lemma. By modifying the desired spacing model, the application range of DCST is extended to the conditions with large initial deviations. Finally, the main theoretical results are verified through numerical simulations. [ABSTRACT FROM AUTHOR]

Summary: In this article, we focus on developing a neural‐network‐based critic learning strategy toward robust dynamic stabilization for a class of uncertain nonlinear systems. A type of general uncertainties involved both in the internal dynamics and in the input matrix is considered. An auxiliary system with actual action and auxiliary signal is constructed after dynamics decomposition and combination for the original plant. The reasonability of the control problem transformation from robust stabilization to optimal feedback design is also provided theoretically. After that, the adaptive critic learning method based on a neural network is established to derive the approximate optimal solution of the transformed control problem. The critic weight can be initialized to a zero vector, which apparently facilitates the learning process. Numerical simulation is finally presented to illustrate the effectiveness of the critic learning approach for neural robust stabilization. [ABSTRACT FROM AUTHOR]

This paper presents an integrated framework that integrates the kinematic and dynamic parameter estimation of an irregular object with non-uniform mass distribution for cooperative aerial manipulators. Unlike existing approaches, including impedance-based control which requires expensive force/torque sensors or the first-order-momentum-based estimator which is weak to noise, this paper suggests a method without such sensor and strong to noise by exploiting the decentralized dynamics and sliding-mode-momentum observer. First, the kinematic estimator estimates the relative distances of multiple aerial manipulators by using translational and angular velocities between aerial robots. By exploiting the distance estimation, the desired trajectories for each aerial manipulator are set. Second, the dynamic parameter estimation is performed for the mass of the common object and the vector between the end-effector frame and the center of mass of the object. Finally, the proposed framework is validated with simulations using aerial manipulators combined with two degrees-of-freedom robotic arms using a noisy measurement. Throughout the simulation, we can decrease the mass estimation error by 60% compared to the existing first-order momentum-based method. In addition, a comparison study shows that the proposed method satisfactorily estimates an arbitrary center-of-mass of an unknown payload in noisy environments.

This paper investigates the adaptive control design problem for time-delayed bilateral teleoperation systems with dynamic and kinematic uncertainties. The majority of the previous investigations in the field of teleoperation systems have only considered the dynamic uncertainties of robots. However, this research studies simultaneous adaptation to both dynamic and kinematic uncertainties. In the presented adaptive control structure, the dynamic and kinematic parameters of the robots are estimated through the proposed adaptive laws and the estimated parameters are utilized to apply amodel-based control law to the teleoperation system. The stability analysis of the teleoperation system with time delay and uncertainties in both kinematic and dynamic parameters is studied based on the Input-to-State Stability (ISS) approach. Simulation results are presented to study the performance of the proposed control structure.

Background: As a control strategy of industrial robots, sliding mode control has the advantages of fast response and simple physical implementation, but it still has the problems of chattering and low tracking accuracy caused by chattering. This paper proposes a new sliding mode control strategy for the application of industrial robot control, which effectively solves these problems. Methods: In this paper, a deep deterministic policy gradient–nonlinear nonsingular fast terminal sliding mode control (DDPG–NNFTSMC) strategy is proposed for industrial robot control. In order to improve the tracking control accuracy and anti-interference ability, DDPG is used to approach the uncertainties of the system in real time, which ensures the robustness of the system in various uncertain environments. Lyapunov function is used to prove the stability and finite time convergence of the system. Compared with the nonsingular terminal sliding mode control (NTSMC), the time to reach the equilibrium point is shorter. With the help of MATLAB/Simulink, the tracking accuracy and control effects are compared with traditional terminal sliding mode control (TSMC), NTSMC and radial basis function–sliding mode control (RBF–SMC), the results showed that it had the advantages of nonsingularity, finite time convergence, small tracking error. The motion accuracy and anti-interference ability of the uncertain manipulator system was further improved, and the chattering problem of the system in the motion process is effectively eliminated.

Stabilization control is an essential mission for the tethered space robot-target combination during the post-capture phase of tethered space robot (TSR). With the consideration of the space tether and the three dimensional attitude of the post-capture combination, dynamic model of the combination is derived by using Lagrange method. Considering the unknown dynamic parameters (the tether attachment point, unknown target inertia parameters) of the post-capture combination, an adaptive anti-windup control approach is presented to overcome the problem of dynamic uncertainty and control input saturation. Complete stability and performance analyses are presented and illustrative simulation results of application to the post-capture combination system verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract Future Mars missions may require improved landed accuracy to facilitate the landing site selection and finally access a region of complex terrain with high scientific return. This paper is to develop a novel, robust, and precision entry guidance algorithm for Mars entry vehicles with low lift-to-drag ratios. In the presence of large uncertainties, the entry terminal point controller algorithm can encounter severe performance degradation due to: (1) the small perturbation assumption, and (2) theoretically ignoring deviations in the atmospheric-density model, aerodynamic-force model, etc. Based on numerical technologies and the classical variation method (VM), this work develops a semi-analytical (SA) algorithm, in which combined effects of several dynamic uncertainties now can be addressed. The terminal-downrange error is predicted by a numerical predictor such that the dependence on the reference trajectory can be reduced and then the issue caused by the small perturbation assumption can be addressed. Such a predicted terminal-downrange error is finally corrected by an analytical corrector, which is designed by the VM. It is indicative that there can be no numerical iterations in the SA algorithm. Numerical results demonstrate the effectiveness of the SA algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive control design problem for time-delayed bilateral teleoperation systems with dynamic and kinematic uncertainties. The majority of the previous investigations in the field of teleoperation systems have only considered the dynamic uncertainties of robots. However, this research studies simultaneous adaptation to both dynamic and kinematic uncertainties. In the presented adaptive control structure, the dynamic and kinematic parameters of the robots are estimated through the proposed adaptive laws and the estimated parameters are utilized to apply amodel-based control law to the teleoperation system. The stability analysis of the teleoperation system with time delay and uncertainties in both kinematic and dynamic parameters is studied based on the Input-to-State Stability (ISS) approach. Simulation results are presented to study the performance of the proposed control structure. [ABSTRACT FROM AUTHOR]

Carbon capture and utilization (CCU), which emerged as a means to reduce anthropogenic carbon emissions, has been highlighted to close the carbon cycle and combat climate change. CCU involves utilizing or converting captured CO 2 to create value-added products that can replace or supplement fossil fuel-derived products. In order to meet climate goals, commercial-scale CCU facilities need to be built and their capacities increased, but barriers to large-scale CCU deployment still exist, primarily caused by large uncertainties in product/energy prices, markets, technologies, and policies. Conventional techno-economic analysis (TEA) methods cannot appropriately assess viability of CCU deployment projects which include dynamic uncertainties and deployment strategies. More flexible methods that can account for both time-varying uncertainties and dynamic capacity building are needed. We propose a systematic framework for the evaluation of commercial CCU deployment using the theory of real options and reinforcement learning (RL). RL is needed as considering options of adding capacities or delaying/abandoning the project at multiple time points under dynamic uncertainties lead to a large-scale stochastic optimal control problem which cannot be solved using conventional optimization methods like stochastic programming. The framework consists of three steps: surrogate modeling, uncertainty modeling, and assessment modeling. The framework is dependent on the type of CCU technology, uncertainties, real options and RL algorithm. We demonstrate the application of the framework through a case study of CO 2 hydrogenation-to-methanol process in Europe, which is a late-stage, i.e., high technology readiness level (TRL), technology. • RL-based methodology to determine investment decisions for commercial carbon capture and utilization deployment is proposed. • Real options is exercised to enable flexible investment decisions for carbon capture and utilization deployment. • Market, technology, and policy uncertainties are considered to evaluate future cash flows and emission reduction. • The proposed methodology is demonstrated with a case study: CO 2 hydrogenation to methanol process in Europe. [ABSTRACT FROM AUTHOR]

The way of expressing the dynamic uncertainty of measurement that allows using the spectral function of the input signal and the frequency response of the measurement tools used to assess the uncertainty in its dynamic operation.

Featured Application: The proposed control methodology could be applied to not only the joint position tracking control for industrial robotic manipulators such as serial, parallel robots, and an electrohydraulic series elastic manipulator, but also other mechanical systems that belong to the class of general nonlinear second-order system. For example, it could be applied for the stabilization or trajectory tracking of mechanical systems as a piezo positioning stage, magnetic levitation systems, or for the chaos control, synchronization, and anti-synchronization of chaotic complex systems. In this study, a robust control strategy is suggested for industrial robotic manipulators. First, to minimize the effects of disturbances and dynamic uncertainties, while achieving faster response times and removing the singularity problem, a nonsingular fast terminal sliding function is proposed. Second, to achieve the proposed tracking trajectory and chattering phenomenon elimination, a robust control strategy is designed for the robotic manipulator based on the proposed sliding function and a continuous adaptive control law. Furthermore, the dynamical model of the robotic system is estimated by applying a radial basis function neural network. Thanks to those techniques, the proposed system can operate free of an exact robotic model. The suggested system provides high tracking accuracy, robustness, and fast response with minimal positional errors compared to other control strategies. Proof of the robustness and stability of the suggested system has been verified by the Lyapunov theory. In simulation analyses, the simulated results present the effectiveness of the suggested strategy for the joint position tracking control of a 3-degree of freedom (3-DOF) PUMA560 robot. [ABSTRACT FROM AUTHOR]

We present new approaches to designing structured controllers, which are robust in the presence of mixed real parametric and complex dynamic uncertainty. As the synthesis of such controllers is inherently NP-hard, we discuss inner and outer relaxation techniques, which make this problem amenable to computations. Our relaxation methods are positively evaluated and compared on the basis of a rich test set and for a challenging missile pilot design problem. [ABSTRACT FROM AUTHOR]

The power grasping problem in robot manipulators in the presence of kinematic uncertainties and uncontrollable forces is studied in this paper. We consider a whole-limb manipulator system able to use not only its end-effector but also all other parts of its body for manipulation and grasping. Accordingly, grasping is not limited to wrapping the robotic arm around the object, and the desired force can be applied in contact points. By proposing an optimal adaptive control scheme, optimal contact forces are shown to be regulated by force decomposition. Power grasping in the presence of kinematic uncertainties and uncontrollable contact forces for multiple whole-limb manipulators is the main contribution of the paper as compared to existing results in the literature. Experimental results demonstrate the validity of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the global robust output tracking control problem via output feedback for a class of nonlinear uncertain systems with the integral input-to-state stable (iISS) dynamic uncertainties. By performing the coordinates transformation and employing an extended state observer (ESO), it can be seen that the robust setpoint tracking control is well solved. The proposed control strategy simplifies the control design procedure. This control scheme finds its application in the mass-spring mechanical system. It is shown that the asymptotic tracking control for any desired displacement can be achieved in the mass-spring mechanical system in the case of unknown parameters. The simulation results demonstrate that the proposed control scheme has a better tracking performance. This verifies the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]