This paper focuses on adaptive prescribed performance control for nonlinear systems with parametric uncertainties. The proposed control scheme incorporates a certainty equivalence controller, a batch least-squares ide...
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ISBN:
(纸本)9798331518509;9798331518493
This paper focuses on adaptive prescribed performance control for nonlinear systems with parametric uncertainties. The proposed control scheme incorporates a certainty equivalence controller, a batch least-squares identifier (BaLSI) and a performance triggered condition. The off-line BaLSI, which utilizes all the previously appeared excitation information for parameter updating, is activated as intervals by the performance triggered condition. The effects of the parametric uncertainties are eliminate in finite times of updating, and the closed-loop system can achieve the prescribed performance without suffering from stiff differential equation problem. The simulation results are provided to demonstrate the effectiveness of the proposed control scheme.
Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame mat...
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ISBN:
(纸本)9798350384581;9798350384574
Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame matching. The accuracy and sensitivity of existing degeneracy detection methods need to be further improved. In this paper, we propose a novel method for degeneracy detection using local geometric models based on point-to-distribution matching. To obtain an accurate description of local geometric models, an adaptive adjustment of voxel segmentation according to the point cloud distribution and density is designed. The codes of the proposed method is open-source and available at https://***/jisehua/***. Experiments with public datasets and self-build robots were conducted to evaluate the methods. The results exhibit that our proposed method achieves higher accuracy than the other existing approaches. Applying our proposed method is beneficial for improving the robustness of the LiDAR-SLAM systems.
Swarm robotics is an emerging field of research which is increasingly attracting attention thanks to the advances in robotics and its potential applications. However, despite the enthusiasm surrounding this area of re...
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ISBN:
(纸本)9783031709319;9783031709326
Swarm robotics is an emerging field of research which is increasingly attracting attention thanks to the advances in robotics and its potential applications. However, despite the enthusiasm surrounding this area of research, software development for swarm robotics is still a tedious task. That fact is partly due to the lack of dedicated solutions, in particular for low-cost systems to be produced in large numbers and which can have important resource constraints. To address this issue, we introduce BittyBuzz, a novel runtime platform: it allows Buzz, a domain-specific language, to run on microcontrollers while maintaining dynamic memory management. BittyBuzz is designed to fit a flash memory as small as 32 kB (with usable space for scripts) and work with as little as 2 kB of RAM. We introduce the BittyBuzz implementation, its differences from the original Buzz virtual machine, and its advantages for swarm roboticssystems. We show that BittyBuzz is successfully integrated with three robotic platforms with minimal memory footprint and conduct experiments to show computation performance of BittyBuzz. Results show that BittyBuzz can be effectively used to implement common swarm behaviors on many microcontroller-based robot systems.
There has been a growing interest in parallel strategies for solving trajectory optimization problems. One key step in many algorithmic approaches to trajectory optimization is the solution of moderately-large and spa...
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ISBN:
(纸本)9798350384581;9798350384574
There has been a growing interest in parallel strategies for solving trajectory optimization problems. One key step in many algorithmic approaches to trajectory optimization is the solution of moderately-large and sparse linear systems. Iterative methods are particularly well-suited for parallel solves of such systems. However, fast and stable convergence of iterative methods is reliant on the application of a high-quality preconditioner that reduces the spread and increase the clustering of the eigenvalues of the target matrix. To improve the performance of these approaches, we present a new parallel-friendly symmetric stair preconditioner. We prove that our preconditioner has advantageous theoretical properties when used in conjunction with iterative methods for trajectory optimization such as a more clustered eigenvalue spectrum. Numerical experiments with typical trajectory optimization problems reveal that as compared to the best alternative parallel preconditioner from the literature, our symmetric stair preconditioner provides up to a 34% reduction in condition number and up to a 25% reduction in the number of resulting linear system solver iterations.
Consider Lagrange systems with uncertain dynamics and unknown disturbances under input saturations in this paper. A disturbance observer is constructed to provide the estimate of the total disturbance lumped by uncert...
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ISBN:
(纸本)9798350350319;9798350350302
Consider Lagrange systems with uncertain dynamics and unknown disturbances under input saturations in this paper. A disturbance observer is constructed to provide the estimate of the total disturbance lumped by uncertain dynamics and unknown disturbances of Lagrange systems. A new specified performance function with saturation characteristics (SPFSC) is developed to handle input saturations. Further, incorporating the developed SPFSC into a barrier function, based on the disturbance observer and the barrier function, a proportional-derivative controller with specified performance is proposed for position stabilization of Lagrange systems such that the position stabilization error settles within a specified tolerance error band in a specified settling time. Therein, the problem that the bandwidth of the controller approaches infinity when the time approaches the specified settling time is solved due to the SPFSC-incorporated barrier function being injected into the position stabilization controller as its bandwidth and properly choosing the parameters of the SPFSC. Simulation results on a three-degrees-of-freedom parallel robot show the effectiveness of the developed controller.
This paper investigates region tracking problem for a nonlinear system subject to measurement noise. Its purpose is to make enough use of the prescribed boundaries to reduce the fluctuations of control inputs and ener...
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ISBN:
(纸本)9798331518509;9798331518493
This paper investigates region tracking problem for a nonlinear system subject to measurement noise. Its purpose is to make enough use of the prescribed boundaries to reduce the fluctuations of control inputs and energy consumption. A novel region tracking control scheme is proposed based on bistable stochastic resonance. To construct a bistable stochastic resonance model, the tracking error is treated the Brownian particle and a weak periodic signal is added to the model, while the error in the dynamic loop is considered as noise. If the potential parameters are properly selected, the optimal stochastic resonance of the tracking error occurs. Then, the tracking error will switch between the potential wells determined by the potential parameters. Finally, the new design is applied on an over-actuated underwater vehicle. The results verify the superiority of the proposed controller in region tracking performances, including the use of the prescribed boundaries and fluctuations of control inputs.
This article proposes an efficient robust model predictive control (MPC) framework for constrained linear time-invariant (LTI) systems in the presence of additive disturbance. In the framework, a novel MPC optimizatio...
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ISBN:
(纸本)9798350385731;9798350385724
This article proposes an efficient robust model predictive control (MPC) framework for constrained linear time-invariant (LTI) systems in the presence of additive disturbance. In the framework, a novel MPC optimization problem is formulated by using control parameterization based on Gaussian kernels. Then, an efficient MPC control law is derived, which aims at reducing the online computational cost. We show that the controlled system driven by the designed control law complies with system constraints. Moreover, the robust stability (i.e., the state trajectory converges into a bounded set) of the closed-loop system is obtained given feasibility is fulfilled and the Gaussian kernels are properly designed. Numerical examples and comparisons with the conventional robust MPC are performed, which validate the proposed method.
Automated milking systems are sophisticated pieces of equipment that find a very large application on modern farms. They carry out milking processes completely autonomously, thanks to a robotic arm that is equipped wi...
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Automated milking systems are sophisticated pieces of equipment that find a very large application on modern farms. They carry out milking processes completely autonomously, thanks to a robotic arm that is equipped with numerous sensors and auxiliary devices. In this paper, the components of automated milking systems are analyzed in terms of their technological characteristics, focusing mainly on the robots used. Typically, these systems use specialized robots that have a different design structure from industrial robots and are equipped with attachments to perform different operations. As a result of the technological analysis of the systems and robots used in automated milking systems, we can summarize that the development of robotics in the livestock sector has made a significant contribution to improving milking and animal care processes. The main issues that need to be further investigated are related to improved methods for collecting animal data and milk parameters, methods and algorithms for better udder and teat recognition and localization of cows, providing improvedmobility and control of robotic arms, improving additional attachments and adding additional functionality and modularity to robotic arms.
One of the major challenges in the design and construction of soft-rigid hybrid systems is having robust bonding at soft-rigid interfaces. Soft robots tend to be compliant and adaptive but weak, while rigid robots ten...
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ISBN:
(纸本)9798350381818
One of the major challenges in the design and construction of soft-rigid hybrid systems is having robust bonding at soft-rigid interfaces. Soft robots tend to be compliant and adaptive but weak, while rigid robots tend to be strong and precise but uncompromising. Soft-rigid hybrid systems can provide a blend of both compliant interactions with environments as well as fast and precise body position controls. In this paper, we propose a fabrication strategy using flocking to achieve strong bonding between soft and rigid parts. Flocking is a fabrication method that bonds short fibers to fabrics or plastics. The fibers create a fuzzy surface texture on rigid components, which increases the surface area. In the context of soft robotic molding, flocked surface texture increases mechanical bonding between soft and rigid components and enables incorporation of rigid components with increased complexity or challenging placement that could be overmolded but not glued. In this paper, we investigate design parameters for flocking such as substrate materials, adhesives, and flocking materials;we recommend design and fabrication guidelines for the use of flocking to incorporate printed ABS and PLA components in silicone. To demonstrate the utility of flocking in a range of soft systems, we have fabricated several example soft systems with integrated components, including a pneumatic network (pneu-net) actuator, soft chambers connected to semi-rigid tubing, and a sensorized soft actuator. The performance of these demonstrations was comparable or exceeded that of silicone glues and allows for direct overmolding of complex structures, making flocking applicable and versatile in soft-rigid hybrid systems.
In this article, the main focus is finite-time fault-tolerant consensus problem of second-order nonlinear multiagent systems with input quantization and channel fading. By using neural networks to approximate nonlinea...
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ISBN:
(纸本)9798350350319;9798350350302
In this article, the main focus is finite-time fault-tolerant consensus problem of second-order nonlinear multiagent systems with input quantization and channel fading. By using neural networks to approximate nonlinear terms, more accurate estimation of nonlinear functions can be achieved. Combining sliding mode controller with neural networks to improve the robustness. Combining control theoretical knowledge, a detailed analysis was conducted on the consensus analysis under channel fading and input quantization. Examples and simulation results were provided to verify the effectiveness of theoretical results.
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