Advanced Control for Applications最新文献

We consider the problem of shaping the transient step response of nonlinear systems to satisfy a class of integral constraints. Such constraints are inherent in hybrid energy systems consisting of energy sources and storage elements. While typical transient specifications aim to minimize overshoot, this problem is unique in that it requires the presence of an appreciable overshoot to satisfy the foregoing constraints. The problem was previously studied in the context of linear systems and this article extends that work to nonlinear systems. A combined integral and feedforward control, that requires minimal knowledge of the plant model, is shown to make the system amenable to meeting such constraints. Broadly, the compensation is effective for nonlinear plants with stable open-loop step response and a positive DC gain. However, stability of the resulting closed-loop system mandates bounds on the integral gain. In this regard, we state and prove generalized stability theorems for first and higher-order nonlinear plants.

Concepts for control of wind farms (WFs) can be clustered into two distinct concepts, namely, wind power plant control (WPPC) and wind farm flow control (WFFC). WPPC is concerned with the connection to the power system, compliance with grid codes, and provision of power system services. This comprises the traditional way of operating a WF without consideration of aerodynamic turbine interaction. However, flow phenomena like wake effects can have a large impact on the overall performance of the WF. WFFC considers such aerodynamic phenomena in the WF operation. It can be viewed as a new feature that shall be integrated with the existing control functions. The interaction of these different control concepts is discussed in this article, leading to an identification of the challenges whose solutions will contribute to a successful integration of electrical system and aerodynamic aspects of WF control.

We deal with a new maximum principle-based stochastic control model for river management through operating a dam and reservoir system. The model is based on coupled forward–backward stochastic differential equations (FBSDEs) derived from jump-driven streamflow dynamics and reservoir water balance. A continuous-time branching process with immigration driven by a tempered stable subordinator efficiently describes clustered inflow streamflow dynamics. This is a completely new attempt in hydrology and control engineering. Applying a stochastic maximum principle to the dynamics based on an objective functional for designing cost-efficient control of dam and reservoir systems leads to the FBSDEs as a system of optimality equations. The FBSDEs under a linear-quadratic ansatz lead to a tractable model, while they are solved numerically in the other cases using a least-squares Monte-Carlo method. Optimal controls are found in the former, while only sub-optimal ones are computable in the latter due to a hard state constraint. Model parameters are successfully identified from a real data of a river in Japan having a dam and reservoir system. We also show that the linear-quadratic case can capture the real operation data of the system with underestimation of the outflow discharge. More complex cases with a realistic time horizon are analyzed numerically to investigate impacts of considering the environmental flows and seasonal operational purposes. Key challenges towards more sophisticated modeling and analysis with jump-driven FBSDEs are discussed as well.

This article investigates the problem of communication relay establishment for multiple agent-based mobile units using a relay vehicle. The objective is to drive autonomously the relay vehicle to attain a position for communication relay establishment while maintaining the other vehicles inside of its field-of-view. A bearing-based control law is proposed for the relay drone and designed for both single and multiple agents. We also provide a collision avoidance scheme that ensures no collisions between the relay and other agents. Numerical simulations and experimental results are reported as well to show the efficacy of the proposed approach.

Predictive functional control (PFC) is a straightforward and cheap model-based technique for systematic control of well-damped open-loop processes. Nevertheless, its oversimplified design characteristics are often the cause of diminished efficacy in more challenging applications; processes involving lightly damped and/or unstable dynamics have been particularly difficult to control with PFC. This paper presents a more sustainable solution for such applications by integrating the concept of prestabilization within the predictive functional control formulation. This is essentially a two-stage synthesis wherein the undesirable open-loop dynamics are first compensated, using a well-understood classical approach such as proportional integral derivative (PID), before implementing predictive control in a cascade structure. The proposal, although comes with significant implications for tuning and constraint handling, is, nonetheless, straightforward and provides improved closed-loop control in the presence of external perturbations compared to the standard PFC and the PID algorithms, as demonstrated with two industrial case studies.

The Action Governor (AG) is a supervisory scheme augmenting a nominal control system in order to enhance the system's safety and performance. It acts as an action filter, monitoring the action commands generated by the nominal control policy and adjusting the ones that might lead to undesirable system behavior. In this article, we present an approach based on learning to developing an AG for autonomous vehicle (AV) decision policies to improve their safety for operating in mixed-autonomy traffic (i.e., traffic involving both AVs and human-operated vehicles (HVs)). To achieve this, we demonstrate that it is possible to train the AG in a traffic simulator that is capable of representing in-traffic interactions among AVs and HVs. We illustrate the effectiveness of this learning-based AG approach to improving AV in-traffic safety through simulation case studies.

The article proposes flatness-based control and a Kalman filter-based disturbance observer for solving the control problem of a robotic exoskeleton under time-delayed exogenous disturbances. A two-link lower-limb robotic exoskeleton is used as a case study. It is proven that this robotic system is differentially flat. The robot is considered to be subject to unknown contact forces at its free-end which in turn generate unknown disturbance torques at its joints. It is shown that the dynamic model of the robotic exoskeleton can be transformed into the input–output linearized form and equivalently into the linear canonical Brunovsky form. This linearized description of the exoskeleton's dynamics is both controllable and observable. It allows for designing a stabilizing feedback controller with the use of the pole-placement (eigenvalues assignment) method. Moreover, it allows for solving the state estimation problem with the use of Kalman Filtering (the use of the Kalman filter on the flatness-based linearized model of nonlinear dynamical systems is also known as derivative-free nonlinear Kalman filtering). Furthermore, (i) by extending the state vector of the exoskeleton after considering as additional state variables the additive disturbance torques which affect its joints and (ii) by redesigning the Kalman filter as a disturbance observer, one can achieve the real-time estimation of the perturbations that affect this robotic system. Finally, by including in the controller of the exoskeleton additional terms that compensate for the estimated disturbance torques, the perturbations' effects can be eliminated and the precise tracking of reference trajectories by the joints of this robot can be ensured.

Astronomical observations in the molecule rich 3-mm window using large reflector antennas provide a unique view of the Universe. To efficiently carry out these observations gravitational and thermal deformations have to be corrected. Terrestrial laser scanners have been used to measure the deformations in large reflector antennas due to gravity, but have not yet been used for measuring thermal deformations. In this work, we investigate the use of a terrestrial laser scanner to measure thermal deformations on the primary reflector of the Green Bank Telescope (GBT). Our method involves the use of differential measurements to reduce the systematic effects of the terrestrial laser scanner. We use the active surface of the primary reflector of the GBT to validate our method and explore its limitations. We find that when using differential measurements it is possible to accurately measure deformations corresponding to different Zernike polynomials down to an amplitude of 60 $��\mu �$m. The difference between the amplitudes of known deformations and those measured are $��<�140��\mu �$m when the wind speed is $��\lesssim �2�$ m s$��{}^{�-�1�}�$. From these differences we estimate that it should be possible to bring the surface error of the GBT down to $��240�\pm �6��\mu �$m. This suggests that using a commercial off-the-shelf terrestrial laser scanner it is possible to measure deformations induced by thermal gradients on a large parabolic reflector.

The finite-time stability (FTS) of a symmetrically interconnected system is studied under the consideration of utilizing the system structural properties to reduce the complexity of analysis. The FTS of this kind of large-scale system can be guaranteed by that of two lower dimensional systems. The concept of mixed stability, taking care of both transient and steady-state performances of a system, is introduced. Based on the FTS analysis, a decentralized finite-time controller via state feedback is given to stabilize the large-scale system. Meanwhile, an observer-based decentralized output feedback controller is provided to make the closed-loop system finite-time stable. The FTS conditions and related decentralized stabilization controller parameters are both derived from solving some differential or algebraic linear matrix inequalities.