control design and state estimation are usually more straightforward for linear than for nonlinear dynamical systems, which has motivated the development of methods for quantifying the extent of nonlinearity in dynami...
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control design and state estimation are usually more straightforward for linear than for nonlinear dynamical systems, which has motivated the development of methods for quantifying the extent of nonlinearity in dynamical systems. Although many well-defined methods have been proposed for systems described by ordinary differential equations, such methods are not as well explored for dynamical systems described by PDEs and descriptor systems that represent most chemical processes. This paper reviews, discusses, and compares methods for the definition and computation of nonlinearity measures. The measures are categorized in terms of open- vs. closed-loop control topologies, theoretical vs. numerically computed, state transformation dependency, input scaling dependency, linearization vs. optimized linear modeling vs. average linear modeling, applicability to unstable dynamical systems, and applicability to the right-hand side of the state equation or to input-output relationships. Then extensions of the nonlinearity measures are discussed for hybrid systems and those described by coupled differential, integral, and algebraic equations, often referred to as descriptor/singular systems.
One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of taking the future system evolution into account. However, often external disturbances or refere...
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ISBN:
(数字)9783907144022
ISBN:
(纸本)9781728188133
One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of taking the future system evolution into account. However, often external disturbances or references are not a priori known, which renders the predictive controllers shortsighted or uninformed. Adaptive and learning based prediction models can provide suitable predictions to the controller and therefor can be applied to overcome this issue. We propose to learn references for model predictive controllers via Gaussian processes. To illustrate the approach, we consider robot assisted surgery, where a robotic manipulator must follow a learned reference position based on optical tracking measurements.
This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents, where the nonconvex local loss and convex local constraint functions can vary arbitrari...
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This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents, where the nonconvex local loss and convex local constraint functions can vary arbitrarily across iterations, and the information of them is privately revealed to each agent at each iteration. For a uniformly jointly strongly connected time-varying directed graph, we propose two distributed bandit online primal–dual algorithm with compressed communication to efficiently utilize communication resources in the one-point and two-point bandit feedback settings, respectively. In nonconvex optimization, finding a globally optimal decision is often NP-hard. As a result, the standard regret metric used in online convex optimization becomes inapplicable. To measure the performance of the proposed algorithms, we use a network regret metric grounded in the first-order optimality condition associated with the variational inequality. We show that the compressed algorithm with one-point bandit feedback establishes an O(Tθ1) network regret bound and an O(T7/4−θ1) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (3/4,5/6] is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T5/2−2θ1). In addition, we show that the compressed algorithm with two-point bandit feedback establishes an O(Tmax{1−θ1,θ1}) network regret and an O(T1−θ1/2) network cumulative constraint violation bounds, where θ1 ∈ (0,1). Moreover, the network cumulative constraint violation bound is reduced to O(T1−θ1) under Slater’s condition. The bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with inequality constraints. To the best of our knowledge, thi
In this paper, a robust adaptive back-stepping attitude controller is investigated for hypersonic reentry vehicle under unknown parameters and external disturbances. Firstly, the affine nonlinear system of HRV is esta...
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This paper develops a novel off-policy game Q-learning algorithm to solve the anti-interference control problem for discrete-time linear multi-player systems using only data without requiring system matrices to be kno...
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This paper develops a novel off-policy game Q-learning algorithm to solve the anti-interference control problem for discrete-time linear multi-player systems using only data without requiring system matrices to be known. The primary contribution of this paper lies in that the Q-learning strategy employed in the proposed algorithm is implemented in an off-policy policy iteration approach other than on-policy learning due to the well-known advantages of off-policy Q-learning over on-policy Q-learning. All of the players work hard together for the goal of minimizing their common performance index meanwhile defeating the disturbance that tries to maximize the specific performance index, and finally they reach the Nash equilibrium of the game resulting in satisfying disturbance attenuation condition. In order to find the solution to the Nash equilibrium, the anti-interference control problem is first transformed into an optimal control problem. Then an off-policy Q-learning algorithm is proposed in the framework of typical adaptive dynamic programming (ADP) and game architecture, such that control policies of all players can be learned using only measured data. Comparative simulation results are provided to verify the effectiveness of the proposed method.
The intelligence nonlinear control scheme via double power reaching law based sliding mode control method is proposed to solve the problems of model uncertainties and unknown outside ***,the aerodynamic parameters of ...
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The intelligence nonlinear control scheme via double power reaching law based sliding mode control method is proposed to solve the problems of model uncertainties and unknown outside ***,the aerodynamic parameters of the morphing vehicle are replaced with curve-fitted approximation to build the accurate model for control design in the hypersonic *** the nonlinear vehicle model is transformed into the strict feedback multi-input/multi-output nonlinear system by using the input-output feedback linearization *** the same time,the disturbance observer is used to approximate the unknown disturbance,and the sliding mode method is used to solve the problem of non-matching and ***,according to the buffeting problem in sliding mode control,the double power is *** results show that the proposed method can ensure the global stability of the closed-loop system,and has good tracking and robust performance.
This paper studies the distributed average tracking problem pertaining to a discrete-time linear time-invariant multi-agent network, which is subject to, concurrently, input delays, random packet-drops, and reference ...
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This paper deals with the control of simple process models with state-delays. A pre-compensator is used to cancel the delay. Due to the cancellation of some terms, the process as well as its undelayed part should be s...
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A Discrete Event Systems (DESs) model is developed for the synchronization and time modulation of flow systems. For the case of a four-way intersection, the model is developed into the form of two subsystems. The firs...
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The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is considered. This problem is an important component o...
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