查看更多>>摘要：This paper focuses on optimizing an unknown cost function through extremum seeking(ES)control in the presence of a slow nonlinear dynamic sensor responsible for measuring the cost.In contrast to traditional perturbation-based ES control,which often suffers from sluggish convergence,the proposed method eliminates the time-scale separation between sensor dynamics and ES control by using the relative degree of the nonlinear sensor system.To improve the convergence rate,the authors incorporate high-frequency dither signals and a differentiator.To enhance the robustness with the existence of rapid disturbances,an off-the-shelf linear high-gain differentiator is applied.The first result demonstrates that,for any desired convergence rate,with properly tuned parameters for the proposed ES algorithm,the input of the cost function can converge to an arbitrarily small neighborhood of the optimal solution,starting from any initial condition within any given compact set.Furthermore,the second result shows the robustness of the proposed ES control in the presence of sufficiently fast,zero-mean periodic disturbances.Simulation results substantiate these theoretical findings.
查看更多>>摘要：This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR problem.In particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value function.This method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing methods.Three numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.
查看更多>>摘要：In this paper,a distributed cooperative control protocol is presented to deal with actua-tor failures of multi-agent systems in the presence of connectivity preservation.With the developed strategy,each agent can track the reference trajectory of the leader in the presence of actuator failures,disturbances and uncertainties.The connectivity of the multi-agent system can always be ensured during the control process.To achieve the aforementioned control objectives,a potential function is introduced to the distributed adaptive fault-tolerant control algorithm to preserve the initial connected network among the agents.The uncertainty of the multi-agent system,which is allowed to be de-scribed by discontinuous functions,is approximated and compensated using the fuzzy logic system.The asymptotic stability of the closed-loop system is demonstrated through the use of Cellina's ap-proximate selection theorem of nonsmooth analysis.Due to the developed adaptive laws,the upper bound of the disturbance is allowed to be uncertain,which facilitates the implementation of the control scheme.Finally,simulation results are provided to verify the effectiveness of the proposed control scheme.
查看更多>>摘要：This paper studies the distributed Nash equilibrium seeking(DNES)problem for games whose action sets are compact and whose network graph is switching satisfying the jointly strongly connected condition.To keep the actions of all players in their action sets all the time,one has to resort to the projected gradient-based method.Under the assumption that the unique Nash equilibrium is the unique equilibrium of the pseudogradient system,an algorithm is proposed that is able to exponentially find the Nash equilibrium.Further,the authors also consider the distributed Nash equilibrium seeking problem for games whose actions are governed by high-order integrator dynamics and belong to some compact sets.Two examples are used to illustrate the proposed approach.
查看更多>>摘要：It is a significant research direction for highly complex musculoskeletal robots that how to develop the ability of motion learning and generalization.The cooperations of multiple brain regions are crucial to improving motion performance.Inspired by the neural mechanisms of structures,functions,and interconnections of basal ganglia and cerebellum,a biologically inspired integration model for motor learning of musculoskeletal robots is proposed.Based on the neural characteristics of the basal ganglia,the basal ganglia actor network,which mainly simulates the dorsal striatum,outputs motion commands,and the basal ganglia critic network,which simulates the ventral striatum,estimates action-state values.Their network parameters are updated using the soft actor-critic method.Based on the sensorimotor prediction mechanism of the cerebellum,the cerebellum network evaluates the state feature extraction quality of the basal ganglia actor network and then updates the weights of its feature layer.This learning method is proven to converge to the optimal policy.Furthermore,drawing on the mechanism of dopaminergic dynamic regulation in the basal ganglia,the adaptive adjustment of target entropy and the dopaminergic experience replay are proposed to further improve the integration model,which contributes to the exploration-exploitation trade-off of motor learning.The bio-inspired integration model is validated on a musculoskeletal system.Experimental results indicate that this model can effectively control the musculoskeletal robot to accomplish the motion task from random starting locations to random target positions with high precision and robustness.
查看更多>>摘要：This paper presents a learning-based control policy design for point-to-point vehicle po-sitioning in the urban environment via BeiDou navigation.While navigating in urban canyons,the multipath effect is a kind of interference that causes the navigation signal to drift and thus imposes severe impacts on vehicle localization due to the reflection and diffraction of the BeiDou signal.Here,the authors formulated the navigation control system with unknown vehicle dynamics into an optimal control-seeking problem through a linear discrete-time system,and the point-to-point localization con-trol is modeled and handled by leveraging off-policy reinforcement learning for feedback control.The proposed learning-based design guarantees optimality with prescribed performance and also stabilizes the closed-loop navigation system,without the full knowledge of the vehicle dynamics.It is seen that the proposed method can withstand the impact of the multipath effect while satisfying the prescribed convergence rate.A case study demonstrates that the proposed algorithms effectively drive the vehicle to a desired setpoint under the multipath effect introduced by actual experiments of BeiDou navigation in the urban environment.
查看更多>>摘要：In this paper,the authors study the cooperative target-fencing problem for n-dimensional systems and a target with a general trajectory.Without using the velocity of the vehicles,a position feedback control law is proposed to fence the general target into the convex hull formed by the vehicles.Specifically,the dynamics of each vehicle is described by a double-integrator system.Two potential functions are designed to guarantee connectivity preservation of the communication network and col-lision avoidance among the vehicles.The proposed approach can deal with a target whose trajectory is any twice continuously differentiable function of time.The effectiveness of the result is verified by a numerical example.
查看更多>>摘要：Quantized feedback control is fundamental to system synthesis with limited communication capacity.In sharp contrast to the existing literature on quantized control which requires an explicit dynamical model,the authors study the quadratic stabilization and performance control problems with logarithmically quantized feedback in a direct data-driven framework,where the system state matrix is not exactly known and instead,belongs to an ambiguity set that is directly constructed from a finite number of noisy system data.To this end,the authors firstly establish sufficient and necessary conditions via linear matrix inequalities for the existence of a common quantized controller that achieves our control objectives over the ambiguity set.Then,the authors provide necessary conditions on the data for the solvability of the LMIs,and determine the coarsest quantization density via semi-definite programming.The theoretical results are validated through numerical examples.
查看更多>>摘要：The semi-tensor product(STP)of matrices is generalized to multidimensional arrays,called the compound product of hypermatrices.The product is first defined for three-dimensional hypermatri-ces with compatible orders and then extended to general cases.Three different types of hyperdetermi-nants are introduced and certain properties are revealed.The Lie groups and Lie algebras corresponding to the hypermatrix products are constructed.Finally,these results are applied to dynamical systems.
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