We are developing a series of systems science-based clinical tools that will assist in modeling, diagnosing, and quantifying postural control deficits in human subjects. In line with this goal, we have designed and co...
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We are developing a series of systems science-based clinical tools that will assist in modeling, diagnosing, and quantifying postural control deficits in human subjects. In line with this goal, we have designed and constructed a seated balance device and associated experimental task for identification of the human seated postural control system. In this work, we present a quadratic programming (QP) technique for optimizing a time-domain experimental input signal for this device. The goal of this optimization is to maximize the information present in the experiment, and therefore its ability to produce accurate estimates of several desired seated postural control parameters. To achieve this, we formulate the problem as a nonconvex QP and attempt to locally maximize a measure (T-optimality condition) of the experiment's Fisher information matrix (FIM) under several constraints. These constraints include limits on the input amplitude, physiological output magnitude, subject control amplitude, and input signal autocorrelation. Because the autocorrelation constraint takes the form of a quadratic constraint (QC), we replace it with a conservative linear relaxation about a nominal point, which is iteratively updated during the course of optimization. We show that this iterative descent algorithm generates a convergent suboptimal solution that guarantees monotonic nonincreasing of the cost function value while satisfying all constraints during iterations. Finally, we present successful experimental results using an optimized input sequence.
In this paper convexity constraints are derived for a background model of electron energy loss spectra (EELS) that is linear in the fitting parameters. The model outperforms a power-law both on experimental and simula...
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In this paper convexity constraints are derived for a background model of electron energy loss spectra (EELS) that is linear in the fitting parameters. The model outperforms a power-law both on experimental and simulated backgrounds, especially for wide energy ranges, and thus improves elemental quantification results. Owing to the model's linearity, the constraints can be imposed through fitting by quadratic programming. This has important advantages over conventional nonlinear power-law fitting such as high speed and a guaranteed unique solution without need for initial parameters. As such, the need for user input is significantly reduced, which is essential for unsupervised treatment of large datasets. This is demonstrated on a demanding spectrum image of a semiconductor device sample with a high number of elements over a wide energy range.
In 1970, Pshenichny published a linearization method for nonlinear programming in Russian, which has been overlooked in the English literature. The method is essentially a recursive quadratic programming technique wit...
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In 1970, Pshenichny published a linearization method for nonlinear programming in Russian, which has been overlooked in the English literature. The method is essentially a recursive quadratic programming technique with an active set strategy. Pshenichny has proved global convergence of the method and convergence rate estimates. The method is presented in this paper, with convergence theorems stated. Application of the method is made to shape optimal design, kinematic optimization, and dynamic system optimization. The method is shown to be particularly attractive in utilization of mechanical design sensitivity analysis techniques and performs well on all classes of problems treated.
It is demonstrated that the linear programming problem in d variables and n constraints can be solved in O(n) time when d is fixed. This bound follows from a multidimensional search technique which is applicable for q...
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It is demonstrated that the linear programming problem in d variables and n constraints can be solved in O(n) time when d is fixed. This bound follows from a multidimensional search technique which is applicable for quadratic programming as well. There is also developed an algorithm that is polynomial in both n and d provided d is bounded by a certain slowly growing function of n.
Model predictive control (MPC) is an optimal control strategy where control input calculation is based on minimizing the predicted tracking error over a finite horizon that moves with time. This strategy has an advant...
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Model predictive control (MPC) is an optimal control strategy where control input calculation is based on minimizing the predicted tracking error over a finite horizon that moves with time. This strategy has an advantage over conventional state feedback and output feedback controllers because it predicts the response of the system, rather than simply reacting to it. Therefore, MPC can offer improved performance in the presence of input and output constraints. Many implementations of MPC on aerospace vehicles appear in literature [1]. Some of these include spacecraft and satellite attitude control [2-4], spacecraft rendezvous and docking [5], helicopters [6], and atmospheric re-entry [7, 8].
Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Bas...
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Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence;and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.
We introduce a new domain for finding precise numerical invariants of programs by abstract interpretation. This domain, which consists of sub-level sets of non-linear functions, generalizes the domain of linear templa...
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We introduce a new domain for finding precise numerical invariants of programs by abstract interpretation. This domain, which consists of sub-level sets of non-linear functions, generalizes the domain of linear templates introduced by Manna, Sankaranarayanan, and Sipma. In the case of quadratic templates, we use Shor's semi-definite relaxation to derive safe and computable abstractions of semantic functionals, and we show that the abstract fixpoint can be over-approximated by coupling policy iteration and semi-definite programming. We demonstrate the interest of our approach on a series of examples (filters, integration schemes) including a degenerate one (symplectic scheme).
The presence and exploitation of Vehicle Automation and Communication Systems (VACS) while defining optimal control strategies in motorway traffic flow control is addressed in this paper. VACS are supposed to act both...
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The presence and exploitation of Vehicle Automation and Communication Systems (VACS) while defining optimal control strategies in motorway traffic flow control is addressed in this paper. VACS are supposed to act both as sensors (providing information on traffic conditions) and as actuators, allowing the application of ramp metering, variable speed limit control, and lane changing control. A quadratic programming problem is defined on the basis of a novel first-order traffic flow model for multi-lane motorways. An example is presented in order to illustrate the effectiveness of the proposed optimisation problem.
Model predictive control (MPC) is the mainstream method in the motion control of autonomous vehicles. However, due to the complex and changeable driving environment, the perturbation of vehicle parameters will cause t...
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Model predictive control (MPC) is the mainstream method in the motion control of autonomous vehicles. However, due to the complex and changeable driving environment, the perturbation of vehicle parameters will cause the steady-state error problem, which will lead to the degradation of controller performance. In this paper, the offset-free MPC control method is proposed to solve the steady-state error problem systematically. The core idea of this method is to model the model mismatch, control input offset, and external disturbances as disturbance terms, then use filters to observe these disturbances and finally eliminate the influence of these disturbances on the steady-state error in the MPC solution stage. This paper uses the Kalman filter as an observer, which is integrated into our latest designed MPC solver. Based on state-of-the-art sparse quadratic programming (QP) solver operator splitting solver for quadratic programs (OSQP), an offset free model predictive control (OF-MPC) framework based on disturbance observation and MPC is formed. The proposed OF-MPC solver can efficiently deal with common model mismatch problems such as tire stiffness mismatch, steering angle offset, lateral slope disturbance, and so on. This framework is very efficient and completes all calculations in less than 7 ms when the horizon length is 50. The efficiency and robustness of the algorithm are verified on our newly designed robot operating system (ROS)-Unreal4-carsim real-time cosimulation platform and real vehicle experiments.
The optimal control of automatic transmission plays an important role in the shifting smoothness and fuel economy of heavy-duty mining trucks. In this paper, a dynamic model of the powertrain system is built to study ...
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The optimal control of automatic transmission plays an important role in the shifting smoothness and fuel economy of heavy-duty mining trucks. In this paper, a dynamic model of the powertrain system is built to study the clutch pressure control during the shifting process. A linear-quadratic optimal regulator is used to achieve the optimum control pressure of clutches, where shifting jerk and clutch friction loss are chosen to a form quadratic performance index function. Besides, a detailed solution of the linear-quadratic problem with the disturbance matrix in the state equations is provided. This paper also carries out a software simulation and verification of the normal condition (no load without slope) and the extreme condition (full load with maximum slope). Compared with the preset reference trajectory control, the simulation results show that the proposed optimal clutch pressure control can effectively reduce jerk and friction loss during the shifting process and has good robustness to different operating conditions.
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