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Model Predictive planning and control algorithms based on A*-type graph search techniques achieve computationally fast and nearly optimal solutions when they use a cost-to-goal (or “heuristic”) function, i.e. an estimate of the cost from the current state to the goal state, that correlates well with the actual optimal cost-to-goal values. Compared to search methods without a cost-to-goal estimate,...
This paper presents centralized, decentralized and distributed nonlinear model predictive controllers design for a tractor-trailer system. Several comparisons are made in terms of their performances and computation time. The experimental results show that the centralized nonlinear model predictive controller has ability to let a tractor-trailer system follow trajectories with the lowest tracking error,...
In this paper, a quadcopter is controlled by a nonlinear model predictive controller, NMPC, for trajectory tracking in presence of an external perturbation. The nonlinear model predictive control was basically confined to slow processes. Applications to fast processes such as robots are rare because the time for the solution may exceed the sampling period. Metaheuristics have been used for solving...
The paper presents the recursive robust output variable prediction algorithm, applicable for systems described in the form of nonlinear algebraic-differential equations. The algorithm bases on the uncertainty interval description, the system model, and the measurements. To improve the algorithm efficiency, nonlinear system models are linearised along the nominal trajectory. The effectiveness of the...
This paper presents the design of predictive control with state constraints for the swing-up and stabilizing control of a cart with an inverted pendulum system where the state constraints are proposed on angle and velocity of pendulum, position and velocity of cart, Since the system has strong nonlinearity and inherent instability, a step of linearization is necessary to extract linear state space...
This paper proposes an approach to extend the mixed logical dynamical modelling framework for synthesizing robust optimal control actions for constrained piecewise affine systems subject to bounded additive input disturbances. Rather than using closed-loop dynamic programming arguments, robustness is achieved here with an open-loop optimization strategy, such that the optimal control sequence optimizes...
This paper describes a nonlinear Model Predictive Control (MPC) algorithm with on-line optimal linearisation. Unlike the classical MPC algorithms with successive model linearisation at the current operating point of the process (using the Taylor's series expansion), the best possible linear approximation of the nonlinear predicted output trajectory is repeatedly found in the discussed approach. The...
This work proposes a practical approach to control the particle size distribution in seeded semi-batch emulsion polymerization. In this approach, initially an offline optimization is performed and the resulting monomer feed trajectory is implemented at the plant up to the predefined mid-course point of the batch. Frequent measurements are used to estimate the lumped states of the system using a state...
Many biaxial contouring systems involve competing control objectives of maximizing accuracy while minimizing traversal time. A model predictive controller for contouring systems is proposed where the control inputs are determined by minimizing a cost function which reflects the tradeoff between these competing objectives, subject to state and actuator constraints. The path speed is automatically adjusted...
This paper deals with state feedback control of chained systems based on a Nonlinear Model Predictive Control (NMPC) strategy. Chained systems can model many common nonholonomic vehicles. We establish a relation between the degree of nonholonomy and the minimum length of the control horizon so as to make the NMPC feasible. A necessary condition on the control horizon of NMPC is given and theoretically...
Open water systems are one of the most externally influenced systems due to their size and continuous exposure to uncertain meteorological forces. In this paper we use a stochastic programming approach to control a drainage system in which the weather forecast is modeled as a disturbance tree. A model predictive controller is used to optimize the expected value of the system variables taking into...
Currently, pseudo-omnidirectional, wheeled mobile robots with independently steered and driven wheels seem to provide a solid compromise between complexity, flexibility and robustness. Yet, such undercarriages are imposed to the risk of actuator fighting and suffer from singular regions within their configuration space. To address these problems we expand a previously developed potential field (PF)...
Previous efforts to control cellular differentiation have been largely experimental. Although some mathematical models for this process exist, rarely has a quantitative approach been employed to design experiments that predictably direct the cell fate. As an initial step towards this aim, a control strategy for sustaining a desired constant level of differentiated human promyelocytic leukemia (HL60)...
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