scholarly journals Model Based Robust Predictive Control of Ship Roll/Yaw Motions with Input Constraints

2020 ◽  
Vol 10 (10) ◽  
pp. 3377
Author(s):  
Zhongjia Jin ◽  
Sheng Liu ◽  
Lincheng Jin ◽  
Wei Chen ◽  
Weilin Yang
Keyword(s):  
Predictive Control ◽  
State Feedback ◽  
State Feedback Control ◽  
Linear Matrix ◽  
Control Input ◽  
Input Constraints ◽  
Control Performance ◽  
Parameter Uncertainties ◽  
Yaw Control ◽  
Control Gain

A robust H∞-type state feedback model predictive control (H∞-SFMPC) with input constraints is proposed to optimize the control performance during the ship sailing. Specifically, the approach employed in this paper is able to optimize the closed-loop performance with respect to an H∞-type cost function which predicts the system performance based on the actual model instead of the ideal model. As a result, the effect caused by disturbances is attenuated. The state feedback control gain for the control input of the rudder-fin joint roll/yaw control system is obtained by solving a constrained convex optimization problem in terms of linear matrix inequalities. Simulations are carried out, which reveal that the proposed approach has outstanding control performance. Furthermore, it is found that the approach also has significant robustness with respect to parameter uncertainties.

Human Postural Feedback Response Described by Eigenvector

2006 ◽  
Vol 326-328 ◽  
pp. 739-742 ◽  
Author(s):  
Se Young Kim ◽  
Suk Yung Park
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
State Feedback Control ◽  
Joint Torque ◽  
Modal Control ◽  
Control Input ◽  
Postural Responses ◽  
Control Gain ◽  
Full State ◽  
Full State Feedback

Human postural responses appeared to have stereotyped modality, such as ankle mode, knee mode, and hip mode in response to various levels of postural challenges. We examined whether human postural control gain of full-state feedback could be decoupled along with the eigenvectors. To verify the model, postural responses subjected to fast backward perturbation were used. Upright posture was modeled as 3-segment inverted pendulum incorporated with linear feedback control, and joint torques were calculated using inverse dynamics. Postural modalities, such as ankle, knee and hip mode, were obtained from eigenvectors of biomechanics model. As oppose to the full-state feedback control, independent modal control assumes that modal control input is determined by the linear combinations of corresponding modality. We used linear regression to obtain and compare the feedback gains for both eigenvector control gain and full-state feedback. As a result, we found that both feedback gains of two control models that fit the joint torque data are reasonably closed each other especially at the joint angle feedback gains. This implies that the simple parameterization using eigenvectors may be used to correlate the feedback gains of full-state feedback control.

Robust Guaranteed Cost Control of Uncertain Fuzzy Systems Under Sampled-Data Inputs

2009 ◽  
Vol 13 (2) ◽  
pp. 150-154
Author(s):  
Jun Yoneyama ◽  
Keyword(s):  
Fuzzy Systems ◽  
State Feedback ◽  
State Feedback Control ◽  
Delay Systems ◽  
Linear Matrix ◽  
Control Input ◽  
Sampled Data ◽  
Data Control ◽  
Guaranteed Cost ◽  
Sampled Data Control

A dynamical system is usually modeled as a continuous-time system, while the control input is applied at discrete instants. This is called a sampled-data control system. This paper is concerned with robust sampled-data control with guaranteed cost for uncertain fuzzy systems. The sampled-data control input is usually the zero-order hold and hence has a piecewise-continuous delay. Thus, an input delay system approach to robust sampled-data control is introduced. Sufficient robust guaranteed cost performance conditions for the closed-loop system with a sampled-data state feedback controller are given in terms of linear matrix inequalities(LMIs). Such robust conditions are derived via descriptor approach to fuzzy time-delay systems under the assumption that a sampling interval may vary but is not greater than some prescribed number. A design method of robust sampled-data guaranteed cost controller for uncertain fuzzy systems. Numerical examples are given to illustrate our sampled-data state feedback control.

scholarly journals LMI-Based Model Predictive Control for Underactuated Surface Vessels with Input Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Lutao Liu ◽  
Zhilin Liu ◽  
Jun Zhang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Matrix ◽  
Control Input ◽  
Input Constraints ◽  
State Dependent ◽  
Operating Region ◽  
Surface Vessels ◽  
Underactuated Surface Vessels ◽  
The Given

A nonlinear model predictive control (MPC) is proposed for underactuated surface vessel (USV) with constrained inputs. Aimed at the special structure of USV, a state-dependent coefficient (SDC) under the given USV is constructed in terms of diffeomorphism and state-dependent Riccati equation (SDRE) theory. Based on linear matrix inequalities (LMIs), the states of the USV are steered into an operating region around zero. When the states reach the region, the control law is switched to stabilize the system. And the constrained control input of the considered system is solved by convex optimization based on MPC involving LMIs. The simulation results verified the effectiveness of the proposed method. It is shown that, based on LMIs, it is easy to get the MPC for the USV with input constraints.

scholarly journals A Robust Fault-Tolerant Predictive Control for Discrete-Time Linear Systems Subject to Sensor and Actuator Faults

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2307
Author(s):  
Sofiane Bououden ◽  
Ilyes Boulkaibet ◽  
Mohammed Chadli ◽  
Abdelaziz Abboudi
Keyword(s):  
Model Predictive Control ◽  
Linear Systems ◽  
Predictive Control ◽  
Robust Stability ◽  
Discrete Time ◽  
Fault Tolerant ◽  
Linear Matrix ◽  
Input Constraints ◽  
Actuator Faults ◽  
Time Linear

In this paper, a robust fault-tolerant model predictive control (RFTPC) approach is proposed for discrete-time linear systems subject to sensor and actuator faults, disturbances, and input constraints. In this approach, a virtual observer is first considered to improve the observation accuracy as well as reduce fault effects on the system. Then, a real observer is established based on the proposed virtual observer, since the performance of virtual observers is limited due to the presence of unmeasurable information in the system. Based on the estimated information obtained by the observers, a robust fault-tolerant model predictive control is synthesized and used to control discrete-time systems subject to sensor and actuator faults, disturbances, and input constraints. Additionally, an optimized cost function is employed in the RFTPC design to guarantee robust stability as well as the rejection of bounded disturbances for the discrete-time system with sensor and actuator faults. Furthermore, a linear matrix inequality (LMI) approach is used to propose sufficient stability conditions that ensure and guarantee the robust stability of the whole closed-loop system composed of the states and the estimation error of the system dynamics. As a result, the entire control problem is formulated as an LMI problem, and the gains of both observer and robust fault-tolerant model predictive controller are obtained by solving the linear matrix inequalities (LMIs). Finally, the efficiency of the proposed RFTPC controller is tested by simulating a numerical example where the simulation results demonstrate the applicability of the proposed method in dealing with linear systems subject to faults in both actuators and sensors.

State Feedback Robust H∞ Control for Generalized Discrete System and Simulation

2013 ◽  
Vol 467 ◽  
pp. 621-626
Author(s):  
Chen Fang ◽  
Jiang Hong Shi ◽  
Kun Yu Li ◽  
Zheng Wang
Keyword(s):  
Discrete System ◽  
State Feedback ◽  
Closed Loop ◽  
The State ◽  
Linear Matrix ◽  
Sufficient Condition ◽  
Matrix Inequalities ◽  
Parameter Uncertainties ◽  
Robust Controller ◽  
Closed Loop Systems

For a class of uncertain generalized discrete linear system with norm-bounded parameter uncertainties, the state feedback robust control problem is studied. One sufficient condition for the solvability of the problem and the state feedback robust controller are obtained in terms of linear matrix inequalities. The designed controller guarantees that the closed-loop systems is regular, causal, stable and satisfies a prescribed norm bounded constraint for all admissible uncertain parameters under some conditions. The result of the normal discrete system can be regarded as a particular form of our conclusion. A simulation example is given to demonstrate the effectiveness of the proposed method.

Robust H2/H∞Control Strategy for Linear Markovian Jump Systems

2014 ◽  
Vol 525 ◽  
pp. 646-652
Author(s):  
Min Bian ◽  
Qing Yun Guo
Keyword(s):  
Control Strategy ◽  
State Feedback ◽  
Control Strategies ◽  
State Feedback Control ◽  
Linear Matrix ◽  
Bounded Real Lemma ◽  
Finite State ◽  
Linear State ◽  
Feasible Solutions ◽  
Jump Systems

The robust H2/<em>H</em>∞ control strategy for a class of linear continuous-time uncertain systems with randomly jumping parameters is investigated. The transition of the jumping parameters is decided by a finite-state Markov process. The uncertainties are supposed to be norm-bounded. It is desired to design a linear state feedback control strategies such that the closed-loop system satisfies H performance and minimizes the H2 norm of the system. A sufficient condition is first established on the existence of the robust H2/<em>H</em>∞controller bases on the bounded real lemma. Then the corresponding state-feedback law is given in terms of a set of linear matrix inequalities (LMIs). It is showed that this condition is equivalent to the feasible solutions problem of LMI. Furthermore, the control strategy design problem is converted into a convex optimization problem subject to LMI constraints, which can be easily solved by standard numerical software.

scholarly journals Robust H ∞ state-feedback control for linear systems

2017 ◽  
Vol 473 (2200) ◽  
pp. 20160934 ◽  
Author(s):  
Hao Chen ◽  
Zhenzhen Zhang ◽  
Huazhang Wang
Keyword(s):  
Feedback Control ◽  
Linear Systems ◽  
Control Algorithm ◽  
State Feedback ◽  
Convex Combination ◽  
State Feedback Control ◽  
Parameter Uncertainties ◽  
Globally Asymptotically Stable ◽  
Loop Control ◽  
Control Approach

This paper investigates the problem of robust H ∞ control for linear systems. First, the state-feedback closed-loop control algorithm is designed. Second, by employing the geometric progression theory, a modified augmented Lyapunov–Krasovskii functional (LKF) with the geometric integral interval is established. Then, parameter uncertainties and the derivative of the delay are flexibly described by introducing the convex combination skill. This technique can eliminate the unnecessary enlargement of the LKF derivative estimation, which gives less conservatism. In addition, the designed controller can ensure that the linear systems are globally asymptotically stable with a guaranteed H ∞ performance in the presence of a disturbance input and parameter uncertainties. A liquid monopropellant rocket motor with a pressure feeding system is evaluated in a simulation example. It shows that this proposed state-feedback control approach achieves the expected results for linear systems in the sense of the prescribed H ∞ performance.

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