Temperature Control of an Ill-Conditioned Thermal Process Using Regularization

2002 ◽  
Author(s):  
Kwan-Woong Gwak ◽  
Glenn Y. Masada
Keyword(s):  
Temperature Control ◽  
Thermal Process ◽  
Sliding Mode ◽  
Nonlinear Controller ◽  
Temperature Tracking ◽  
Nonlinear Controllers ◽  
Sliding Mode Controllers ◽  
Modeling Errors ◽  
Value Decomposition ◽  
Control Designs

New regularization embedded nonlinear control designs are proposed for the temperature control of an ill-conditioned thermal process. A classic nonlinear controller applied to such a process is shown to provide good temperature tracking but generates physically unreasonable actuator solutions. Tikhonov and truncated singular value decomposition (TSVD) methods, embedded in the design of feedback linearizing controllers (FBL) and sliding mode controllers (SMC), also provide good temperature tracking and generate physically reasonable and actuator-constraint-satisfying solutions for the ill-conditioned system, in spite of the modeling errors inherent in applying regularization. These nonlinear controllers are also robust to changes in process parameters of the ill-conditioned process.

Regularization Embedded Nonlinear Control Designs for Input-Constrained and Ill-Conditioned Thermal System

2004 ◽  
Vol 126 (3) ◽  
pp. 574-582 ◽  
Author(s):  
Kwan-Woong Gwak ◽  
Glenn Y. Masada
Keyword(s):  
Singular Value Decomposition ◽  
Nonlinear Control ◽  
Singular Value ◽  
Input Constraint ◽  
Nonlinear Controller ◽  
Truncated Singular Value Decomposition ◽  
Temperature Tracking ◽  
Nonlinear Controllers ◽  
Value Decomposition ◽  
Control Designs

New regularization embedded nonlinear control designs are proposed for the temperature control of an input-constrained and ill-conditioned thermal process. A classic nonlinear controller applied to such a process is shown to provide good temperature tracking but generates physically unreasonable actuator solutions, i.e. input-constraint-violation. The reason of input-constraint-violating control solutions—ill-conditionedness—is shown by applying singular value decomposition (SVD) on the linear algebraic equivalence of the nonlinear controllers (LAENC). Based on the analogy of LAENC and regularization method for the linear algebraic equations, Tikhonov, truncated singular value decomposition (TSVD) and modified TSVD (MTSVD) methods are embedded in the design of feedback linearizing controllers (FBL) and sliding mode controllers (SMC). These regularization embedded nonlinear controllers (RENLC) provide good temperature tracking and generate physically reasonable and actuator-constraint-satisfying solutions for the ill-conditioned system, in spite of the modeling errors inherent in applying regularization. The optimal Tikhonov parameter is found using an L-curve. Quantitative comparisons of the residuals and standard deviations of the control inputs are used as criteria to select the optimal truncated singular value decomposition (TSVD) parameter.

Robust cascade control of electrical drives using discrete-time chattering-free sliding mode controllers with output saturation

2021 ◽  
Author(s):  
Milutin P. Petronijević ◽  
Čedomir Milosavljević ◽  
Boban Veselić ◽  
Branislava Peruničić-Draženović ◽  
Senad Huseinbegović
Keyword(s):  
Discrete Time ◽  
Sliding Mode ◽  
Cascade Control ◽  
Electrical Drives ◽  
Sliding Mode Controllers ◽  
Chattering Free

scholarly journals Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 831
Author(s):  
Izzat Al-Darraji ◽  
Dimitrios Piromalis ◽  
Ayad A. Kakei ◽  
Fazal Qudus Khan ◽  
Milos Stojemnovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Robot Arm ◽  
Robust Controller ◽  
Practical Applications ◽  
Control Loops ◽  
Modeling Errors ◽  
Aerial Robot

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.

Rotational and relative translational control for satellite electromagnetic formation flying in low earth orbit

2017 ◽  
Vol 89 (6) ◽  
pp. 815-825 ◽  
Author(s):  
Li Fan ◽  
Min Hu ◽  
Mingqi Yang
Keyword(s):  
Control Problem ◽  
Translational Control ◽  
Attitude Control ◽  
Formation Flying ◽  
Sliding Mode ◽  
Large Angle ◽  
Translation Control ◽  
Nonlinear Controller ◽  
Content Type ◽  
Hybrid Particle Swarm Optimization

Purpose The purpose of this paper is to develop a theoretical design for the attitude control of electromagnetic formation flying (EMFF) satellites, present a nonlinear controller for the relative translational control of EMFF satellites and propose a novel method for the allocation of electromagnetic dipoles. Design/methodology/approach The feedback attitude control law, magnetic unloading algorithm and large angle manoeuvre algorithm are presented. Then, a terminal sliding mode controller for the relative translation control is put forward and the convergence is proved. Finally, the control allocation problem of electromagnetic dipoles is formulated as an optimization issue, and a hybrid particle swarm optimization (PSO) – sequential quadratic programming (SQP) algorithm to optimize the free dipoles. Three numerical simulations are carried out and results are compared. Findings The proposed attitude controller is effective for the sun-tracking process of EMFF satellites, and the magnetic unloading algorithm is valid. The formation-keeping scenario simulation demonstrates the effectiveness of the terminal sliding model controller and electromagnetic dipole calculation method. Practical implications The proposed method can be applied to solve the attitude and relative translation control problem of EMFF satellites in low earth orbits. Originality/value The paper analyses the attitude control problem of EMFF satellites systematically and proposes an innovative way for relative translational control and electromagnetic dipole allocation.

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