臺灣博碩士論文加值系統

This work proposes an optimization scheme for smooth, saturated integral sliding mode control in various types of network communication, including 5G-SA, 5G-NSA, WiFi-6, and experimental WiFi. First, the ball and plate model system is analyzed. Then, an integral sliding mode control with a smooth saturation function is designed, and the stability of the controller is analyzed. To optimize the parameter, the Bald Eagle Search (BES) algorithm is implemented by applying the Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE) objective functions to constant and stochastic time delays from network communications. The BES algorithm is compared with the Genetic Algorithm (GA) and Particle Swarm Optimizer (PSO) algorithms. The comparison results reveal that BES algorithm outperforms the other algorithms in terms of the convergence rate and final objective function. To speed up the convergence rate and execution time, an accelerated-BES is proposed. The accelerated-BES is also compared with PSO and GA. Finally, the optimized controller is validated by simulations and experiments.

This work proposes an optimization scheme for smooth, saturated integral sliding mode control in various types of network communication, including 5G-SA, 5G-NSA, WiFi-6, and experimental WiFi. First, the ball and plate model system is analyzed. Then, an integral sliding mode control with a smooth saturation function is designed, and the stability of the controller is analyzed. To optimize the parameter, the Bald Eagle Search (BES) algorithm is implemented by applying the Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE) objective functions to constant and stochastic time delays from network communications. The BES algorithm is compared with the Genetic Algorithm (GA) and Particle Swarm Optimizer (PSO) algorithms. The comparison results reveal that BES algorithm outperforms the other algorithms in terms of the convergence rate and final objective function. To speed up the convergence rate and execution time, an accelerated-BES is proposed. The accelerated-BES is also compared with PSO and GA. Finally, the optimized controller is validated by simulations and experiments.

Master’s Thesis Recommendation Form i
Qualification Form by Master’s Degree Examination Committee ii
Abstract iii
Acknowledgement iv
List of Tables vii
List of Figures viii
Nomenclature x
CHAPTER 1 Introduction 1
1.1. Background 1
1.2. Objective 2
1.3. Organization of the Thesis 2
CHAPTER 2 Literature Review 4
2.1. Control Methods 5
2.2. Optimization Algorithm 6
CHAPTER 3 Dynamic Model of The Ball Balancing System 9
3.1. One-degree-of-freedom approach 10
3.2. Two-degree-of-freedom approach 12
CHAPTER 4 Controller Design 15
4.1. Conventional-SMC and Integral-SMC 15
4.2. Chattering Reduction 16
4.3. Lyapunov Stability 17
CHAPTER 5 Optimization Algorithm 20
5.1. Delay Definitions of Network Controlled Systems 20
5.1.1. 5G Delay Characteristics 20
5.1.2. WiFi-6 Delay Characteristics 21
5.2. Objective Function 23
5.3. Optimization Algorithm 24
5.3.1. Particle Swarm Optimizer (PSO) 24
5.3.2. Genetic Algorithm (GA) 25
5.3.3. Bald Eagle Search (BES) 26
5.3.4. Accelerated Bald Eagle Search Algorithm (accelerated-BES) 29
CHAPTER 6 Simulation and Experimental Result 31
6.1. Deterministic Delay Simulation 31
6.2. Stochastic Delay Simulation 34
6.3. Accelerated-BES Performance Analysis 42
6.4. Implementation 44
CHAPTER 7 Conclusion and Future Works 46
7.1. Conclusion 46
7.2. Future Works 47
References 48

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