Synthesis of LQR Controller Based on BAT Algorithm for Furuta Pendulum Stabilization

In this study, a controller design method based on the LQR method and BAT algorithm is presented for the Furuta pendulum stabilization system. Determine the LQR controller, it is often based on the designer's experience or using trial and error to find the Q, R matrices. The BAT search algorithm is based on the characteristics of the bat population in the wild. However, there are advantages to finding multivariate objective functions. The BAT algorithm has an improvement for the LQR controller to optimize the linear square function with fast response time, low energy consumption, overshoot, and a small number of oscillations. Swarm optimization algorithms have advantages in finding global extrema of multivariate functions. Therefore, with a large number of elements of the Q and R matrices, they can also be quickly found and these matrices still satisfy the Riccati equation. The controller with optimal parameters is verified through simulation results with different scenarios. The performance of the proposed controller is compared with a conventional LQR controller and implemented on a real system

Finite-Time State Estimation for an Inverted Pendulum under Input-Multiplicative Uncertainty

A sliding mode observer is presented, which is rigorously proven to achieve finite-time state estimation of a dual-parallel underactuated (i.e., single-input multi-output) cart inverted pendulum system in the presence of parametric uncertainty. A salient feature of the proposed sliding mode observer design is that a rigorous analysis is provided, which proves finite-time estimation of the complete system state in the presence of input-multiplicative parametric uncertainty. The performance of the proposed observer design is demonstrated through numerical case studies using both sliding mode control (SMC)- and linear quadratic regulator (LQR)-based closed-loop control systems. The main contribution presented here is the rigorous analysis of the finite-time state estimator under input-multiplicative parametric uncertainty in addition to a comparative numerical study that quantifies the performance improvement that is achieved by formally incorporating the proposed compensator for input-multiplicative parametric uncertainty in the observer. In summary, our results show performance improvements when applied to both SMC- and LQR-based control systems, with results that include a reduction in the root-mean square error of up to 39% in translational regulation control and a reduction of up to 29% in pendulum angular control

Finite-Time State Estimation for an Inverted Pendulum under Input-Multiplicative Uncertainty

A sliding mode observer is presented, which is rigorously proven to achieve finite-time state estimation of a dual-parallel underactuated (i.e., single-input multi-output) cart inverted pendulum system in the presence of parametric uncertainty. A salient feature of the proposed sliding mode observer design is that a rigorous analysis is provided, which proves finite-time estimation of the complete system state in the presence of input-multiplicative parametric uncertainty. The performance of the proposed observer design is demonstrated through numerical case studies using both sliding mode control (SMC)- and linear quadratic regulator (LQR)-based closed-loop control systems. The main contribution presented here is the rigorous analysis of the finite-time state estimator under input-multiplicative parametric uncertainty in addition to a comparative numerical study that quantifies the performance improvement that is achieved by formally incorporating the proposed compensator for input-multiplicative parametric uncertainty in the observer. In summary, our results show performance improvements when applied to both SMC- and LQR-based control systems, with results that include a reduction in the root-mean square error of up to 39% in translational regulation control and a reduction of up to 29% in pendulum angular control

Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs

This study examines the effectiveness of four different fuzzy logic controllers in self-balancing wheelchairs. The controllers under consideration are Type-1 Takagi-Sugeno-Kang (TSK) FLC, Interval Type-2 TSK FLC, Type-1 Mamdani FLC, and Interval Type-2 Mamdani FLC. A MATLAB-based simulation environment serves for the evaluation, focusing on key performance indicators like percentage overshoot, rise time, settling time, and displacement. Two testing methodologies were designed to simulate both ideal conditions and real-world hardware limitations. The simulations reveal distinct advantages for each controller type. For example, Type-1 TSK excels in minimizing overshoot but requires higher force. Interval Type-2 TSK shows the quickest settling times but needs the most force. Type-1 Mamdani has the fastest rise time with the lowest force requirement but experiences a higher percentage of overshoot. Interval Type-2 Mamdani offers balanced performance across all metrics. When a 2.7 N control input cap is imposed, Type-2 controllers prove notably more efficient in minimizing overshoot. These results offer valuable insights for future design and real-world application of self-balancing wheelchairs. Further studies are recommended for the empirical testing and refinement of these controllers, especially since the initial findings were limited to four-wheeled self-balancing robotic wheelchairs

Hybrid fuzzy-PID like optimal control to reduce energy consumption

The electric motor is one of the appliances that consume considerable energy. Therefore, the control method which can reduce energy consumption with better performance is needed. The purpose of this research is to minimize the energy consumption of the DC motor with maintaining the performance using Hybrid Fuzzy-PID. The input of the Fuzzy system is the error and power of the system. Where error is correlated with matric Q and power is correlated with matric R. Therefore, adjusting the fuzzy rule on error and power is like adjust matrices Q and R in LQR method. The proposed algorithm can reduce energy consumption. However, system response is slightly decrease shown from ISE (Integral Square Error). The energy reduction average is up to 5.58% while the average of ISE increment is up to 1.89%. The more speed variation in the system, the more energy can be saved by the proposed algorithm. While in terms of settling time, the proposed algorithm has the longest time due to higher computation time in the fuzzy system. This performance can be increased by tuning fuzzy rules. This algorithm offers a solution for a complex system which difficult to be modeled

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique

OBSERVER-BASED-CONTROLLER FOR INVERTED PENDULUM MODEL

This paper presents a state space control technique for inverted pendulum system. The system is a common classical control problem that has been widely used to test multiple control algorithms because of its nonlinear and unstable behavior. Full state feedback based on pole placement and optimal control is applied to the inverted pendulum system to achieve desired design specification which are 4 seconds settling time and 5% overshoot. The simulation and optimization of the full state feedback controller based on pole placement and optimal control techniques as well as the performance comparison between these techniques is described comprehensively. The comparison is made to choose the most suitable technique for the system that have the best trade-off between settling time and overshoot. Besides that, the observer design is analyzed to see the effect of pole location and noise present in the system

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique

State-Feedback Controller Based on Pole Placement Technique for Inverted Pendulum System

This paper presents a state space control technique for inverted pendulum system using simulation and real experiment via MATLAB/SIMULINK software. The inverted pendulum is difficult system to control in the field of control engineering. It is also one of the most important classical control system problems because of its nonlinear characteristics and unstable system. It has three main problems that always appear in control application which are nonlinear system, unstable and non-minimumbehavior phase system. This project will apply state feedback controller based on pole placement technique which is capable in stabilizing the practical based inverted pendulum at vertical position. Desired design specifications which are 4 seconds settling time and 5 % overshoot is needed to apply in full state feedback controller based on pole placement technique. First of all, the mathematical model of an inverted pendulum system is derived to obtain the state space representation of the system. Then, the design phase of the State-Feedback Controller can be conducted after linearization technique is performed to the nonlinear equation with the aid of mathematical aided software such as Mathcad. After that, the design is simulated using MATLAB/Simulink software. The controller design of the inverted pendulum system is verified using simulation and experiment test. Finally the controller design is compared with PID controller for benchmarking purpose

Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool

Computer programming and IoT are the key skills required in Industrial Revolution 4.0 (IR4.0). The industry demand is very high and therefore related students in this field should grasp adequate knowledge and skill in college or university prior to employment. However, learning technology related subject without applying it to an actual hardware can pose difficulty to relate the theoretical knowledge to problems in real application. It is proven that learning through hands-on activities is more effective and promotes deeper understanding of the subject matter (He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an integrated learning tool that combines learning of computer programming and IoT control for an industrial liquid filling system model is developed and tested. The integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the IoT application. The system set-up is pre-designed for semi-automation liquid filling process to enhance hands-on learning experience but can be easily programmed for full automation. Overall, it is a user and cost friendly learning tool that can be developed by academic staff to aid learning of IoT and computer programming in related education levels and field