Hybrid Control of a Double Linear Inverted Pendulum using LQR-Fuzzy and LQR-PID Controllers

Habib, Maki K.; Ayankoso, Samuel A.

doi:10.1109/icma54519.2022.9856235

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…Genetic algorithm (GA) [15], particle swarm optimisation algorithm (PSO) [16], gravitational search algorithm [17], grey wolf optimisation algorithm (GWO) [18], whale optimisation algorithm (WOA) [19], multi-verse optimisation algorithm (MVO) [20], and multi-objective grey wolf optimisation algorithm (MOGWO) [21] are highly preferred to find the optimum design parameters of any nonconvex problems. A PSO-based approach is proposed to optimise the weighting matrices of the optimal full-state controller (LQR) [22], [23]. A single-objective hybrid algorithm-based active disturbance rejection controller tuning method is proposed to improve the transient response of the control system [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator

Keskin

Aliskan

2023

ELEKTRON ELEKTROTECH

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In case an analytical approach to the selection of any weighting function is not possible, the selection process is usually a random and time-consuming process. In robust H∞ control theory, the selection of scalar, time, or frequency-dependent weighting functions is the main issue to shape the amplitude-frequency characteristic curve of the feedback controller. Therefore, we propose a robust H∞ control approach which utilises the multi-objective grey wolf optimisation algorithm (MOGWO) to obtain the optimal performance weighting functions in the presence of right half-plane zeros and limited bandwidth constraints. A trade-off design flowchart is proposed, providing Pareto optimal solutions to choose the optimal configuration of the robust feedback controller. The control method is structured by combining the robust H∞ optimal technique and the multi-objective algorithm. The effectiveness of the approach is compared with the non-convex single-objective heuristic solutions like the multi-verse optimisation algorithm (MVO), whale optimisation algorithm (WOA), and grey wolf optimisation algorithm (GWO). The focus of this design is to track and stabilise the output voltage of the DC-DC converter in the presence of external disturbances and parameter uncertainties. The optimised controllers are implemented using a digital signal processor (DSP) on a 200 W interleaved boost converter. The simulation results and experimental findings show that the proposed control method provides supreme disturbance rejection along with maintaining the stability of the system.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator

Keskin

Aliskan

2023

ELEKTRON ELEKTROTECH

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“…In addition, some researchers combine two or three control strategies for validation on RIP system. For instance, linear quadratic regulator associates with neural network for improving the responses [22], Lyapunov-base controller and linear PD controller [23], LQR-FLC and LQR-PID [24]- [26], backstepping sliding mode controller [27], fuzzy-sliding mode controller [28]. Moreover, swing-up and trajectory tracking are problems that many researcher focus on studying and using RIP for validation their proposed control schemes.…”

Section: Introductionmentioning

confidence: 99%

Combining Passivity-Based Control and Linear Quadratic Regulator to Control a Rotary Inverted Pendulum

Vo,

Nguyen,

Duong

et al. 2023

Journal of Robotics and Control

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In this manuscript, new combination methodology is proposed, which named combining Passivity-Based Control and Linear Quadratic Regulator (for short, CPBC-LQR), to support the stabilization process as the system is far from equilibrium point. More precisely, Linear Quadratic Regulator (for short, LQR) is used together with Passivity-Based Control (for short, PBC) controller. Though passivity-based control and linear quadratic regulator are two control methods, it is possible to integrate them together. The combination of passivity-based control and linear quadratic regulator is analyzed, designed and implemented on so-called rotary inverted pendulum system (for short, RIP). In this work, CPBC-LQR is validated and discussed on both MATLAB/Simulink environment and real-time experimental setup. The numerical simulation and experimental results reveal the ability of CPBC-LQR control scheme in stabilization problem and achieve a good and stable performance. Effectiveness and feasibility of proposed controller are confirmed via comparative simulation and experiments.

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LQR controller performance via particle swarm optimization and neural networks

Chacko,

Kumar,

Abraham

2024

Optim Control Appl Methods

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The inverted pendulum‐cart (IPC) control problem has long been a benchmark in the field of control systems due to its inherent instability and nonlinear dynamics. The linear quadratic regulator (LQR) control technique has been proven to be effective in stabilizing the inverted pendulum; however, the challenge lies in finding the optimal control gains that provide the best performance. This article presents a method to address the LQR control design problem for the IPC system which is compared against a particle swarm optimization based LQR and a neural network optimized LQR. The method provides a deterministic approach to finding the weighing matrices Q and R in accordance with the time domain characteristics chosen by the designer, such as settling time and maximum peak overshoot. Results from MATLAB simulations indicate that the suggested strategy has good performance.

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Hybrid Control of a Double Linear Inverted Pendulum using LQR-Fuzzy and LQR-PID Controllers

Cited by 4 publications

References 20 publications

Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator

Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator

Combining Passivity-Based Control and Linear Quadratic Regulator to Control a Rotary Inverted Pendulum

LQR controller performance via particle swarm optimization and neural networks

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