MATLAB Implementation of Classical Guidance Laws with Code-Based Explanations

In the fields of aircraft control and missile guidance, theoretical research and engineering implementation of classical guidance laws remain core subjects. MATLAB, as a standard tool for engineering computation, is particularly suitable for simulation verification and performance analysis of guidance algorithms.

Proportional Navigation (PN) is the most fundamental and widely used guidance strategy, whose core principle involves adjusting the vehicle's acceleration command to eliminate line-of-sight angular rate. In MATLAB implementation, it requires establishing kinematic equations and designing closed-loop control systems. A typical implementation includes target-missile relative motion modeling, guidance command generation modules, and dynamic response modules. Code implementation typically involves calculating relative velocity vectors and implementing feedback control loops using MATLAB's Control System Toolbox functions.

Augmented Proportional Navigation (APN) adds acceleration compensation terms to PN, enabling more effective interception of maneuvering targets. MATLAB implementation requires additional consideration of target acceleration estimation modules, which imposes requirements on filtering algorithms. The selection of algorithm parameters directly affects guidance accuracy and energy consumption. Implementation often incorporates Kalman filters or other estimation algorithms from MATLAB's Signal Processing Toolbox.

Optimal Guidance Law (OGL) is based on modern control theory, deriving guidance commands through performance index optimization. MATLAB implementation typically involves solving Riccati equations and adjusting state variable weighting matrices. These algorithms demonstrate outstanding terminal accuracy but demand higher model precision. Implementation commonly uses MATLAB's Optimization Toolbox and Control System functions for matrix computations and differential equation solving.

In engineering practice, MATLAB implementations of these guidance laws generally include the following common modules: kinematic/dynamic modeling components, environmental disturbance simulation, sensor noise injection, core guidance algorithms, and performance evaluation metric calculations. By modifying parameter configurations, one can quickly compare the performance differences of various guidance laws under identical scenarios. Typical implementation structures involve modular programming with separate functions for each subsystem, allowing for systematic testing and validation.

Notably, MATLAB implementations of classical guidance laws serve not only as algorithm verification tools but also as teaching platforms for understanding guidance principles. By adjusting parameters and observing system response changes, one can intuitively comprehend the operational mechanisms of key parameters such as guidance gain and navigation ratio. Code implementation often includes visualization tools from MATLAB's Graphics Library to plot trajectory comparisons and performance metrics.