{"schemaVersion":1,"modelVersion":"finite-horizon-reference-ilc-v1","generatedBy":"docker/iterative-learning-lab/generate.mjs","learning":{"variable":"reference correction in radians","law":"u[k+1] = u[k] + alpha * P^T * (r - y[k])","loss":"0.5 * sum_over_samples_and_channels(error^2)","normalization":"L = max_channels min((sum abs(h))^2, sum_i (N-i)*h[i]^2); alpha = gamma/L","gainChoices":"conservative gamma=0.35; default gamma=1.5; aggressive alpha=2.25/lambdaEstimate","spectrumNote":"L is a proven upper bound on ||P||_2^2. lambdaEstimate is a Rayleigh/power estimate, NOT a certified upper bound; it is used only for the deliberately aggressive setting.","reset":"Every trial resets every internal state; reference, disturbance, horizon and discretization remain fixed.","limits":"Educational linear models only. No input saturation, contact, torque command, sensor noise or hardware connection.","sampleConvention":"command[n] produces y[n]=C*x[n+1]; displayed time[n]=(n+1)*dt. Initial t=0 state is documented separately."},"scenarios":{"joint":{"title":"单关节闭环伺服","modelNote":"一阶离散闭环等效伺服；反馈包含在对象中，学习量是参考角修正，不是力矩。固定的输出扰动每轮完全重复。","referenceNote":"r(t)=0.65 sin(2πt/5) sin²(πt/5)，初始角为0，T=5s。","units":{"time":"s","reference":"rad","correction":"rad","output":"rad","error":"rad","tip":"m"},"channels":["关节角 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